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Escobar Barrios, José R. Rangel Méndez, Nancy V. Pérez Aguilar, Guillermo Andrade Espinosa and José L. Dávila Rodríguez",authors:[{id:"12709",title:"Dr.",name:"Jose Rene",middleName:null,surname:"Rangel-Mendez",fullName:"Jose Rene Rangel-Mendez",slug:"jose-rene-rangel-mendez"},{id:"12711",title:"Dr.",name:"Vladimir Alonso",middleName:null,surname:"Escobar Barrios",fullName:"Vladimir Alonso Escobar Barrios",slug:"vladimir-alonso-escobar-barrios"},{id:"112164",title:"Dr",name:"Guillermo",middleName:null,surname:"Andrade-Espinosa",fullName:"Guillermo Andrade-Espinosa",slug:"guillermo-andrade-espinosa"},{id:"112165",title:"Dr.",name:"José Luis",middleName:null,surname:"Dávila-Rodríguez",fullName:"José Luis Dávila-Rodríguez",slug:"jose-luis-davila-rodriguez"},{id:"112167",title:"Dr.",name:"Nancy Verónica",middleName:null,surname:"Pérez-Aguilar",fullName:"Nancy Verónica Pérez-Aguilar",slug:"nancy-veronica-perez-aguilar"}]},{id:"36175",title:"Preparation and Characterization of PVDF/PMMA/Graphene Polymer Blend Nanocomposites by Using ATR-FTIR Technique",slug:"preparation-and-characterization-of-pvdf-pmma-graphene-polymer-blend-nanocomposites-by-using-ft-ir-t",signatures:"Somayeh Mohamadi",authors:[{id:"108556",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohamadi",fullName:"Somayeh Mohamadi",slug:"somayeh-mohamadi"}]},{id:"36176",title:"Reflectance IR Spectroscopy",slug:"fundamental-of-reflectance-ir-spectroscopy",signatures:"Zahra Monsef Khoshhesab",authors:[{id:"111629",title:"Dr.",name:"Zahra",middleName:null,surname:"Monsef Khoshhesab",fullName:"Zahra Monsef Khoshhesab",slug:"zahra-monsef-khoshhesab"}]},{id:"36177",title:"Evaluation of Graft Copolymerization of Acrylic Monomers Onto Natural Polymers by Means Infrared Spectroscopy",slug:"evaluation-of-graft-copolymerization-of-acrylic-monomers-onto-natural-polymers-by-means-infrared-spe",signatures:"José Luis Rivera-Armenta, Cynthia Graciela Flores-Hernández, Ruth Zurisadai Del Angel-Aldana, Ana María Mendoza-Martínez, Carlos Velasco-Santos and Ana Laura Martínez-Hernández",authors:[{id:"37761",title:"Prof.",name:"Ana Laura",middleName:null,surname:"Martinez-Hernandez",fullName:"Ana Laura Martinez-Hernandez",slug:"ana-laura-martinez-hernandez"},{id:"107855",title:"Dr.",name:"Jose Luis",middleName:null,surname:"Rivera Armenta",fullName:"Jose Luis Rivera Armenta",slug:"jose-luis-rivera-armenta"},{id:"108894",title:"MSc.",name:"Cynthia Graciela",middleName:null,surname:"Flores-Hernández",fullName:"Cynthia Graciela Flores-Hernández",slug:"cynthia-graciela-flores-hernandez"},{id:"108896",title:"MSc.",name:"Ruth Zurisadai",middleName:null,surname:"Del Angel Aldana",fullName:"Ruth Zurisadai Del Angel Aldana",slug:"ruth-zurisadai-del-angel-aldana"},{id:"108898",title:"Dr.",name:"Carlos",middleName:null,surname:"Velasco-Santos",fullName:"Carlos Velasco-Santos",slug:"carlos-velasco-santos"},{id:"108905",title:"Dr.",name:"Ana Maria",middleName:null,surname:"Mendoza-Martínez",fullName:"Ana Maria Mendoza-Martínez",slug:"ana-maria-mendoza-martinez"}]},{id:"36178",title:"Applications of FTIR on Epoxy Resins - Identification, Monitoring the Curing Process, Phase Separation and Water Uptake",slug:"applications-of-ftir-on-epoxy-resins-identification-monitoring-the-curing-process-phase-separatio",signatures:"María González González, Juan Carlos Cabanelas and Juan Baselga",authors:[{id:"107857",title:"Prof.",name:"Juan",middleName:null,surname:"Baselga",fullName:"Juan Baselga",slug:"juan-baselga"},{id:"138113",title:"Dr.",name:"María",middleName:null,surname:"González",fullName:"María González",slug:"maria-gonzalez"},{id:"138114",title:"Dr.",name:"Juan C.",middleName:null,surname:"Cabanelas",fullName:"Juan C. Cabanelas",slug:"juan-c.