Choice of cultivar as per length of growing season.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"5191",leadTitle:null,fullTitle:"Artificial Neural Networks - Models and Applications",title:"Artificial Neural Networks",subtitle:"Models and Applications",reviewType:"peer-reviewed",abstract:"The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models.\nThis book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.",isbn:"978-953-51-2705-5",printIsbn:"978-953-51-2704-8",pdfIsbn:"978-953-51-4175-4",doi:"10.5772/61493",price:139,priceEur:155,priceUsd:179,slug:"artificial-neural-networks-models-and-applications",numberOfPages:414,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"367a7a8daf581754dcd37bc588ec5cbf",bookSignature:"Joao Luis G. Rosa",publishedDate:"October 19th 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5191.jpg",numberOfDownloads:32112,numberOfWosCitations:88,numberOfCrossrefCitations:79,numberOfCrossrefCitationsByBook:11,numberOfDimensionsCitations:119,numberOfDimensionsCitationsByBook:15,hasAltmetrics:1,numberOfTotalCitations:286,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 13th 2015",dateEndSecondStepPublish:"November 3rd 2015",dateEndThirdStepPublish:"February 7th 2016",dateEndFourthStepPublish:"May 7th 2016",dateEndFifthStepPublish:"June 6th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7,8",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"151889",title:"Dr.",name:"Joao Luis Garcia",middleName:null,surname:"Rosa",slug:"joao-luis-garcia-rosa",fullName:"Joao Luis Garcia Rosa",profilePictureURL:"https://mts.intechopen.com/storage/users/151889/images/4861_n.jpg",biography:'João Luís Garcia Rosa is an associate professor at the Department of Computer Science, University of São Paulo (USP) at Sao Carlos, Brazil, where he teaches disciplines such as Neural Networks, Brain-Computer Interfaces, and Artificial Intelligence. He received a degree in Electrical Engineering – Electronics and Automation, a Master\'s degree in Computer Engineering, and a PhD in Computational Linguistics, all from the University of Campinas (Unicamp), Brazil. He is currently with the Bio-inspired Computing Laboratory (BioCom). Dr. Rosa has experience in computer science, with emphasis on artificial Intelligence, brain-computer interfaces, and biologically plausible artificial neural networks. He presented three 2-hour tutorials in IJCNN 2005 ("Biologically Plausible Artificial Neural Networks"), in IJCNN 2015 ("Noninvasive Electroencephalogram-based Brain-Computer Interfaces”), and in IJCNN 2017 ("Event-Related Potentials: Cognition in Brain-Computer Interfaces"). He has published three books and several papers in journals and peer-reviewed proceedings.',institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"605",title:"Machine Learning",slug:"numerical-analysis-and-scientific-computing-machine-learning"}],chapters:[{id:"51046",title:"Zhang Neural Networks for Online Solution of Time-Varying Linear Inequalities",doi:"10.5772/62732",slug:"zhang-neural-networks-for-online-solution-of-time-varying-linear-inequalities",totalDownloads:2216,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"In this chapter, a special type of recurrent neural networks termed “Zhang neural network” (ZNN) is presented and studied for online solution of time-varying linear (matrix-vector and matrix) inequalities. Specifically, focusing on solving the time-varying linear matrix-vector inequality (LMVI), we develop and investigate two different ZNN models based on two different Zhang functions (ZFs). Then, being an extension, by defining another two different ZFs, another two ZNN models are developed and investigated to solve the time-varying linear matrix inequality (LMI). For such ZNN models, theoretical results and analyses are presented as well to show their computational performances. Simulation results with two illustrative examples further substantiate the efficacy of the presented ZNN models for time-varying LMVI and LMI solving.",signatures:"Dongsheng Guo, Laicheng Yan and Yunong Zhang",downloadPdfUrl:"/chapter/pdf-download/51046",previewPdfUrl:"/chapter/pdf-preview/51046",authors:[{id:"179539",title:"Dr.",name:"Dongsheng",surname:"Guo",slug:"dongsheng-guo",fullName:"Dongsheng Guo"},{id:"184344",title:"Mr.",name:"Laicheng",surname:"Yan",slug:"laicheng-yan",fullName:"Laicheng Yan"},{id:"184345",title:"Dr.",name:"Yunong",surname:"Zhang",slug:"yunong-zhang",fullName:"Yunong Zhang"}],corrections:null},{id:"50570",title:"Bayesian Regularized Neural Networks for Small n Big p Data",doi:"10.5772/63256",slug:"bayesian-regularized-neural-networks-for-small-n-big-p-data",totalDownloads:3107,totalCrossrefCites:33,totalDimensionsCites:50,hasAltmetrics:0,abstract:"Artificial neural networks (ANN) mimic the function of the human brain and they have the capability to implement massively parallel computations for mapping, function approximation, classification, and pattern recognition processing. ANN can capture the highly nonlinear associations between inputs (predictors) and target (responses) variables and can adaptively learn the complex functional forms. Like other parametric and nonparametric methods, such as kernel regression and smoothing splines, ANNs can introduce overfitting (in particular with highly-dimensional data, such as genome wide association -GWAS-, microarray data etc.) and resulting predictions can be outside the range of the training data. Regularization (shrinkage) in ANN allows bias of parameter estimates towards what are considered to be probable. Most common techniques of regularizations techniques in ANN are the Bayesian regularization (BR) and the early stopping methods. Early stopping is effectively limiting the used weights in the network and thus imposes regularization, effectively lowering the Vapnik-Chervonenkis dimension. In Bayesian regularized ANN (BRANN), the regularization techniques involve imposing certain prior distributions on the model parameters and penalizes large weights in anticipation of achieving smoother mapping.",signatures:"Hayrettin Okut",downloadPdfUrl:"/chapter/pdf-download/50570",previewPdfUrl:"/chapter/pdf-preview/50570",authors:[{id:"179217",title:"Prof.",name:"Hayrettin",surname:"Okut",slug:"hayrettin-okut",fullName:"Hayrettin Okut"}],corrections:null},{id:"51466",title:"Generalized Regression Neural Networks with Application in Neutron Spectrometry",doi:"10.5772/64047",slug:"generalized-regression-neural-networks-with-application-in-neutron-spectrometry",totalDownloads:2451,totalCrossrefCites:3,totalDimensionsCites:8,hasAltmetrics:0,abstract:"The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy.",signatures:"Ma. del Rosario Martinez-Blanco, Víctor Hugo Castañeda-Miranda,\nGerardo Ornelas-Vargas, Héctor Alonso Guerrero-Osuna, Luis\nOctavio Solis-Sanchez, Rodrigo Castañeda-Miranda, José María\nCelaya-Padilla, Carlos Eric Galvan-Tejada, Jorge Isaac Galvan-Tejada,\nHéctor René Vega-Carrillo, Margarita Martínez-Fierro, Idalia Garza-\nVeloz and Jose Manuel Ortiz-Rodriguez",downloadPdfUrl:"/chapter/pdf-download/51466",previewPdfUrl:"/chapter/pdf-preview/51466",authors:[{id:"19773",title:"Dr.",name:"Jose Manuel",surname:"Ortiz-Rodriguez",slug:"jose-manuel-ortiz-rodriguez",fullName:"Jose Manuel Ortiz-Rodriguez"},{id:"22531",title:"Dr.",name:"Maria Del Rosario",surname:"Martinez-Blanco",slug:"maria-del-rosario-martinez-blanco",fullName:"Maria Del Rosario Martinez-Blanco"},{id:"82380",title:"Dr.",name:"Hector Rene",surname:"Vega-Carrillo",slug:"hector-rene-vega-carrillo",fullName:"Hector Rene Vega-Carrillo"},{id:"183473",title:"Dr.",name:"Luis Octavio",surname:"Solis-Sanchez",slug:"luis-octavio-solis-sanchez",fullName:"Luis Octavio Solis-Sanchez"},{id:"189184",title:"MSc.",name:"Victor Hugo",surname:"Castañeda-Miranda",slug:"victor-hugo-castaneda-miranda",fullName:"Victor Hugo Castañeda-Miranda"},{id:"189185",title:"MSc.",name:"Gerardo",surname:"Ornelas-Vargas",slug:"gerardo-ornelas-vargas",fullName:"Gerardo Ornelas-Vargas"},{id:"189186",title:"MSc.",name:"Hector Alonso",surname:"Guerrero-Osuna",slug:"hector-alonso-guerrero-osuna",fullName:"Hector Alonso Guerrero-Osuna"},{id:"189187",title:"Dr.",name:"Jose Maria",surname:"Celaya-Padilla",slug:"jose-maria-celaya-padilla",fullName:"Jose