Ibrahim A. Hameed

By Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and Cornelia Weltzien

In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.

Part of the book: Automation in Agriculture

By Amid Heravi, Desa Ahmad, Ibrahim A. Hameed, Redmond Ramin Shamshiri, Siva K. Balasundram and Muhammad Yamin

A prototype robot that moves on a monorail along the greenhouse for weed elimination between cucumber plants was designed and developed. The robot benefits from three arrays of ultrasonic sensors for weed detection and a PIC18 F4550-E/P microcontroller board for processing. The feedback from the sensors activates a robotic arm, which moves inside the rows of the cucumber plants for cutting the weeds using rotating blades. Several experiments were carried out inside a greenhouse to find the best combination of arm motor (AM) speed, blade rotation (BR) speed, and blade design. We assigned three BR speeds of 3500, 2500, and 1500 rpm, and two AM speed of 10 and 30 rpm to three blade designs of S-shape, triangular shape, and circular shape. Results indicated that different types of blades, different BR speed, and different AM speed had significant effects (P < 0.05) on the percentage of weeds cut (PWC); however, no significant interaction effects were observed. The comparison between the interaction effect of the factors (three blade designs, three BR speeds, and two AM speeds) showed that maximum mean PWC was equal to 78.2% with standard deviation of 3.9% and was achieved with the S-shape blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm. Using this setting, the maximum PWC that the robot achieved in a random experiment was 95%. The lowest mean PWC was observed with the triangular-shaped blade (mean of 50.39% and SD = 1.86), which resulted from BR speed of 1500 rpm and AM speed of 30 rpm. This study can contribute to the commercialization of a reliable and affordable robot for automated weed control in greenhouse cultivation of cucumber.

Part of the book: Agricultural Robots

By Redmond Ramin Shamshiri, Ibrahim A. Hameed, Siva K. Balasundram, Desa Ahmad, Cornelia Weltzien and Muhammad Yamin

Unmanned aerial vehicles carrying multimodal sensors for precision agriculture (PA) applications face adaptation challenges to satisfy reliability, accuracy, and timeliness. Unlike ground platforms, UAV/drones are subjected to additional considerations such as payload, flight time, stabilization, autonomous missions, and external disturbances. For instance, in oil palm plantations (OPP), accruing high resolution images to generate multidimensional maps necessitates lower altitude mission flights with greater stability. This chapter addresses various UAV-based smart farming and PA solutions for OPP including health assessment and disease detection, pest monitoring, yield estimation, creation of virtual plantations, and dynamic Web-mapping. Stabilization of UAVs was discussed as one of the key factors for acquiring high quality aerial images. For this purpose, a case study was presented on stabilizing a fixed-wing Osprey drone crop surveillance that can be adapted as a remote sensing research platform. The objective was to design three controllers (including PID, LQR with full state feedback, and LQR plus observer) to improve the automatic flight mission. Dynamic equations were decoupled into lateral and longitudinal directions, where the longitudinal dynamics were modeled as a fourth order two-inputs-two-outputs system. State variables were defined as velocity, angle of attack, pitch rate, and pitch angle, all assumed to be available to the controller. A special case was considered in which only velocity and pitch rate were measurable. The control objective was to stabilize the system for a velocity step input of 10m/s. The performance of noise effects, model error, and complementary sensitivity was analyzed.

Part of the book: Agricultural Robots

By Muhammad Sultan, Hadeed Ashraf, Takahiko Miyazaki, Redmond R. Shamshiri and Ibrahim A. Hameed

Temperature and humidity control are crucial in next generation greenhouses. Plants require optimum temperature/humidity and vapor pressure deficit conditions inside the greenhouse for optimum yield. In this regard, an air-conditioning system could provide the required conditions in harsh climatic regions. In this study, the authors have summarized their published work on different desiccant and evaporative cooling options for greenhouse air-conditioning. The direct, indirect, and Maisotsenko cycle evaporative cooling systems, and multi-stage evaporative cooling systems have been summarized in this study. Different desiccant materials i.e., silica-gels, activated carbons (powder and fiber), polymer sorbents, and metal organic frameworks have also been summarized in this study along with different desiccant air-conditioning options. However, different high-performance zeolites and molecular sieves are extensively studied in literature. The authors conclude that solar operated desiccant based evaporative cooling systems could be an alternate option for next generation greenhouse air-conditioning.