-cabanelas"}]},{id:"36179",title:"Use of FTIR Analysis to Control the Self-Healing Functionality of Epoxy Resins",slug:"use-of-ft-ir-analysis-to-control-the-self-healing-functionality-of-epoxy-resins",signatures:"Liberata Guadagno and Marialuigia Raimondo",authors:[{id:"106836",title:"Prof.",name:"Liberata",middleName:null,surname:"Guadagno",fullName:"Liberata Guadagno",slug:"liberata-guadagno"}]},{id:"36180",title:"Infrared Analysis of Electrostatic Layer-By-Layer Polymer Membranes Having Characteristics of Heavy Metal Ion Desalination",slug:"infrared-analysis-of-electrostatic-layer-by-layer-polymer-membranes-having-characteristics-of-heavy",signatures:"Weimin Zhou, Huitan Fu and Takaomi Kobayashi",authors:[{id:"110384",title:"Dr.",name:"Takaomi",middleName:null,surname:"Kobayashi",fullName:"Takaomi Kobayashi",slug:"takaomi-kobayashi"}]},{id:"36181",title:"Infrared Spectroscopy as a Tool to Monitor Radiation Curing",slug:"infrared-spectroscopy-as-a-tool-to-monitor-radiation-curing",signatures:"Marco Sangermano, Patrick Meier and Spiros Tzavalas",authors:[{id:"112286",title:"Dr.",name:"Spiros",middleName:null,surname:"Tzavalas",fullName:"Spiros Tzavalas",slug:"spiros-tzavalas"},{id:"114382",title:"Prof.",name:"Marco",middleName:null,surname:"Sangermano",fullName:"Marco Sangermano",slug:"marco-sangermano"},{id:"114384",title:"Dr",name:"Patrick",middleName:null,surname:"Meier",fullName:"Patrick Meier",slug:"patrick-meier"}]},{id:"36182",title:"Characterization of Compositional Gradient Structure of Polymeric Materials by FTIR Technology",slug:"characterization-of-compositional-gradient-structure-of-polymeric-materials-by-ft-ir-technology",signatures:"Alata Hexig and Bayar Hexig",authors:[{id:"20867",title:"Dr.",name:"Bayar",middleName:null,surname:"Hexig",fullName:"Bayar Hexig",slug:"bayar-hexig"},{id:"111986",title:"Dr.",name:"Alata",middleName:null,surname:"Hexig",fullName:"Alata Hexig",slug:"alata-hexig"}]},{id:"36183",title:"Fourier Transform Infrared Spectroscopy - Useful Analytical Tool for Non-Destructive Analysis",slug:"fourier-trasform-infrared-spectroscopy-useful-analytical-tool-for-non-destructive-analysis",signatures:"Simona-Carmen Litescu, Eugenia D. Teodor, Georgiana-Ileana Truica, Andreia Tache and Gabriel-Lucian Radu",authors:[{id:"24425",title:"Dr.",name:"Simona Carmen",middleName:null,surname:"Litescu",fullName:"Simona Carmen Litescu",slug:"simona-carmen-litescu"},{id:"24429",title:"Prof.",name:"Gabriel-Lucian",middleName:null,surname:"Radu",fullName:"Gabriel-Lucian Radu",slug:"gabriel-lucian-radu"},{id:"108318",title:"Dr.",name:"Eugenia D.",middleName:null,surname:"Teodor",fullName:"Eugenia D. Teodor",slug:"eugenia-d.-teodor"},{id:"108323",title:"Dr.",name:"Georgiana-Ileana",middleName:null,surname:"Badea",fullName:"Georgiana-Ileana Badea",slug:"georgiana-ileana-badea"},{id:"136337",title:"Ms.",name:"Andreia",middleName:null,surname:"Tache",fullName:"Andreia Tache",slug:"andreia-tache"}]},{id:"36184",title:"Infrared Spectroscopy in the Analysis of Building and Construction Materials",slug:"infrared-spectroscopy-of-cementitious-materials",signatures:"Lucia Fernández-Carrasco, D. Torrens-Martín, L.M. Morales and Sagrario Martínez-Ramírez",authors:[{id:"107401",title:"Dr.",name:"Lucia J",middleName:null,surname:"Fernández",fullName:"Lucia J Fernández",slug:"lucia-j-fernandez"}]},{id:"36185",title:"Infrared Spectroscopy Techniques in the Characterization of SOFC Functional Ceramics",slug:"infrared-spectroscopy-techniques-in-the-characterization-of-sofc-functional-ceramics",signatures:"Daniel A. Macedo, Moisés R. Cesário, Graziele L. Souza, Beatriz Cela, Carlos A. Paskocimas, Antonio E. Martinelli, Dulce M. A. Melo and Rubens M. Nascimento",authors:[{id:"102015",title:"MSc.",name:"Daniel",middleName:null,surname:"Macedo",fullName:"Daniel