Maria Celaya-Padilla"},{id:"189189",title:"Dr.",name:"Idalia",surname:"Garza-Veloz",slug:"idalia-garza-veloz",fullName:"Idalia Garza-Veloz"},{id:"189191",title:"Dr.",name:"Rodrigo",surname:"Castañeda-Miranda",slug:"rodrigo-castaneda-miranda",fullName:"Rodrigo Castañeda-Miranda"},{id:"189792",title:"Dr.",name:"Jorge Isaac",surname:"Galvan-Tejada",slug:"jorge-isaac-galvan-tejada",fullName:"Jorge Isaac Galvan-Tejada"},{id:"189793",title:"Dr.",name:"Carlos Eric",surname:"Galvan-Tejada",slug:"carlos-eric-galvan-tejada",fullName:"Carlos Eric Galvan-Tejada"},{id:"190887",title:"MSc.",name:"Celina Lizeth",surname:"Castañeda-Miranda",slug:"celina-lizeth-castaneda-miranda",fullName:"Celina Lizeth Castañeda-Miranda"},{id:"211746",title:"Dr.",name:"Margarita de la Luz",surname:"Martinez-Fierro",slug:"margarita-de-la-luz-martinez-fierro",fullName:"Margarita de la Luz Martinez-Fierro"}],corrections:null},{id:"52340",title:"A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects",doi:"10.5772/63387",slug:"a-continuous-time-recurrent-neural-network-for-joint-equalization-and-decoding-analog-hardware-imple",totalDownloads:2170,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Equalization and channel decoding are “traditionally” two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited battery’s size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing.",signatures:"Mohamad Mostafa, Giuseppe Oliveri, Werner G. Teich and Jürgen\nLindner",downloadPdfUrl:"/chapter/pdf-download/52340",previewPdfUrl:"/chapter/pdf-preview/52340",authors:[{id:"147655",title:"Prof.",name:"Jürgen",surname:"Lindner",slug:"jurgen-lindner",fullName:"Jürgen Lindner"},{id:"179497",title:"Dr.",name:"Mohamad",surname:"Mostafa",slug:"mohamad-mostafa",fullName:"Mohamad Mostafa"},{id:"185168",title:"Mr.",name:"Giuseppe",surname:"Oliveri",slug:"giuseppe-oliveri",fullName:"Giuseppe Oliveri"},{id:"185169",title:"Dr.",name:"Werner",surname:"Teich",slug:"werner-teich",fullName:"Werner Teich"}],corrections:null},{id:"50555",title:"Direct Signal Detection Without Data‐Aided: A MIMO Functional Network Approach",doi:"10.5772/63213",slug:"direct-signal-detection-without-data-aided-a-mimo-functional-network-approach",totalDownloads:1559,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Functional network (FN) has been successfully applied in many fields, but so far no methods of direct signal detection (DSD) using FN have been published. In this chapter, a novel DSD approach using FN, which can be applied to cases with a plural source signal sequence, with short sequence, and even with the absence of a training sequence, is presented. Firstly, a multiple‐input multiple‐output FN (MIMOFN), in which the initial input vector is devised via QR decomposition of the receiving signal matrix, is constructed to solve the special issues of DSD. In the meantime, the design method for the neural function of this special MIMOFN is proposed. Then the learning rule for the parameters of neural functions is trained and updated by back‐propagation (BP) algorithm. The correctness and effectiveness of the new approach are verified by simulation results, together with some special simulation phenomena of the algorithm. The proposed method can detect the source sequence directly from the observed output data by utilizing MIMOFN without a training sequence and estimating the channel impulse response.",signatures:"Xiukai Ruan, Yanhua Tan, Yuxing Dai, Guihua Cui, Xiaojing Shi,\nQibo Cai, Chang Li, Han Li, Yaoju Zhang and Dameng Dai",downloadPdfUrl:"/chapter/pdf-download/50555",previewPdfUrl:"/chapter/pdf-preview/50555",authors:[{id:"179863",title:"Dr.",name:"Xiukai",surname:"Ruan",slug:"xiukai-ruan",fullName:"Xiukai Ruan"},{id:"180911",title:"Mrs.",name:"Yanhua",surname:"Tan",slug:"yanhua-tan",fullName:"Yanhua Tan"},{id:"185417",title:"Dr.",name:"Xiaojing",surname:"Shi",slug:"xiaojing-shi",fullName:"Xiaojing Shi"},{id:"185418",title:"Mr.",name:"Qibo",surname:"Cai",slug:"qibo-cai",fullName:"Qibo Cai"},{id:"185420",title:"Mr.",name:"Chang",surname:"Li",slug:"chang-li",fullName:"Chang Li"},{id:"185421",title:"Dr.",name:"Yuxing",surname:"Dai",slug:"yuxing-dai",fullName:"Yuxing Dai"},{id:"185422",title:"Dr.",name:"Han",surname:"Li",slug:"han-li",fullName:"Han Li"},{id:"185423",title:"Dr.",name:"Liming",surname:"Li",slug:"liming-li",fullName:"Liming Li"},{id:"185424",title:"Dr.",name:"Dameng",surname:"Dai",slug:"dameng-dai",fullName:"Dameng Dai"},{id:"186237",title:"Prof.",name:"Guihua",surname:"Cui",slug:"guihua-cui",fullName:"Guihua Cui"}],corrections:null},{id:"51277",title:"Artificial Neural Network as a FPGA Trigger for a Detection of Neutrino-Induced Air Showers",doi:"10.5772/63110",slug:"artificial-neural-network-as-a-fpga-trigger-for-a-detection-of-neutrino-induced-air-showers",totalDownloads:1894,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Neutrinos play a fundamental role in the understanding of the origin of ultrahigh-energy cosmic rays (UHECR). They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, the very low rate of events potentially generated by neutrinos is a significant challenge for detection techniques and requires both sophisticated algorithms and high-resolution hardware. We developed the FPGA trigger which is generated by a neural network. The algorithm can recognize various waveform types. It has been developed and tested on ADC traces of the Pierre Auger surface detectors. We developed the algorithm of artificial neural network on a MATLAB platform. Trained network that we implemented into the largest Cyclone V E FPGA was used for the prototype of the front-end board for the AugerPrime. We tested several variants, and the Levenberg–Marquardt algorithm (trainlm) was the most efficient. The network was trained: (a) to recognize ‘old’ very inclined showers (real Auger data were used as patterns for both positive and negative markers: for reconstructed inclined showers and for triggered by time over threshold (ToT), respectively, (b) to recognize ‘neutrino-induced showers’. Here, we used simulated data for positive markers and vertical real showers for negative ones.",signatures:"Zbigniew Szadkowski, Dariusz Głas and Krzysztof Pytel",downloadPdfUrl:"/chapter/pdf-download/51277",previewPdfUrl:"/chapter/pdf-preview/51277",authors:[{id:"67836",title:"Prof.",name:"Zbigniew Piotr",surname:"Szadkowski",slug:"zbigniew-piotr-szadkowski",fullName:"Zbigniew Piotr Szadkowski"},{id:"185396",title:"Dr.",name:"Dariusz",surname:"Głas",slug:"dariusz-glas",fullName:"Dariusz Głas"},{id:"185397",title:"Dr.",name:"Krzysztof",surname:"Pytel",slug:"krzysztof-pytel",fullName:"Krzysztof Pytel"}],corrections:null},{id:"50680",title:"From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy",doi:"10.5772/63332",slug:"from-fuzzy-expert-system-to-artificial-neural-network-application-to-assisted-speech-therapy",totalDownloads:2203,totalCrossrefCites:3,totalDimensionsCites:6,hasAltmetrics:1,abstract:"This chapter addresses the following question: What are the advantages of extending a fuzzy expert system (FES) to an artificial neural network (ANN), within a computer‐based speech therapy system (CBST)? We briefly describe the key concepts underlying the principles behind the FES and ANN and their applications in assisted speech therapy. We explain the importance of an intelligent system in order to design an appropriate model for real‐life situations. We present data from 1‐year application of these concepts in the field of assisted speech therapy. Using an artificial intelligent system for improving speech would allow designing a training program for pronunciation, which can be individualized based on specialty needs, previous experiences, and the child's prior therapeutical progress. Neural networks add a great plus value when dealing with data that do not normally match our previous designed pattern. Using an integrated approach that combines FES and ANN allows our system to accomplish three main objectives: (1) develop a personalized therapy program; (2) gradually replace some human expert duties; (3) use “self‐learning” capabilities, a component traditionally reserved for humans. The results demonstrate the viability of the