Part of the book: Next-Generation Greenhouses for Food Security

By Seyed Moin-E-Ddin Rezvani, Redmond R. Shamshiri, Ibrahim A. Hameed, Hamid Zare Abyane, Mohsen Godarzi, Davood Momeni and Siva K. Balasundram

A greenhouse is a complex environment in which various biological and non-biological phenomena occur. For simulation and prediction of the climate and plant growth changes in the greenhouse are necessary to provide mathematical models. The dynamic greenhouse climate models are classified in mechanistic and black-box models (ARX). Climatic models are mainly obtained using energy balance or computational fluid dynamics. In the energy balance models, the greenhouse climatic variables are considered uniformity and homogeneity, but in the computational fluid dynamics, the heterogeneity of the greenhouse environment can be shown by 3D simulation. Crop growth simulation models are quantitative tools based on scientific principles and mathematical relationships that can evaluate the different effects of climate, soil, water, and crop management factors on crop growth and development. In this chapter, with a review of the basics of climate models in greenhouses, the results and application of some climate dynamics models based on the energy balance as well as simulations performed with computational fluid dynamics are reviewed. A review of greenhouse growth models and functional–structural plant models (FSPM) was also conducted.

Part of the book: Next-Generation Greenhouses for Food Security

By Mohammed El Idrissi, Omar El Beqqali, Jamal Riffi, Redmond R. Shamshiri, Sanaz Shafian and Ibrahim A. Hameed

Adopting new information and communication technology (ICT) as a solution to achieve food security becomes more urgent than before, particularly with the demographical explosion. In this survey, we analyze the literature in the last decade to examine the existing fog/edge computing architectures adapted for the smart farming domain and identify the most relevant challenges resulting from the integration of IoT and fog/edge computing platforms. On the other hand, we describe the status of Blockchain usage in intelligent farming as well as the most challenges this promising topic is facing. The relevant recommendations and researches needed in Blockchain topic to enhance intelligent farming sustainability are also highlighted. It is found through the examination that the adoption of ICT in the various farming processes helps to increase productivity with low efforts and costs. Several challenges are faced when implementing such solutions, they are mainly related to the technological development, energy consumption, and the complexity of the environments where the solutions are implemented. Despite these constraints, it is certain that shortly several farming businesses will heavily invest to introduce more intelligence into their management methods. Furthermore, the use of sophisticated deep learning and Blockchain algorithms may contribute to the resolution of many recent farming issues.

Part of the book: Digital Agriculture, Methods and Applications

By Redmond R. Shamshiri, Siva K. Balasundram, Abdullah Kaviani Rad, Muhammad Sultan and Ibrahim A. Hameed

Soil salinity and the water crisis are imposing significant challenges to more than 100 countries as dominant factors of agricultural productivity decline. Given the rising trend of climate change and the need to increase agricultural production, it is crucial to execute appropriate management strategies in farmlands to address salinity and water deficiencies. Ground-based soil moisture and salinity sensors, as well as remote sensing technologies in satellites and unmanned aerial vehicles, which can be used for large-scale soil mapping with high accuracy, play a pivotal role in precision agriculture as advantageous soil condition monitoring instruments. Several barriers, such as expensive rates and a lack of systematic networks, may hinder or even adversely impact the progression of agricultural digitalization. As a result, integrating proximal equipment with remote sensing and Internet of things (IoT) capabilities has been shown to be a promising approach to improving soil monitoring reliability and efficiency. This chapter is an attempt to describe the pros and cons of various soil sensors, with the objective of promoting IoT technology in digital agriculture and smart farming.

Part of the book: Digital Agriculture, Methods and Applications

By Seyed Moin-eddin Rezvani, Redmond R. Shamshiri, Jalal Javadi Moghaddam, Siva K. Balasundram and Ibrahim A. Hameed

Agriculture is constantly developing into a progressive sector by benefiting from a variety of high-tech solutions with the ultimate objectives of improving yield and quality, minimizing wastes and inputs, and maximizing the sustainability of the process. For the case of Iran, adaptation of digital agriculture is one of the key economic plans of the government until 2025. For this purpose, the development of infrastructure besides understanding social and cultural impacts on the transformation of traditional agriculture is necessary. This chapter reports the potential of the existing technological advances and the state of the current research efforts for the implementation of digital agriculture in open-field and closed-field crop production systems in Iran. The focus of the study was on the development of affordable IoT devices and their limitations for various farming applications including smart irrigations and crop monitoring, as well as an outlook for the use of robotics and drone technology by local farmers in Iran.