Macedo",slug:"daniel-macedo"},{id:"112309",title:"MSc",name:"Moisés",middleName:"Romolos",surname:"Cesário",fullName:"Moisés Cesário",slug:"moises-cesario"},{id:"112310",title:"Ms.",name:"Graziele",middleName:null,surname:"Souza",fullName:"Graziele Souza",slug:"graziele-souza"},{id:"112311",title:"MSc.",name:"Beatriz",middleName:null,surname:"Cela",fullName:"Beatriz Cela",slug:"beatriz-cela"},{id:"112312",title:"Prof.",name:"Carlos",middleName:null,surname:"Paskocimas",fullName:"Carlos Paskocimas",slug:"carlos-paskocimas"},{id:"112314",title:"Prof.",name:"Antonio",middleName:null,surname:"Martinelli",fullName:"Antonio Martinelli",slug:"antonio-martinelli"},{id:"112315",title:"Prof.",name:"Dulce",middleName:null,surname:"Melo",fullName:"Dulce Melo",slug:"dulce-melo"},{id:"112316",title:"Dr.",name:"Rubens",middleName:"Maribondo Do",surname:"Nascimento",fullName:"Rubens Nascimento",slug:"rubens-nascimento"}]},{id:"36186",title:"Infrared Spectroscopy of Functionalized Magnetic Nanoparticles",slug:"infrared-spectroscopy-of-functionalized-magnetic-nanoparticles",signatures:"Perla E. García Casillas, Claudia A. Rodriguez Gonzalez and Carlos A. Martínez Pérez",authors:[{id:"104636",title:"Dr.",name:"Perla E.",middleName:null,surname:"García Casillas",fullName:"Perla E. García Casillas",slug:"perla-e.-garcia-casillas"},{id:"112440",title:"Dr.",name:"Carlos A.",middleName:null,surname:"Martínez Pérez",fullName:"Carlos A. Martínez Pérez",slug:"carlos-a.-martinez-perez"},{id:"112441",title:"Dr.",name:"Claudia A.",middleName:null,surname:"Rodriguez Gonzalez",fullName:"Claudia A. Rodriguez Gonzalez",slug:"claudia-a.-rodriguez-gonzalez"}]},{id:"36187",title:"Determination of Adsorption Characteristics of Volatile Organic Compounds Using Gas Phase FTIR Spectroscopy Flow Analysis",slug:"determination-of-adsorption-characteristics-of-volatile-organic-compounds-using-gas-phase-ftir-spect",signatures:"Tarik Chafik",authors:[{id:"107310",title:"Prof.",name:"Tarik",middleName:null,surname:"Chafik",fullName:"Tarik Chafik",slug:"tarik-chafik"}]},{id:"36188",title:"Identification of Rocket Motor Characteristics from Infrared Emission Spectra",slug:"identification-of-rocket-motor-characteristics-from-infrared-emission-spectra",signatures:"N. Hamp, J.H. Knoetze, C. Aldrich and C. Marais",authors:[{id:"112229",title:"Prof.",name:"Chris",middleName:null,surname:"Aldrich",fullName:"Chris Aldrich",slug:"chris-aldrich"},{id:"112232",title:"Prof.",name:"Hansie",middleName:null,surname:"Knoetze",fullName:"Hansie Knoetze",slug:"hansie-knoetze"},{id:"135327",title:"Ms.",name:"Corne",middleName:null,surname:"Marais",fullName:"Corne Marais",slug:"corne-marais"}]},{id:"36189",title:"Optical Technologies for Determination of Pesticide Residue",slug:"optical-technology-for-determination-of-pesticide-residue",signatures:"Yankun Peng, Yongyu Li and Jingjing Chen",authors:[{id:"113343",title:"Prof.",name:"Yankun",middleName:null,surname:"Peng",fullName:"Yankun Peng",slug:"yankun-peng"},{id:"116636",title:"Dr.",name:"Yongyu",middleName:null,surname:"Li",fullName:"Yongyu Li",slug:"yongyu-li"},{id:"116637",title:"Dr.",name:"Jingjing",middleName:null,surname:"Chen",fullName:"Jingjing Chen",slug:"jingjing-chen"}]},{id:"36190",title:"High Resolution Far Infrared Spectra of the Semiconductor Alloys Obtained Using the Synchrotron Radiation as Source",slug:"high-resolution-spectra-of-semiconductor-s-alloys-obtained-using-the-far-infrared-synchrotron-radi",signatures:"E.M. Sheregii",authors:[{id:"102655",title:"Prof.",name:"Eugen",middleName:null,surname:"Sheregii",fullName:"Eugen Sheregii",slug:"eugen-sheregii"}]},{id:"36191",title:"Effective Reaction Monitoring of Intermediates by ATR-IR Spectroscopy Utilizing Fibre Optic Probes",slug:"effective-reaction-monitoring-of-intermediates-by-atr-ir-spectroscopy-utilizing-fibre-optic-probes",signatures:"Daniel