hybrid approach in the context of speech therapy that can be extended when designing similar applications.",signatures:"Ovidiu Schipor, Oana Geman, Iuliana Chiuchisan and Mihai Covasa",downloadPdfUrl:"/chapter/pdf-download/50680",previewPdfUrl:"/chapter/pdf-preview/50680",authors:[{id:"180248",title:"Dr.",name:"Oana",surname:"Geman",slug:"oana-geman",fullName:"Oana Geman"},{id:"181060",title:"Prof.",name:"Ovidiu-Andrei",surname:"Schipor",slug:"ovidiu-andrei-schipor",fullName:"Ovidiu-Andrei Schipor"},{id:"181064",title:"Dr.",name:"Iuliana",surname:"Chiuchisan",slug:"iuliana-chiuchisan",fullName:"Iuliana Chiuchisan"},{id:"185161",title:"Prof.",name:"Mihai",surname:"Covasa",slug:"mihai-covasa",fullName:"Mihai Covasa"}],corrections:null},{id:"50519",title:"Neural Networks for Gas Turbine Diagnosis",doi:"10.5772/63107",slug:"neural-networks-for-gas-turbine-diagnosis",totalDownloads:2227,totalCrossrefCites:5,totalDimensionsCites:4,hasAltmetrics:1,abstract:"The present chapter addresses the problems of gas turbine gas path diagnostics solved using artificial neural networks. As a very complex and expensive mechanical system, a gas turbine should be effectively monitored and diagnosed. Being universal and powerful approximation and classification techniques, neural networks have become widespread in gas turbine health monitoring over the past few years. Applications of such networks as a multilayer perceptron, radial basis network, probabilistic neural network, and support vector network were reported. However, there is a lack of manuals that summarize neural network applications for gas turbine diagnosis.",signatures:"Igor Loboda",downloadPdfUrl:"/chapter/pdf-download/50519",previewPdfUrl:"/chapter/pdf-preview/50519",authors:[{id:"179551",title:"Dr.",name:"Igor",surname:"Loboda",slug:"igor-loboda",fullName:"Igor Loboda"}],corrections:null},{id:"51130",title:"Application of Neural Networks (NNs) for Fabric Defect Classification",doi:"10.5772/63427",slug:"application-of-neural-networks-nns-for-fabric-defect-classification",totalDownloads:1894,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,abstract:"The defect classification is as important as the defect detection in fabric inspection process. The detected defects are classified according to their types and recorded with their names during manual fabric inspection process. The material is selected as “undyed raw denim” fabric in this study. Four commonly occurring defect types, hole, warp lacking, weft lacking and soiled yarn, were classified by using artificial neural network (ANN) method. The defects were automatically classified according to their texture features. Texture feature extraction algorithm was developed to acquire the required values from the defective fabric samples. The texture features were assessed as the network input values and the defect classification is obtained as the output. The defective images were classified with an average accuracy rate of 96.3%. As the hole defect was recognized with 100% accuracy rate, the others were recognized with a rate of 95%.",signatures:"H. İbrahim Çelik, L. Canan Dülger and Mehmet Topalbekiroğlu",downloadPdfUrl:"/chapter/pdf-download/51130",previewPdfUrl:"/chapter/pdf-preview/51130",authors:[{id:"89349",title:"Prof.",name:"Lale Canan",surname:"Dülger",slug:"lale-canan-dulger",fullName:"Lale Canan Dülger"},{id:"178353",title:"Dr.",name:"Halil",surname:"Çelik",slug:"halil-celik",fullName:"Halil Çelik"},{id:"185835",title:"Prof.",name:"Mehmet",surname:"Topalbekiroğlu",slug:"mehmet-topalbekiroglu",fullName:"Mehmet Topalbekiroğlu"}],corrections:null},{id:"50967",title:"Thunderstorm Predictions Using Artificial Neural Networks",doi:"10.5772/63542",slug:"thunderstorm-predictions-using-artificial-neural-networks",totalDownloads:2119,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Artificial neural network (ANN) model classifiers were developed to generate ≤15h predictions of thunderstorms within three 400-km2 domains. The feed-forward, multi-layer perceptron and single hidden layer network topology, scaled conjugate gradient learning algorithm, and the sigmoid (linear) transfer function in the hidden (output) layer were used. The optimal number of neurons in the hidden layer was determined iteratively based on training set performance. Three sets of nine ANN models were developed: two sets based on predictors chosen from feature selection (FS) techniques and one set with all 36 predictors. The predictors were based on output from a numerical weather prediction (NWP) model. This study amends an earlier study and involves the increase in available training data by two orders of magnitude. ANN model performance was compared to corresponding performances of operational forecasters and multi-linear regression (MLR) models. Results revealed improvement relative to ANN models from the previous study. Comparative results between the three sets of classifiers, NDFD, and MLR models for this study were mixed—the best performers were a function of prediction hour, domain, and FS technique. Boosting the fraction of total positive target data (lightning strikes) in the training set did not improve generalization.",signatures:"Waylon G. Collins and Philippe Tissot",downloadPdfUrl:"/chapter/pdf-download/50967",previewPdfUrl:"/chapter/pdf-preview/50967",authors:[{id:"179979",title:"Mr.",name:"Waylon",surname:"Collins",slug:"waylon-collins",fullName:"Waylon Collins"},{id:"179981",title:"Dr.",name:"Philippe",surname:"Tissot",slug:"philippe-tissot",fullName:"Philippe Tissot"}],corrections:null},{id:"50486",title:"Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks",doi:"10.5772/63109",slug:"analyzing-the-impact-of-airborne-particulate-matter-on-urban-contamination-with-the-help-of-hybrid-n",totalDownloads:1913,totalCrossrefCites:1,totalDimensionsCites:8,hasAltmetrics:0,abstract:"In this study, particulate matter (PM), total suspended particulate (TSP), PM10, and PM2.5 fractions) concentrations were recorded in various cities from south of Romania to build the corresponding time series for various intervals. First, the time series of each pollutant were used as inputs in various configurations of feed-forward neural networks (FANN) to find the most suitable network architecture to the PM specificity. The outputs were evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Pearson correlation coefficient (r) between observed series and output series. Second, each time series was decomposed using Daubechies wavelets of third order into its corresponding components. Each decomposed component of a PM time series was used as input in the optimal feed-forward neural networks (FANN) architecture established in the first step. The output of each component was re-included to form the modeled series of the original pollutant time series.",signatures:"Daniel Dunea and Stefania Iordache",downloadPdfUrl:"/chapter/pdf-download/50486",previewPdfUrl:"/chapter/pdf-preview/50486",authors:[{id:"82418",title:"Prof.",name:"Stefania Felicia",surname:"Iordache",slug:"stefania-felicia-iordache",fullName:"Stefania Felicia Iordache"},{id:"180202",title:"Associate Prof.",name:"Daniel",surname:"Dunea",slug:"daniel-dunea",fullName:"Daniel Dunea"}],corrections:null},{id:"50563",title:"Neural Networks Applications for the Remote Sensing of Hydrological Parameters",doi:"10.5772/63165",slug:"neural-networks-applications-for-the-remote-sensing-of-hydrological-parameters",totalDownloads:1610,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:1,abstract:"The main artificial neural networks (ANN)‐based retrieval algorithms developed at the Institute of Applied Physics (IFAC) are reviewed here. These algorithms aim at retrieving the main hydrological parameters, namely the soil moisture content (SMC), the plant water content (PWC) of agricultural vegetation, the woody volume of forests (WV) and the snow depth (SD) or snow water equivalent (SWE), from data collected by active (SAR/scatterometers) and passive (radiometers) microwave sensors operating from space. Taking advantage of the fast computation, ANN are able to generate output maps of the target parameter at both local and global scales, with a resolution varying from hundreds of meters to tens