Part of the book: Digital Agriculture, Methods and Applications

By Mohammed El Idrissi, Redmond R. Shamshiri and Ibrahim A. Hameed

Crop health monitoring, as an intelligent farming activity, has become increasingly difficult and tedious for farmers due to the diversity in plant pathology. The performance of frequentist convolutional neural network (CNN) models is no longer sufficient to detect pertinent features in images and detect diseases early and efficiently. On the other hand, uncertainty in convolutional network inference is a big concern that can introduce more inexactitude in the predicted classes. In this context, we propose an intelligent farming application that aims to provide farmers with assistance in wheat crop health monitoring. The Bayesian inference with local reparameterization trick has been used to improve the sampling process during the learning phase. Thus, the uncertainty in the model and the output have been modeled to give an idea of the room for improvement. The classification skill of the proposed Bayesian uncertainty-based monitor can distinguish between wheat crops with no diseases and those infected with leaf and stem rust based on leaf and stem super-resolution image processing. The achieved accuracy is 96%, with a big resistance against overfitting/underfitting issues, and more reliability is obtained through the tolerance of the classification concept. The model is also optimized for real-time inference and adapted for resource-constrained devices.

Part of the book: Precision Agriculture

By Redmond R. Shamshiri, Maryam Behjati, Siva K. Balasundram, Christopher Teh Boon Sung, Ibrahim A. Hameed, Ahmad Kamil Zolkafli, An Ho-Song, Arina Mohd Noh, Badril Hisham Abu Bakar, W.A. Balogun, Beom-Sun Kang, Cong-Chuan Pham, Dang Khanh Linh Le, Dong Hee Noh, Dongseok Kim, Eliezel Habineza, Farizal Kamaroddin, Gookhwan Kim, Heetae Kim, Hyunjung Hwang, Jaesung Park, Jisu Song, Joonjea Sung, Jusnaini Muslimin, Ka Young Lee, Kayoung Lee, Keong Do Lee, Keshinro Kazeem Kolawole, Kyeong Il Park, Longsheng Fu, Md Ashrafuzzaman Gulandaz, Md Asrakul Haque, Md Nasim Reza, Md Razob Ali, Md Rejaul Karim, Md Sazzadul Kabir, Md Shaha Nur Kabir, Minho Song, Mohamad Shukri Zainal Abidin, Mohammad Ali, Mohd Aufa Md Bookeri, Mohd Nadzim Nordin, Mohd Nadzri Md Reba, Mohd Nizam Zubir, Mohd Saiful Azimi Mahmud, Mohd Taufik Ahmad, Muhammad Hariz Musa, Muhammad Sharul Azwan Ramli, Musa Mohd Mokji, Naoto Yoshimoto, Nhu Tuong An Nguyen, Nur Khalidah Zakaria, Prince Kumar, P.K. Garg, Ramlan Ismail, Ren Kondo, Ryuta Kojo, Samsuzzaman, Seokcheol Yu, Seok-Ho Park, Shahriar Ahmed, Siti Noor Aliah Baharom, Sumaiya Islam, Sun-Ok Chung, Ten Sen Teik, Tinah Manduna Mutabazi, Wei-Chih Lin, Yeon Jin Cho and Young Ho Kang

This chapter is a collection of selected abstracts presented at the 10th Asian-Australasian Conference on Precision Agriculture, held from October 24th to 26th in Putrajaya, Malaysia. It aims to emphasize the transformative potential of technology in precision agriculture and smart farming. The featured studies highlight the transformative impact of technology and current improvements in agriculture, offering modern solutions including machine learning, robotics, remote sensing, and geographic information systems (GIS). From autonomous navigation for mobile robots to stress classification in crop production systems, and from phenotypic analysis with LiDAR technology to real-time sensor monitoring in greenhouse agriculture, the majority of abstracts underline the integration of digital tools in different fields of farming with the core objective of reshaping conventional farming techniques and eliminating dependency on manual works. Key examples include the development of a distributed sensing system (DSS) used for orchard robots, stress classification for tomato seedlings through image-based color features and machine learning, and the integration of remote sensing and AI in crop protection. Other solutions, such as automated spraying robots for cherry tomato greenhouses, active back exoskeletons for rice farm lifting tasks, and advancements in seedling transplanting techniques, have shown promising results for contributing to sustainable farming practices by providing accurate and timely information for decision-making amid climate change-induced uncertainties.

Part of the book: Precision Agriculture