Lumpi and Christian Braunshier",authors:[{id:"109019",title:"Dr.",name:"Christian",middleName:null,surname:"Braunshier",fullName:"Christian Braunshier",slug:"christian-braunshier"},{id:"111798",title:"MSc.",name:"Daniel",middleName:null,surname:"Lumpi",fullName:"Daniel Lumpi",slug:"daniel-lumpi"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"63478",title:"Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber",doi:"10.5772/intechopen.80935",slug:"development-of-a-field-robot-platform-for-mechanical-weed-control-in-greenhouse-cultivation-of-cucum",body:'The demand for off-season cultivation of fruits and vegetables require different aspects of automation and robotics in closed-field plant production environments like greenhouses [1]. Modern greenhouse bioproduction systems are required to exhibit integration of automation, biological culture practices, and control systems through the concept of Automation-Culture-Environment-oriented SYStems analysis (ACESYS) as defined in [2, 3]. The growth condition for Solanaceae vegetables in the greenhouse provides the leeway for the growth of other plants as well. In greenhouse cultivation of Cucumber (
The use of labor force that manually pulls out the weeds is still practiced by local growers. This is, however, not an efficient method since the availability of the skilled workforce that accepts repetitive tasks in the harsh greenhouse and field conditions impose uncertainties and timeliness costs [13]. It is, therefore, necessary to select a proper method for effective weed control. The trends in the agricultural robotics in the past 10 years show that automation of plant trimming with simultaneous localization and mapping techniques will change the industry in future [14]. The available time, labor, equipment, costs, and types of weeds and the areas infested need to be considered when planning a weed control program. In this regard, agricultural robotic and automation technology plays an essential role in improving the interactions between human, machine, and plants [15]. For example, the prevention of musculoskeletal disorders in manual harvesting operations in Dutch greenhouses has motivated various researchers for replacement of human labor by automatons robot for picking cucumber [16] and sweet pepper [13] fruits. Automation is a viable and sometimes necessary method to ensure maximum profits with minimum costs. In fact, one of the main purposes of agricultural automation has been always concerned with the substitution of human workforce by robots or mechanized systems that can handle the tasks more accurately and uniformly at a lower cost and higher efficiency [17, 18, 19, 20, 21, 22].
Research and development in agricultural robotics date back to 1980s, with Japan, the Netherlands, and the USA as the pioneer countries. Example of such research works included the works of [7, 23] for robotic weed control and automated harvesting of tomato. Development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. Such vision system should be able to recognize the accurate position of the plant stem and protects it during the weed control [24]. A near-ground image capturing and processing technique to detect broad-leaved weeds in cereal crops under actual field conditions has been reported in the work of [25]. Here, the researchers proposed a method that uses color information to discriminate between vegetation and background, while shape analysis techniques were applied to distinguish between crop and weeds. Shape features of the radish plant and weed were investigated by [26]. They proposed a machine vision system using a charge coupled device camera for the weed detection in a radish farm resulting 92% success rate of recognition for radish and 98% for weeds.