of kilometres, depending on the considered sensor. A peculiar strategy adopted for the training, which has been obtained by combining satellite measurements with data simulated by electromagnetic models (based on the radiative transfer theory, RTT), made these algorithms robust and site independent. The obtained results demonstrated that ANN are a powerful tool for estimating the hydrological parameters at different spatial scales, provided that they have been trained with consistent datasets, made up by both experimental and theoretical data.",signatures:"Emanuele Santi",downloadPdfUrl:"/chapter/pdf-download/50563",previewPdfUrl:"/chapter/pdf-preview/50563",authors:[{id:"180474",title:"Dr.",name:"Emanuele",surname:"Santi",slug:"emanuele-santi",fullName:"Emanuele Santi"}],corrections:null},{id:"51330",title:"Advanced Methods in Neural Networks-Based Sensitivity Analysis with their Applications in Civil Engineering",doi:"10.5772/64026",slug:"advanced-methods-in-neural-networks-based-sensitivity-analysis-with-their-applications-in-civil-engi",totalDownloads:2419,totalCrossrefCites:13,totalDimensionsCites:18,hasAltmetrics:0,abstract:"Artificial neural networks (ANNs) are powerful tools that are used in various engineering fields. Their characteristics enable them to solve prediction, regression, and classification problems. Nevertheless, the ANN is usually thought of as a black box, in which it is difficult to determine the effect of each explicative variable (input) on the dependent variables (outputs) in any problem. To investigate such effects, sensitivity analysis is usually applied on the optimal pre-trained ANN. Existing sensitivity analysis techniques suffer from drawbacks. Their basis on a single optimal pre-trained ANN model produces instability in parameter sensitivity analysis because of the uncertainty in neural network modeling. To overcome this deficiency, two successful sensitivity analysis paradigms, the neural network committee (NNC)-based sensitivity analysis and the neural network ensemble (NNE)-based parameter sensitivity analysis, are illustrated in this chapter. An NNC is applied in a case study of geotechnical engineering involving strata movement. An NNE is implemented for sensitivity analysis of two classic problems in civil engineering: (i) the fracture failure of notched concrete beams and (ii) the lateral deformation of deep-foundation pits. Results demonstrate good ability to analyze the sensitivity of the most influential parameters, illustrating the underlying mechanisms of such engineering systems.",signatures:"Maosen Cao, Nizar F. Alkayem, Lixia Pan and Drahomír Novák",downloadPdfUrl:"/chapter/pdf-download/51330",previewPdfUrl:"/chapter/pdf-preview/51330",authors:[{id:"180549",title:"Prof.",name:"Maosen",surname:"Cao",slug:"maosen-cao",fullName:"Maosen Cao"},{id:"180560",title:"Dr.",name:"Lixia",surname:"Pan",slug:"lixia-pan",fullName:"Lixia Pan"},{id:"180562",title:"Dr.",name:"Nizar Faisal",surname:"Alkayem",slug:"nizar-faisal-alkayem",fullName:"Nizar Faisal Alkayem"}],corrections:null},{id:"51286",title:"Artificial Neural Networks in Production Scheduling and Yield Prediction of Semiconductor Wafer Fabrication System",doi:"10.5772/63444",slug:"artificial-neural-networks-in-production-scheduling-and-yield-prediction-of-semiconductor-wafer-fabr",totalDownloads:2052,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"With the development of artificial intelligence, the artificial neural networks (ANN) are widely used in the control, decision‐making and prediction of complex discrete event manufacturing systems. Wafer fabrication is one of the most complicated and high competence manufacturing phases. The production scheduling and yield prediction are two critical issues in the operation of semiconductor wafer fabrication system (SWFS). This chapter proposed two fuzzy neural networks for the production rescheduling strategy decision and the die yield prediction. Firstly, a fuzzy neural network (FNN)‐based rescheduling decision model is implemented, which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to the current system disturbances. The experimental results demonstrate the effectiveness of proposed FNN‐based rescheduling decision mechanism approach over the alternatives (back‐propagation neural network and Multivariate regression). Secondly, a novel fuzzy neural network‐based yield prediction model is proposed to improve prediction accuracy of die yield in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.",signatures:"Jie Zhang, Junliang Wang and Wei Qin",downloadPdfUrl:"/chapter/pdf-download/51286",previewPdfUrl:"/chapter/pdf-preview/51286",authors:[{id:"180966",title:"Prof.",name:"Jie",surname:"Zhang",slug:"jie-zhang",fullName:"Jie Zhang"},{id:"188119",title:"Mr.",name:"Junliang",surname:"Wang",slug:"junliang-wang",fullName:"Junliang Wang"},{id:"188120",title:"Dr.",name:"Wei",surname:"Qin",slug:"wei-qin",fullName:"Wei Qin"}],corrections:null},{id:"51444",title:"Neural Network Inverse Modeling for Optimization",doi:"10.5772/63678",slug:"neural-network-inverse-modeling-for-optimization",totalDownloads:2278,totalCrossrefCites:4,totalDimensionsCites:4,hasAltmetrics:0,abstract:"In this chapter, artificial neural networks (ANNs) inverse model is applied for estimating the thermal performance () in parabolic trough concentrator (PTC). A recurrent neural network architecture is trained using the Kalman Filter learning from experimental database obtained from PTCs operations. Rim angle (φr), inlet (Tin), outlet (Tout) fluid temperatures, ambient temperature (Ta), water flow (Fw), direct solar radiation (Gb) and the wind velocity (Vw) were used as main input variables within the neural network model in order to estimate the thermal performance with an excellent agreement (R2=0.999) between the experimental and simulated values. The optimal operation conditions of parabolic trough concentrator are established using artificial neural network inverse modeling. The results, using experimental data, showed that the recurrent neural network (RNN) is an excellent tool for modeling and optimization of PTCs.",signatures:"Oscar May, Luis J. Ricalde, Bassam Ali, Eduardo Ordoñez López,\nEduardo Venegas-Reyes and Oscar A. Jaramillo",downloadPdfUrl:"/chapter/pdf-download/51444",previewPdfUrl:"/chapter/pdf-preview/51444",authors:[{id:"181162",title:"Dr.",name:"Luis Josue",surname:"Ricalde Castellanos",slug:"luis-josue-ricalde-castellanos",fullName:"Luis Josue Ricalde Castellanos"},{id:"185657",title:"Dr.",name:"Oscar",surname:"May",slug:"oscar-may",fullName:"Oscar May"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"10651",title:"Machine Learning",subtitle:"Algorithms, Models and Applications",isOpenForSubmission:!1,hash:"6208156401c496e0a4ca5ff4265324cc",slug:"machine-learning-algorithms-models-and-applications",bookSignature:"Jaydip Sen",coverURL:"https://cdn.intechopen.com/books/images_new/10651.jpg",editedByType:"Edited by",editors:[{id:"4519",title:"Prof.",name:"Jaydip",surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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\r\n\tCerebral palsy is defined as "a group of permanent disorders of the development of movement and posture, causing activity limitation, that is attributed to non-progressive disturbances that occurred in the developing fetal or infant brain." Cerebral palsy is the most common motor disability seen in childhood. Cerebral palsy is caused by abnormal brain development or damage to the developing brain that results in a person’s inability to control his or her muscles. The most common structural problem is within the white matter of the brain. It may occur during pregnancy, delivery, the first month of life, or less commonly in early childhood. There are four types of cerebral palsy: spastic, dyskinetic (also includes athetoid, choreoathetosis, and dystonic cerebral palsies), ataxic, and mixed types. The most common of those is the spastic type cerebral palsy. Although there are treatment alternatives for cerebral palsy, there is still room for the best medical care for patients with cerebral palsy. In this book, we will also include the future perspective for the treatment of cerebral palsy.
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Cooper",slug:"robert-d.-cooper"}]},{id:"43671",title:"Ecology of Larval Habitats",slug:"ecology-of-larval-habitats",signatures:"Eliška Rejmánková, John Grieco, Nicole Achee and Donald R.\nRoberts",authors:[{id:"151632",title:"Prof.",name:"Nicole",middleName:null,surname:"Achee",fullName:"Nicole Achee",slug:"nicole-achee"},{id:"152601",title:"Prof.",name:"Eliska",middleName:null,surname:"Rejmankova",fullName:"Eliska Rejmankova",slug:"eliska-rejmankova"},{id:"169016",title:"Dr.",name:"John",middleName:null,surname:"Grieco",fullName:"John Grieco",slug:"john-grieco"}]},{id:"43954",title:"From Anopheles to Spatial Surveillance: A Roadmap Through a Multidisciplinary Challenge",slug:"from-anopheles-to-spatial-surveillance-a-roadmap-through-a-multidisciplinary-challenge",signatures:"Valérie Obsomer, Nicolas Titeux, Christelle Vancustem, Grégory\nDuveiller, Jean-François Pekel, Steve Connor, Pietro Ceccato and\nMarc Coosemans",authors:[{id:"131417",title:"Dr.",name:"Valérie",middleName:null,surname:"Obsomer",fullName:"Valérie Obsomer",slug:"valerie-obsomer"},{id:"152754",title:"Prof.",name:"Marc",middleName:null,surname:"Coosemans",fullName:"Marc Coosemans",slug:"marc-coosemans"},{id:"153949",title:"Dr.",name:"Pietro",middleName:null,surname:"Ceccato",fullName:"Pietro Ceccato",slug:"pietro-ceccato"},{id:"153950",title:"Dr.",name:"Gregory",middleName:null,surname:"Duveiller",fullName:"Gregory Duveiller",slug:"gregory-duveiller"},{id:"153952",title:"Dr.",name:"Christelle",middleName:null,surname:"Vancutsem",fullName:"Christelle Vancutsem",slug:"christelle-vancutsem"},{id:"153980",title:"Dr.",name:"Nicolas",middleName:null,surname:"Titeux",fullName:"Nicolas Titeux",slug:"nicolas-titeux"},{id:"154158",title:"Dr.",name:"Steve J",middleName:null,surname:"Connor",fullName:"Steve J Connor",slug:"steve-j-connor"},{id:"167685",title:"MSc.",name:"Jean-Francois",middleName:null,surname:"Pekel",fullName:"Jean-Francois Pekel",slug:"jean-francois-pekel"}]},{id:"43960",title:"Simian Malaria Parasites: Special Emphasis on Plasmodium knowlesi and Their Anopheles Vectors in Southeast Asia",slug:"simian-malaria-parasites-special-emphasis-on-plasmodium-knowlesi-and-their-anopheles-vectors-in-sout",signatures:"Indra Vythilingam and Jeffery Hii",authors:[{id:"151116",title:"Dr.",name:"Indra",middleName:null,surname:"Vythilingam",fullName:"Indra Vythilingam",slug:"indra-vythilingam"},{id:"169006",title:"Dr.",name:"Jeffery",middleName:null,surname:"Hii",fullName:"Jeffery Hii",slug:"jeffery-hii"}]},{id:"44039",title:"Thermal Stress and Thermoregulation During Feeding in Mosquitoes",slug:"thermal-stress-and-thermoregulation-during-feeding-in-mosquitoes",signatures:"Chloé Lahondère and Claudio R. Lazzari",authors:[{id:"151619",title:"Prof.",name:"Claudio",middleName:null,surname:"R. Lazzari",fullName:"Claudio R. Lazzari",slug:"claudio-r.-lazzari"},{id:"151620",title:"Ms.",name:"Chloé",middleName:null,surname:"Lahondère",fullName:"Chloé Lahondère",slug:"chloe-lahondere"}]},{id:"43955",title:"The Anopheles Mosquito Microbiota and Their Impact on Pathogen Transmission",slug:"the-anopheles-mosquito-microbiota-and-their-impact-on-pathogen-transmission",signatures:"Mathilde Gendrin and George K. Christophides",authors:[{id:"154007",title:"Dr.",name:"Mathilde",middleName:null,surname:"Gendrin",fullName:"Mathilde Gendrin",slug:"mathilde-gendrin"},{id:"154008",title:"Prof.",name:"George",middleName:"K",surname:"Christophides",fullName:"George Christophides",slug:"george-christophides"}]},{id:"43829",title:"Bacterial Biodiversity in Midguts of Anopheles Mosquitoes, Malaria Vectors in Southeast Asia",slug:"bacterial-biodiversity-in-midguts-of-anopheles-mosquitoes-malaria-vectors-in-southeast-asia",signatures:"Sylvie Manguin, Chung Thuy Ngo, Krajana Tainchum, Waraporn\nJuntarajumnong, Theeraphap Chareonviriyaphap, Anne-Laure\nMichon and Estelle Jumas-Bilak",authors:[{id:"50017",title:"Prof.",name:"Sylvie",middleName:null,surname:"Manguin",fullName:"Sylvie Manguin",slug:"sylvie-manguin"},{id:"75315",title:"Prof.",name:"Theeraphap",middleName:null,surname:"Chareonviriyaphap",fullName:"Theeraphap Chareonviriyaphap",slug:"theeraphap-chareonviriyaphap"},{id:"88985",title:"Prof.",name:"Anne-Laure",middleName:null,surname:"Michon",fullName:"Anne-Laure Michon",slug:"anne-laure-michon"},{id:"88986",title:"Prof.",name:"Estelle",middleName:null,surname:"Jumas-Bilak",fullName:"Estelle Jumas-Bilak",slug:"estelle-jumas-bilak"},{id:"156016",title:"MSc.",name:"Chung Thuy",middleName:null,surname:"Ngo",fullName:"Chung Thuy Ngo",slug:"chung-thuy-ngo"},{id:"156018",title:"MSc.",name:"Krajana",middleName:null,surname:"Tainchum",fullName:"Krajana Tainchum",slug:"krajana-tainchum"},{id:"156019",title:"Dr.",name:"Waraporn",middleName:null,surname:"Juntarajumnong",fullName:"Waraporn Juntarajumnong",slug:"waraporn-juntarajumnong"}]},{id:"43899",title:"Distribution, Mechanisms, Impact and Management of Insecticide Resistance in Malaria Vectors: A Pragmatic Review",slug:"distribution-mechanisms-impact-and-management-of-insecticide-resistance-in-malaria-vectors-a-pragmat",signatures:"Vincent Corbel and Raphael N’Guessan",authors:[{id:"152666",title:"Dr.",name:"Vincent",middleName:null,surname:"Corbel",fullName:"Vincent Corbel",slug:"vincent-corbel"},{id:"169017",title:"Dr.",name:"Raphael",middleName:null,surname:"N'Guessan",fullName:"Raphael N'Guessan",slug:"raphael-n'guessan"}]},{id:"43851",title:"Perspectives on Barriers to Control of Anopheles Mosquitoes and Malaria",slug:"perspectives-on-barriers-to-control-of-anopheles-mosquitoes-and-malaria",signatures:"Donald R. Roberts, Richard Tren and Kimberly Hess",authors:[{id:"151439",title:"Prof.",name:"Donald",middleName:null,surname:"R. Roberts",fullName:"Donald R. Roberts",slug:"donald-r.-roberts"},{id:"151656",title:"Mr.",name:"Richard",middleName:null,surname:"Tren",fullName:"Richard Tren",slug:"richard-tren"},{id:"154152",title:"Ms.",name:"Kimberly",middleName:null,surname:"Hess",fullName:"Kimberly Hess",slug:"kimberly-hess"}]},{id:"43874",title:"Residual Transmission of Malaria: An Old Issue for New Approaches",slug:"residual-transmission-of-malaria-an-old-issue-for-new-approaches",signatures:"Lies Durnez and Marc Coosemans",authors:[{id:"152754",title:"Prof.",name:"Marc",middleName:null,surname:"Coosemans",fullName:"Marc Coosemans",slug:"marc-coosemans"},{id:"169018",title:"Dr.",name:"Lies",middleName:null,surname:"Durnez",fullName:"Lies Durnez",slug:"lies-durnez"}]},{id:"44330",title:"Vector Control: Some New Paradigms and Approaches",slug:"vector-control-some-new-paradigms-and-approaches",signatures:"Claire Duchet, Richard Allan and Pierre Carnevale",authors:[{id:"151662",title:"Dr.",name:"Pierre",middleName:null,surname:"Carnevale",fullName:"Pierre Carnevale",slug:"pierre-carnevale"},{id:"169000",title:"Dr.",name:"Richard",middleName:null,surname:"Allan",fullName:"Richard Allan",slug:"richard-allan"},{id:"169008",title:"Dr.",name:"Claire",middleName:null,surname:"Duchet",fullName:"Claire Duchet",slug:"claire-duchet"}]},{id:"43870",title:"New Salivary Biomarkers of Human Exposure to Malaria Vector Bites",slug:"new-salivary-biomarkers-of-human-exposure-to-malaria-vector-bites",signatures:"Papa M. Drame, Anne Poinsignon, Alexandra Marie, Herbert\nNoukpo, Souleymane Doucoure, Sylvie Cornelie and Franck\nRemoue",authors:[{id:"151515",title:"Dr.",name:"Papa Makhtar",middleName:null,surname:"Drame",fullName:"Papa Makhtar Drame",slug:"papa-makhtar-drame"},{id:"151648",title:"Dr.",name:"Franck",middleName:null,surname:"Remoué",fullName:"Franck Remoué",slug:"franck-remoue"},{id:"154034",title:"Dr.",name:"Anne",middleName:null,surname:"Poinsignon",fullName:"Anne Poinsignon",slug:"anne-poinsignon"},{id:"154035",title:"MSc.",name:"Alexandra",middleName:null,surname:"Marie",fullName:"Alexandra