A combined method of color and shape features for sugar beet weed segmentation was proposed by [27] with 92% success rate in classification. This rate increased to 96% by adding two shape features. Another approach extracted a correlation between the three main color components R, G and B, which constitute weeds and sugar beet color classes by means of discriminant analysis [28]. Their method resulted in different classification success rates between 77 and 98%. The segmentation of weeds and soybean seedlings by CCD images in the field was studied by [29]. Texture features of weed species have been applied for distinguishing weed species by [30] with grass and broadleaf classification accuracies of 93 and 85%, respectively. Textural image analysis was used to detect weeds in the grass [31]. Gabor wavelet features of NIR images of apples were extracted for quality inspection and used as input to kernel PCA [32]. Kernel PCA first maps the nonlinear features to linear space, and then, PCA is applied to separate the image Gabor wavelet (5 scales and 8 orientations) combined with kernel PCA that had the highest recognition rate (90.5%). Improvements in vision-based control system [13, 33, 34, 35, 36] have enabled several applications of robotic manipulators for greenhouse and orchard tasks and have contributed to the decrease in workload and labor’s fatigue, while improving the efficiency and safety of the operations. These achievements were considered a challenge in the earlier agricultural robotics works [23, 37, 38]. For example, spray equipment for weed control has been developed with vertical spray booms that increase the deposition in the canopy [39, 40, 41]. Some of these alternatives are self-propelled vehicles such as Fumimatic® (IDM S.L, Almería, Spain) and Tizona (Carretillas Amate S.L., Almería, Spain), or autonomous vehicles such as Fitorobot (Universidad de Almería, Cadia S.L., Almería, Spain) that have been designed specifically to move without difficulty over loose soils and in spaces with a large number of obstacles [41]. These vehicles rely on inductive sensors to follow metal pipes buried in the soil. Few studies have addressed the navigation problem of vehicles in greenhouses operating completely autonomously [9, 11, 15]. The main challenge of these systems is that localization approaches needed for feeding the closed-loop controllers would lead to inaccurate measurements after a few steps fail for long trajectories [42]. A stereovision system along with an image processing algorithm was used to recognize the weeds and also to estimate their location in the field. In order to experiment with vision sensors and agricultural robots, [13] created a completely simulated environment in V-REP, ROS, and MATLAB for improvement of plant/fruit scanning and visual servoing task through an easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. In another study, [43] designed a field survey mobile robot platform based for navigating inside greenhouses and open-field cultivation for automated image acquisition. A functional model shown in Figure 1 was introduced by [44] in the field test of an autonomous robot for deleafing cucumber plants grown in a high-wire cultivation system. This model was also adapted and used by [13] for the robotic harvesting of sweet pepper and on a greenhouse field survey mobile platform [43]. Artificial neural networks have also been used by many researchers to discriminate weeds [45, 46] with machine vision as shown in Figure 2. A fixed-position weed robot was presented by [47], which is interfaced to a standard belt-conveyor displacement system and provides the robot with pallets containing the crops. These reviews indicate that a commercial robotic platform for the elimination of weeds in a cucumber greenhouse has not been materialized yet. In addition, most of the research works in the area of robotic weed control are applicable prior to the plant growth or in some cases when the main plant height is between 0.2 and 0.3 m.
Task sequence during leaf picking of cucumber, adapted from [
Mechatronic paradigm followed in this research, adapted from [
The overall objective of this study was to design and develop an affordable robotic weed control system for application in greenhouse cultivation of cucumbers where plants can reach to a height of 10 m. Our design is based on mechanical weed removal techniques without using chemical materials. The specific objectives were to determine (i) the best blade design for cutting the weeds among cultivation rows, (ii) the best blade rotation (BR) speed, and (iii) the best arm motor (AM) speed.
A flowchart of the methodology is shown in Figure 3. A prototype robot was designed using AutoCAD software 2011 v18.1 (Autodesk Inc., San Rafael, CA, USA). Schematic views of the prototype robot, as well as the corresponding dimensions and parts are shown and illustrated in Figures 4 and 5. The main mechanical components of the robotic platform consist of a monorail, main chassis, ball bearings, wheels, arms, blade, and adjusting mechanism. Major electrical components include DC motors, microswitches, a 12 V 7.2–9 amp sealed lead acid battery, SRF05 ultrasonic sensors, pic 18F4550 microcontroller, and 2 × 24 LCD monitor (Figure 6). We began with the design of a monorail that was responsible to support the robot navigations and stops between two cucumber rows inside the greenhouse. The monorail has a width of 0.06 m and was placed 0.4 m above the ground (Figure 5A and B). The algorithm for robot navigation between two consecutive stops points on the monorail is also illustrated in the flowchart of Figure 5. Right after the robot is switched on, it starts moving on the monorail that is fixed along the greenhouse from one row to another. Upon reaching the first stopper point on the rail, the robot strikes the first microswitch, which sends a deactivation signal to the first motor responsible for moving the robot. While stopped between two cucumber plant rows, the robot scans for weeds and determines the distance between the detected weed and the blade arm using the ultrasonic sensors. Subsequently, a command signal is sent to the arm motor and blade motor for activating the blade rotation as illustrated in the flowchart of Figure 5.
Flowchart of the research methodology.
The CAD model design of the weed control robot.