Marie",slug:"alexandra-marie"},{id:"154037",title:"Dr.",name:"Souleymane",middleName:null,surname:"Doucoure",fullName:"Souleymane Doucoure",slug:"souleymane-doucoure"},{id:"154038",title:"MSc.",name:"Herbert",middleName:null,surname:"Noukpo",fullName:"Herbert Noukpo",slug:"herbert-noukpo"},{id:"154039",title:"Dr.",name:"Sylvie",middleName:null,surname:"Cornélie",fullName:"Sylvie Cornélie",slug:"sylvie-cornelie"}]},{id:"44149",title:"Transgenic Mosquitoes for Malaria Control: From the Bench to the Public Opinion Survey",slug:"transgenic-mosquitoes-for-malaria-control-from-the-bench-to-the-public-opinion-survey",signatures:"Christophe Boëte and Uli Beisel",authors:[{id:"98400",title:"Dr.",name:"Christophe",middleName:null,surname:"Boëte",fullName:"Christophe Boëte",slug:"christophe-boete"},{id:"167749",title:"Dr.",name:"Uli",middleName:null,surname:"Beisel",fullName:"Uli Beisel",slug:"uli-beisel"}]}]}],publishedBooks:[{type:"book",id:"7064",title:"Current Perspectives in Human Papillomavirus",subtitle:null,isOpenForSubmission:!1,hash:"d92a4085627bab25ddc7942fbf44cf05",slug:"current-perspectives-in-human-papillomavirus",bookSignature:"Shailendra K. 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It is the second most important cereal crop in the world in terms of acreage and production. Global production of Maize was about 1040 million MT in the year 2016–2017, where in USA and China contributed about 38 and 23%, respectively. In India, maize is the 3rd most important food crop after rice and wheat, where about 15 million farmers are engaged in maize cultivation [1]. In India, Andhra Pradesh ranks first in maize production followed by Karnataka with per cent share of 20.9 and 16.5, respectively [2]. It has a share of 9% in about Rs. 100 billion agriculture sector gross domestic product [3]. Maize can be cultivated successfully in loamy sand to heavy clay, well aerated, neutral pH soils. As of tropical origin, it is highly sensitive to water stagnation, so avoid the cultivation in low-lying or poor drainage fields. Furthermore, extended low temperature less 5°C severally affects the crop. Optimum range of temperature for better crop growth and yield realization is 25–35°C [4]. Being day neutral, maize crop can be cultivated throughout the year which leads to high yield levels in a short period of time. In this chapter, we are going to discuss an array of different production technologies to be followed by farmers for successful cultivation and better realization of yields. A brief outline of the chapter is given below.
\nCentral America and Mexico is the primary centre of origin of maize which consists of a diversity of maize crop. Various studies reveal that maize crop was a significant crop in Mexico about 5000 years ago. USA has the largest area under maize crop followed by Brazil, China, Mexico and India. USA also stands first in terms of production followed by China. In India, Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh and Punjab are the major maize growing states. Highest acreage and production is in Uttar Pradesh while average yield/ha is recorded in Andhra Pradesh [2, 5].
\nMaize crop can grow under diverse conditions from sea level to about 3000 m altitude throughout the year in many parts of the country. In Northern India,
Type of cultivar/hybrid to be grown depends on the crop season namely, spring,
Length of cropping season (days) | \nType of cultivar | \n
---|---|
More than 100 | \nLate maturing | \n
90 to 100 | \nMedium maturing | \n
80 to 90 | \nEarly maturing | \n
Choice of cultivar as per length of growing season.
Due to occurrence of diverse climatic conditions in country, planting time varies from place to place. Optimum planting time in different agro-climatic regions is described in \nTable 2\n [2]. The optimum time to sow the crop depends on availability of irrigation facilities. For example, if irrigation facilities are available, maize crop can be sown about 2 weeks before onset of monsoon while under rainfed conditions, crop is sown with the onset of monsoon to have optimum moisture regime so that proper plant stand can be maintained in field. In Punjab, Maize crop can be sown during all seasons at following sowing times (\nTable 3\n) [7, 8]:
\nAgro-climatic region | \nOptimum planting time | \n
---|---|
Indo-gangatic plains | \n15 June–15 July | \n
North-western hills | \nApril-early May | \n
North-eastern hills | \nFirst fortnight of March | \n
Peninsular region | \nMay–June | \n
Optimum planting of maize in different agro-climatic regions.
Season | \nPlanting time | \n
---|---|
\n | \nLast week of May to last week of June | \n
Spring | \n20th of Jan to 15th of Feb | \n
Season wise planting time maize.
Being a non-tillering crop it cannot compensate for the lost space if proper plant stand is not maintained under field conditions. So maintenance of 60–65,000 plants/ha is pre-requisite for realizing maximum yield. Sowing of the crop should be done 60 × 20–25 cm crop geometry. For hybrids and composites, seed rate can be used with respect to seed weight and requirement of plant population as given in \nTable 4\n [2, 8].
\nHybrids | \n20–25 kg/ha | \n
Composites | \n18–20 kg/ha | \n
Seed rate of maize hybrids and composites.
Seed treatment plays a pivotal role in prevention of diseases and availability of nutrients to growing crop. For instance, seed treatment of maize with Bavistin or Derosal or Agrozim 50 WP (Carbendazim) @ 3 g/kg seed prevents the attack of seed and soil borne diseases in maize crop. Furthermore, treatment of seed with consortium (biofertilizer) @ 1.25 kg/ha helps in yield enhancement and improvement of soil health [2, 7, 8].
\nCrop geometry has direct effect on inter and intra-plant competition in field crops. Maize crop can be planted in varied crop geometries (\nTable 5\n) depending upon the purpose of cultivation [2, 8]. Interculture operations like thinning, gap filling and earthing-up play critical role in performance of maize crop. Thinning needs to be performed about 10 days after germination to keep 1 plant/hill. Further, 2 earthing-ups are required in maize crop. First at 35–40 and 2nd at 60–65 days after germination [9].
\nPurpose | \nCrop geometry | \n
---|---|
Grain crop | \n60 cm × 20 cm; 75 cm × 20 cm | \n
Baby corn | \n30 cm × 20 cm; 60 cm × 15 cm | \n
Fodder | \n30 cm × 10 cm | \n
Crop geometry of maize to be followed as per requirement.
Although crop establishment is a series of events that depends on interactions of seed, soil moisture, method of sowing, machinery etc. but method of planting plays an important role in establishment of crop under given set of conditions. Maize is mainly sown directly through seed by using different methods of tillage & establishment. Recently, resource conservation technologies (RCTs) namely, zero tillage, minimum tillage, surface seeding etc. had came in practice in various maize based cropping system and are cost effective and environment friendly. Following are major planting methods that vary from situation to situation.
\nMaize crop can be cultivated without any primary tillage under no-till (\nFigure 1\n) with decreased cost of cultivation and better resource use efficiency. In this situation, maintenance of proper soil moisture at sowing and band placement of seed and fertilizers with zero-till seed-cum-fertilizer planter with furrow opener as per the soil texture and field conditions is pre-requisite. The technology is followed by large number of farmers especially under rice-maize and maize-wheat systems in peninssular and eastern India. If the field is infested with weeds, farmers can go for foliar spray of gramoxone 24 SL (paraquat) @ 1250 ml/ha about 24 hours before planting of maize crop [2, 7, 8].
\nMaize crop sown under zero tillage system.
This planting method (\nFigure 2\n) is considered best for cultivation during monsoon and winter seasons both under excess and limited water availability conditions. On non-uniform lands, this method is most suitable for successful cultivation of maize crop. Planting of crop needs to be done on the southern side of the east–west ridges/beds for better exposure to sunlight during winters and better crop stand. Raised bed planter having inclined plate, cupping or roller type seed dropping system should be used for planting that facilitates proper placement of seed and fertilizers in single operation for having good crop stand, higher productivity and resource use efficiency. Irrigation water can be saved to the tune of 20–30%. Under temporary excess soil moisture/water logging due to heavy rains, the furrows will act as drainage channels and crop can be saved from excess soil moisture stress [2, 5, 7, 8].
\nPlanting of maize crop on the ridges.
Maize crop can be cultivated by conventional tillage flat planting (\nFigure 3\n) depending upon soil type and availability of irrigation facilities. Light soils have high infiltration rate and low water holding capacity, so farmers can go for flat planting of maize crop. Under rainfed conditions, to have better moisture availability to crop for longer period, flat planting becomes better alternate. Flat planting is also beneficial when no tillage system gets infested with high weed population and chemical/manual weed control becomes non-economical [7, 8].
\nFlat sowing of maize crop.
It is better establishment technique winter maize (\nFigure 4\n) in the intensive cropping system where field cannot be vacated on time, to prevent the delayed planting and crop loss due to low temperature. Under this situation, nursery of the crop is raised on a smaller portion of land and seedlings are transplanted in required field as and when they achieve certain age. For example, if the fields are to be vacated during December–January, it is advisable to go for nursery sowing 30–40 days before the transplanting. Seedlings can be transplanted in the furrows followed by light irrigation [2, 5].
\nMaize crop establishment through transplanting system.
Furrow planting (\nFigure 5\n) of maize is recommended when crop is to be cultivated during spring season as high evaporative losses may lead to water deficit stress in flat and raised bed or ridge sowing [2, 5, 7, 8].
\nCrop establishment by furrow planting.
Water requirement of the maize crop varies from 400 to 600 mm [10]. Excess or shortage of moisture can have harmful impact on the crop growth. Proper drainage of standing water and meeting the crop needs at critical stages play a pivotal role in better crop performance. Especially for winter maize, it is advisable to keep soil wet (frequent & mild irrigation) during 15 December to 15 February to protect the crop from frost injury [3].
\nFlood method of irrigation is followed where maize crop is cultivated with flat sowing. Crop is irrigated as and when required. Generally, young seedlings, knee high stage (V8), flowering (VT) and grain 7.
\nfilling (GF) are critical stages and hence irrigation should be ensured at these stages [2, 7, 8].
\nWhen crop is cultivated as ridge/raised bed planting, furrow irrigation is followed. Care needs to be taken at first irrigation that water should not overflow on the ridges/beds. As a thumb rule, the irrigation should be applied in furrows up to 2/3rd height of the ridges/beds. In raised bed and in limited irrigation water, the irrigation water can also be applied in alternate furrows to save irrigation water. In rainfed conditions, tied-ridges prove helpful in conserving the rainwater, increasing its availability in the root zone for longer period [2, 7, 8, 11].
\nHigh temperature and high evaporative demand during summer season enhances the water requirement of maize crop as a result of which farmers go for a number of irrigation. To increase the water use efficiency of crop, above ground drip irrigation is recommended by Punjab Agricultural University. In this, broad beds are prepared at 1.20 m apart from centre to centre of furrow. These beds are 80 cm wide on the top and 40 cm wide furrows between beds. The beds are covered with U.V stabilized plastic film (Black) of 25 micron thickness (23 grams per m2). Two rows of maize are planted at a spacing of 60 cm keeping plant to plant distance of 20 cm. One lateral pipe is used to irrigate two rows of maize. The drippers are spaced 30 cm apart and are operated at a discharge of 2.2 L per hour as given in \nTable 6\n [7, 8, 12]. Prevailing climatic regimes of an area affect the efficiency of drip irrigation system [12].
\nMonth | \nTiming of irrigation (min) | \n
---|---|
February | \n22 | \n
March | \n64 | \n
April | \n120 | \n
May | \n130 | \n
Month-wise timing of above ground drip irrigation in spring maize.
* If discharge rate is different, time of irrigation may be adjusted proportionally by the formula:
\nIn field experiments, sub surface drip irrigation and fertigation resulted in 18.4% higher system productivity with saving of 28.5% applied irrigation water. Sub-surface irrigation technology can be followed in maize-wheat-summer moong cropping system. For this system, Place drip inline having dripper having 20 cm spacing at 20 cm depth with lateral to lateral spacing of 67.5 cm for sub surface drip irrigation in maize-wheat-summer moong cropping system. Sow one row of maize, two rows of wheat and two rows of summer moong on each drip inline during respective season. If discharge of the dripper is 2.2 L/hour, the schedule given in \nTable 7\n can be followed for sub-surface drip irrigation in above mentioned cropping system [7, 8, 10].
\nCrop | \nMonth | \nTiming of irrigation (min) | \n
---|---|---|
Maize | \nJuly | \n35 | \n
August | \n35 | \n|
September | \n50 | \n|
October | \n30 | \n|
Wheat | \nDecember | \n30 | \n
January | \n65 | \n|
February | \n70 | \n|
March | \n50 | \n|
Summer Moong | \nMay | \n60 | \n
June | \n45 | \n
Month-wise timing of sub-surface drip irrigation in maize-wheat-summer moong cropping system.
If discharge rate is different, then time of irrigation may be adjusted proportionally by the formula:
\nThis technique (\nFigure 6\n) involves alternate wetting and drying of two halves of root zone of crop plants during consecutive irrigations. The PRD technique was developed on the basis of knowledge of root-to-shoot chemical signaling (can be negative or positive) about soil conditions that regulates the shoot physiology. Alternating is essential for maintaining a constant emission of signals from the root-to-shoot, because prolonged exposure of root to drying soil may cause anatomical changes which reduce the ability of root to sense soil drying and not able to sustain the production of ABA for long time period [10]. Different methods to apply the PRD technique can be separation of root system into two parts with sheet particularly in pots, controlled alternate surface drip irrigation on half part of the root zone, controlled alternate subsurface drip irrigation on half part of the root zone or controlled alternate furrow irrigation [10].
\nField view of partial root drying irrigation technique in maize.
Maize crop is infested with grassy and broad leaf annual weeds. Among grassy,
Non-chemical weed control measures can physical or cultural that means manual removal of weeds from the maize fields. In cultural method, Give two hoeings 15–30 days after sowing with khurpa/kasaula/wheel-hoe/triphali/tractor-drawn cultivator. Mulching is practice of keeping crop residues or plastic sheets on the soil surface within the crop rows. Mulching helps in temperature regulation, water conservation as well weed control in field crops [7, 8].
\nSometimes due to continuous rains during the early stages of maize growth it becomes impossible to enter in the field. Also due to scarce availability of farm labour, the only effective way to control weeds is the use of herbicides. Spray of atrataf 50 WP (atrazine) @ 2 kg/ha on medium to heavy textured soils and 1.25 kg/ha in light soils within 10 days of sowing, using 500 L of water prove propitious in keeping weed population low in maize fields. Spray the herbicide uniformly at recommended rates to minimize residual toxicity to crops sown after maize. Alternatively, spray 262.5 ml/ha laudis 420 SC (tembotrione) in 375 L of water at 20 days after sowing provides effective control of mixed weed flora. For the control of
Among the cereal crops, maize in general and specifically hybrids are very responsive to nutrients applied through organic or inorganic means. The rate of application depends on soil nutrient status and cropping system. For realizing required yield, the dose of applied nutrients should be as par the soil supplying capacity and crop demand. As the response of maize crop to organic manures is remarkable so integrated nutrient management (INM) is very important option in maize based systems.
Apply 10–15 t/ha of good quality farmyard manure per hectare to the maize crop year after year [7, 8].
Green manure the field, to be put under maize with Dhaincha/Sunhemp/Cowpea. Cowpea/Dhaincha/Sunhemp should be sown during second fortnight of April using 12/20/20 kg seed per acre, respectively. The 50 days old green manure crop should be burried and allowed to decompose for about 10 days before sowing of maize. In case, summer moong crop is grown the straw should be burried before sowing of maize [7, 8].