The mechanism and flowchart for the robot navigation and control on the monorail showing (A) the monorail layout, (B) the robot mounted on the monorail, (C) the robot main body and manipulator arm, and (D) the joint setup between the robot and the monorail.
Major electrical module and wiring connections of the weed control robot.
The moving mechanical arm consists of a chassis, a small arm, and the main arm. Two main criteria were considered in designing the robot frame including minimum weight (for increasing the motor efficiency), and strength (for standing vibrations). The frame was made from an iron band bearing with the dimensions of 0.02 × 0.18 × 0.005 m. In order to provide support for the battery, bearing bases, microswitches, and the main arm, we installed additional extensions to the frame in a way that the robot gravity center is placed on the monorail. The battery is the heaviest part of the robot and can power the robot for 2 h. It was installed on the central frame above the rails and wheels. The battery weight creates stability for the robot when the main arm is outstretched, and this weight and location for the battery can hold the spinning wheel implemented in place. We placed several holes on the frame to facilitate the installation of the motor, wheels, and the required electrical fragments (Figure 5C and D). The robot makes use of four ball bearings of diameter 0.02 m, out of which three were used to hold the robot to the rail and to facilitate a smooth movement (two bearings were placed on the right and one on the left side). The fourth bearing was used to act as the second wheel for the robot. All the ball bearings have a diameter of 0.02 m and are installed on the central frame. The diameter of the robot main wheel is 0.04 m, and the ideal speed was determined using trial and errors and time-motion studies during the conducted tests. The arm frame is made of an iron band bearing with a dimension of 0.02 × 0.2 × 0.005 m. A blade was installed on the main arm that moves forward and enables robot access to the weeds between the main plants. A shank protector in one of the holes in the arm frame makes the movement and the selection of the angle for smooth cutting.
Major electronic components of the robot are three sets of SRF05 ultrasonic sensors, a PIC18F4550 microcontroller, and a 2 × 24 LCD monitor (Figure 6). The ultrasonic sensors were placed in a row having 0.10 m distance from each other. The sensors are specially positioned in a way that they cover the space between two cucumber plants on the cultivation row. As mentioned earlier, upon receiving a signal indicating weed existence, the microcontroller program determines the distance between the weed and the sensors and whether the weed is on the left, right, or middle of the sensors. This signal activates the cutting mechanism. Finally, the information of the entire process, including the distance between weed and sensors, and the specific sensor that identified the weed are shown on the robot LCD. During the experimental phase, we considered several improvements and adjustment on the sensing part and corresponding microcontroller program. For example, we used a tube pipe cover for each of the ultrasonic sensors to change the circular waves to linear waves. This was necessary because sound waves that broadcast from transmitters of ultrasonic sensors are circular. When these sensors are close to the ground, the broadcasting waves that bounce off from the ground are misinterpreted as weeds.
The robot movements are supported by three 12 V, 0.89 A DC motors that are labeled for this paper by motor 1, 2, and 3. The first motor was fixed directly to the wheels in front of the robot and was responsible for the robot movement on the monorail. To select the optimum speed for the robot, six motor speeds of 30, 40, 50, 60, 80, and 120 rpm were tested. We found that the motor with 60 rpm, 1.358 N·m torque, 12 V, 0.89 A had the best performance in the greenhouse under study. The second motor was connected to the small arm and is responsible to rotate the big arm that moves the blade of the robot at a selected speed of 10 rpm and torque of 8.15 N·m. The third motor was fixed to the frame of the main arm for rotating the blade at a high speed of 3500 rpm for efficient weed cutting and removal. This frame can move up and down and can fix the distance between the blade and the ground level. It should be noted that the 3500 rpm blade rotation speed and the 10 rpm arm motor speed were found from the experiments.