Inoculate the maize seed with recommended bio-fertilizer as described earlier. For this, mix half kg packet of recommended consortium bio-fertilizer with 1 L of water and then thoroughly mix it with maize seed on clean pucca floor. Let it dry in shade and sow the seed immediately. Inoculation with bio-fertilizer should be done after treating the seed with fungicide. The seed inoculation with consortium biofertilizer increase grain yield as well as improves soil health [7, 8, 11].
Paddy straw compost @ 450 kg/ha along with recommended dose of fertilizers can be an alternate to farm yard manure [7, 8].
As a general recommendation, one could apply 120 kg N, 60 kg P2O5 and 40 kg K2O per hectare for hybrids and 80 kg N, 30 kg P2O5 and 20 kg K2O per hectare for composites. Drill one third of nitrogen and the entire quantity of phosphorous and potassium at the time of sowing. Top dress one third of nitrogen at the knee-high stage and the remaining one third at the pre tasseling stage. It may be noted that application of nitrogen fertilizer more than recommended dose is no substitute for FYM [7, 8].
Decreased Zn availability visuals emerge on middle leaves (2nd or 3rd from tip) of plants which include white or light yellow band and reddish veins on both sides of the midrib [7, 8]. Remedial measures are described in \nTable 8\n:
Method of application | \nZnSO4 (33%) | \nZnSO4 (21%) | \n
---|---|---|
Broadcasting | \n16.25 kilogram/ha | \n25 kilogram/ha | \n
Foliar application | \n1.88 + 0.94 kilogram unslaked lime | \n3 + 1.5 kilogram unslaked lime | \n
Remedial measures for Zn deficiency in maize.
It refers to simultaneous application of irrigation water and fertilizers by drip irrigation. By this method, FUE can go up to 80%. In drip irrigation model for spring maize, certain recommendations are made in respect to fertilizer application along with drip irrigation. For the medium fertility soils application of 200 kg of urea, 80 kg of mono ammonium phosphate (MAP) and 40 kg of muriate of potash (white)/ha is recommended. Start fertigation 12 days after sowing of maize and apply 25% of the fertilizers in four equal splits during first month on weekly basis. Rest of the fertilizer should be applied in equal splits on weekly basis upto first week of May. Furthermore, in sub-surface drip irrigation, fertilizer can be applied to maize crop when grown in maize-wheat-summer moong cropping system. For instance, Apply sub surface drip irrigation at 3 days interval for maize and summer moong with fertigation of 80% recommended dose of NPK. In maize, apply 1/5 dose of NPK at sowing and fertigate remaining P and K in 5 splits and N in 7 splits at 9 days interval starting from 15 DAS. Apply sub surface drip irrigation at 7 days interval up to mid-February and thereafter at 5 days interval to wheat with fertigation of 80% recommended dose of NPK. In wheat, apply 1/5th dose of NPK at sowing and fertigated the remaining NPK in 8 splits at 7 days interval starting from crown root initiation. In summer moong, fertigated NPK dose in 5 equal splits at 6 days interval starting from 10 DAS. Use urea, mono ammonium 119 phosphate and muriate of potash as source of N, P and K, respectively [7, 8].
\nIPM (\nFigure 7\n) is highly efficient and eco-friendly strategy which includes integrated use of all possible alternates that can be biological, physical, cultural or chemical for controlling pests. Growers who are aware of the potential for pest infestation follow a four-tiered approach. The four steps include: set action thresholds, monitor and identify pests, prevention and control [11, 13].
\n
\n
\n
\n
Components of IPM.
This approach encompasses use of living entities for the control of insect-pests and diseases. Living entities can be predators, herbivores or parasites along with intensive human interference. For controlling maize borer and other insects, apply bio-insecticides like Neemazal (1%) @ 300 ml/ha. The maize borer can also be managed by using tricho-cards twice having 40,000 eggs of Corcyra parasitized by
\n
Summer plowing of field.
Destruction of perennating stages in stubbles, cobs, stalks.
Cut and bury the severely infested plant parts.
Spray the crop 2–3 weeks after sowing as soon as borer injury to the leaves is noticed with Coragen 18.5 SC (chlorantraniliprole) @ 75 ml using 150 L water/ha with knap-sack sprayer [7, 8, 11].
Damage of maize crop by maize stem borer.
\n
Spring crop should be sown between January 20 and February 15.
Seed should be treated with gaucho (imidacloprid) 600 FS @ 6 ml/kg seed [7].
Attack of shoot fly in maize crop.
\n
Collection and destruction of young larvae by cutting and burying the attacked plant parts.
\n
In recent years, non-associated pests (\nFigures 10\n and \n11\n) have been reported in different parts of India with the details as below (\nTable 9\n) [3]:
\nAttack of army worm in maize crop.
Attack of pollen eating beetle on maize tassels.
Pest name | \nPlant part infested | \nRegion | \n
---|---|---|
\n | \nCob | \nSouthern India | \n
\n | \nPollen | \nNorthern India | \n
Recently reported pest infestations in maize.
\n
\n
Maize crop infested with banded leaf and sheath blight.
\n
Maydis leaf blight attack in maize crop.
\n
\n
Removal of secondary host, that is,
Proper drainage of the fields.
Spray mancozeb @ 500 g/ha in 250 L of water after about a fortnight of sowing. Give two more sprays at 10-day intervals. Grow recommended varieties [7, 8, 11].
Maize crop attacked by brown stripe downy mildew.
For use as grain, cobs should be harvested when grains are at about 20% moisture. Whereas to consume as sweet corn, harvesting should be done when tassel starts turning brown and swelling of cob initiates. In case of baby corn, harvest young cob when the silk is near emergence [6].
\nSystem in which >2 crops are cultivated in proper sequence on given piece of land during a year. Efficiency of the system is determined by a number of factors namely, manpower, choice of crop/cultivar, availability of irrigation facilities etc. technical competence, need based farm activities play a critical role in performance of multiple cropping. Following strategies can be adopted for successful adoption of intensive cropping:
\n\nMaize crop can be cultivated along with other crops as intercrops for better utilization of resources, enhanced income per unit area and time basis. For instance, intercropping of 1 row of fodder cowpea or maize, groundnut and soybean in
Crops like wheat, paddy, potato, sugarcane, chickpea, berseem, barley, oats etc. can be grown successfully after harvest of maize crop. Following are some of the most appropriate maize based cropping systems [2, 8]:
Cowpea/pearl millet/maize (fodder)
Spring maize-basmati-wheat
Maize/rice-wheat
Maize/rice-potato-wheat
Maize-potato/
Maize-potato-onion
Maize-potato-mentha
Maize-wheat/celery-pearl millet fodder
Maize/rice-
Maize-vegetable pea/potato-spring maize
Maize-potato-sugarcane-wheat
Maize-wheat-sugarcane
Maize drying is a vital operation which involves removal of moisture from the cobs/grains. It is carried out because high moisture grain will deteriorate rapidly due to grain respiration and heating, germination of grains, mold (fungal) growth and subsequent incidence of mycotoxins (e.g. aflatoxin) and increase insect multiplication and damage. The optimum moisture content of maize should be 14% or less [14].
\n\n
\n
\n
\n
A portable maize dryer 3 ton capacity has been developed by Punjab Agricultural University, Ludhiana as per international norms and recommended to dry maize grains from a moisture level 25 to 15% in 8–10 hours. This cross-flow dryer has three pass, indirect type diesel fired heating system. A control panel to regulate and display the temperature of heated air, exit air and speed of air blower with variable frequency drive is provided for better operation. The dryer can maintain air temperature 60–75°C with the grain temperature of 45°C for seed and 60°C for commercial purpose. The dryer is capable of drying maize grain @ 1.0–1.5% per hour consuming about 4 L/hour. of diesel initially for 1 hour. A provision of heat recovery from flue gases ensures higher fuel efficiency with reduced diesel consumption to about 2 L/hour, later on. The dryer can be operated both with tractor PTO or electricity. One each of skilled and unskilled labor is required to operate this dryer [8].
\n\n
Adoption of production techniques namely, selection of cultivars, irrigation techniques, INM. IPM and other technological interventions certainly prove propitious in achieving the potential yield targets.
Maize crop provides better opportunity to scientific community in exploration of resource conservation technologies like zero tillage, partial root drying irrigation, integrated pest management etc.
Characteristically, maize crop can fit well in diverse crop rotations and intercropping options, which enhances its preference in intensive agriculture.
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