Three types of blade, namely the S-shaped, the triangle-shaped, and the circular-shaped blade (Figure 7) were initially considered in the weed cutting experiments. We conducted several tests to find the best blade width (equal to 0.1 m) for matching the 0.4 m distance between two cucumber plants. Based on our field tests, we found that the S-shaped blade was the most efficient design for the purpose of weed cutting. The blade was built from double stainless steel material to resist the corrosion in high humidity greenhouse environment. Analysis and calculations were carried out for finding the blade tip speed and corresponding vector components according to the formulations given in [48]. The corresponding diagrams of this analysis are shown schematically in Figure 7. It can be observed from Figure 7A that the direction of the tip of the blade follows a cycloid curve on the ground level. The component of blade speed in the direction of robot forward speed vector, as well as the demonstration of vector gradient in the blade speeds, is shown in Figure 7B–D. Here, WB is the circular speed of the blade (rad/s),
Design of the cutting blade, (A): calculating the velocity of rotating blade in stickles, (B): component of blade speed in direction of moving U, (C, D): calculating U and V, (E): components of forces, and (F): vector demonstration of the blade speed. Adapted from [
The weed control robot was tested in a 5000 m2 greenhouse in Jiroft city (28°40′41″N 57°44′26″E) located to the south of Kerman province of Iran (Figure 8). We planted over 10,000 cucumber seeds in pots and placed them in the greenhouse with spaces between the two plants being 0.4 m. It should be noted that in order to manually remove the weeds from 1 ha of the greenhouse under study, four seasonal workers had to perform the task every day, for 8 months (equivalent to 832 man/hour). Three experiments were conducted at different growth stages as follows: (i) during the seedling and germination stage, 15 days after the crop was cultivated and the surrounding weeds were also 15 days old (these weeds usually have thin and very flexible stalks and are 10 cm high), (ii) during the vegetation and early fruiting stage, when the cucumber plants were 2 months old, and (iii) during the mature fruiting stage, when the plants were at their mature height. Three types of blades were selected, namely the S-shaped, triangular-shaped, and circular-shaped blade. For each blade, we assigned three blade rotation (BR) speeds of BR1 = 3500, BR2 = 2500, and BR3 = 1500 rpm with two arm motor (AM) speed of AM1 = 10 and AM2 = 30 rpm. A factorial design with two-way analysis of variance (ANOVA) was used to determine variation effects in the cutting weed performance of each blade due to BR speed, AM speed, and their interaction. For the kth blade type, under the ith level of blade speed and the jth level of arm speed factor, the two-way ANOVA model was stated as
Outside and inside views of the experimental site (top), and corresponding factorial design of experiment (bottom) for determining the best combination of blade type, blade speed, and arm speed.
Results of statistical analysis are summarized in Tables 1–3 showing that the effects of blade type (T), blade rotation (BR) speed, and arm motor (AM) speed are significant at the 0.05 level. Moreover, it was found that the S-shaped blade with a mean (μ) of 67.8% and standard error (σ) of 3.052% had the highest effect, and triangular-shaped blade with μ = 61.38% and σ = 3.083% had the lowest effect on the percentage of the weeds cut (PWC). The BR factor was significant at P < 0.05, indicating that blade rotation speed of 3500 rpm with μ = 78.23% and σ = 1.71% had the highest effect and the 1500 rpm with μ = 50.39 and σ = 1.86% had the lowest effect. The AM speed factor was also found to be significant at P < 0.05, which indicates that the speed of 10 rpm with μ = 69.1% and σ = 2.45% had the highest effect and the speed of 30 rpm with μ = 59.8% and σ = 2.46% has had the lowest effect on the PWC. It was found that (Table 1) different blade shapes with the AM speed of 10 rpm had a significant effect on the PWC. While the mean PWC by the S-shaped blades was the highest, increasing AM speed to 30 rpm reduced the efficiency of the S-shaped blade (as well as with the other two blades), resulting a mean PWC of 59.39%. According to the P-values in Table 2, while all of the main effects of blade type, BR, and AM speeds are significant at 0.05 level, their interactions were not found to have a significant effect on the PWC. The results provided in Table 3 show that the difference between the two blades, S-shape and triangular shape is significant at the 0.05 level. In other words, the mean of weeds cut by these two blades are significantly different, and according to the mean differences column, the mean of the PWC by the S-shape blade is larger than the PWC by the triangular-shaped blade. The mean difference between the S-shaped and the circular-shaped blade with P-value of 0.036 is also significant at the 0.05 level. This implies that the average PWC by these two blades are significantly different, and according to the mean differences column, the mean PWC by the S-shaped blade is larger than the mean of the PWC by the circular-shaped blade. It was found that the difference between the means of the triangular-shaped blade and circular-shaped blade with the P-value of 0.076 is not significant at the 0.05 level, that is, the mean of the PWC by these two blade types are not significantly different.
Blade type | μ: Mean percentage of weeds cut (%) | σ: Std. error (%) |
---|---|---|
A: S shaped | 67.8 | 3.05 |
B: Triangular shape | 61.38 | 3.08 |
C: Circular shape | 64.3 | 3.38 |
Blade rotation speed (rpm) | ||
1500 | 50.3 | 1.86 |
2500 | 64.9 | 1.51 |
3500 | 78.2 | 1.71 |
Arm motor speed (rpm) | ||
10 | 69.148 | 2.457 |
30 | 59.88 | 2.461 |
Factor effects on the percentage of weeds cut.
Model | Sum of squares | Mean sum of squares | P-value |
---|---|---|---|
Blade type (T) | 110.3 | 110.3 | 0.0462 |
Blade rotation speed (BR) | 6977.1 | 3488.6 | 0.000 |
Arm motor speed (AM) | 1157.4 | 1157.4 | 0.000 |
Error | 1264.6 | 26.3 | |
Interaction types | P-value | ||
T × BR | 114.1 | 57.1 | 0.1272 |
T × AM | 5.4 | 5.4 | 0.6493 |
BR × AM | 73.6 | 36.8 | 0.2560 |
T × BR × AM | 16 | 8 | 0.7356 |
Variance analysis and effects of the robot blade type (T), blade rotation (BR) speed, and arm motor (AM) speed on the percentage of weed cutting performance.
Mean differences | P-value | |
---|---|---|
Blade type | ||
A-B | 6.4444 | 0.000 |
A-C | 3.5000 | 0.036 |
B-C | −2.9444 | 0.076 |
Blade rotation (rpm) | ||
BR1-BR2 | −14.5556 | 0.000 |
BR1-BR3 | −27.8333 | 0.000 |
BR2-BR3 | −13.2778 | 0.000 |
Comparison of significant difference between blade types (A: S-shape, B: Triangular shape, and C: Circular shape), and blades rotation speed (BR1: 1500, BR2: 2500, and BR3: 3500 rpm).
Results of analysis of variance also showed that the mean differences between the BR speeds are significant, indicating that the resulted PWC with BR1 = 1500, BR2 = 2500, and BR3 = 3500 rpm are not equal. More specifically, the PWC in 1500 rpm was found to be smaller than those of 2500 and 3500 rpm. In addition, the mean PWC in 2500 rpm was also smaller than that of 3500 rpm. This can also be observed from the bar plots of Figure 9, showing that the mean PWC in 1500 rpm is the smallest (59.39%) and that of 3500 rpm was the largest (78.23%). The bar plots in Figure 10 illustrate descriptive statistics and frequency of the PWC for the experiments with the robot using all factors (blade types A, B, C, blade rotation speeds of 1500, 2500, 3500 rpm, and arm motor speed of 10 and 30 rpm). It can be seen from Figure 10 that the average PWC by the blades was significantly different. Consequently, the highest PWC cut was related to S-shaped at the blade rotation speed of 3500 rpm. In each motor arm speed, the increase in the rotational blade speed caused an increase in the PWC. In each rotational blade speed, if the motor arm speed increases, the PWC cut will decrease. Comparing the interactions between the three different types of blades, blade speed, and the speed of the arm the following results was obtained: the highest PWC in the entire experiment was 95%, which was obtained when the S-shaped blade at the rotational speed of 3500 rpm was used and motor speed was 10 rpm. The lowest PWC was 45%, which was obtained when the blade speed was 1500 rpm, AM speed was 30 rpm, and the blade type was triangular in shape. The analysis of the interaction of the BR speed and blade type showed that (i) none of the mutual interactions was significant in the variance test, (ii) t-test showed that if the rotational speed of the blade is low, the blade type will have a significant effect on the PWC, and (iii) for all the blade types, the highest PWC cut was at BR speed of 3500 rpm.
Comparison of the effects of various blade types on (left) and various blade rotation speeds (right) on the percentage of weeds cut.
Bar plots describing percentage of weeds cut with different blade type, blade rotation speed, and robot arm speed.
In this study, we designed, developed, and fabricated a prototype robot for mechanical weed control in greenhouse cultivation of cucumber. Automatic weed cutting experiments that were carried using the robot consist of ultrasonic sensor, which senses the existence of weeds between the cucumber plants. The robot then moves between cucumber rows on a monorail in the greenhouse, with an arm that moves the blade between the plants for cutting the detected weeds. The entire process of weed detection, moving the arm and blades, and weeds cutting is carried out in 10 s. Among the three blade types tested (S-, triangular-, and circular shapes), it was concluded that the S-shape was the most efficient design. For the best blade rotation (BR) and arm motor (AM) speeds, it was concluded that as the AM speed increased, the percentage of weeds cut (PWC) reduces; therefore, the motor with 10 rpm, 8.15 N·m torque, 12 V, and 0.89 A was selected to for moving the arm. The average weeds cut at 10 and 30 rpm was 69.1 and 58.9%, respectively. Finally, it was concluded that the best robot performance corresponding to the highest percentage of weeds cut was achieved with the S-shaped blade when the BR speed was 3500 rpm, and the AM speed was 10 rpm.
The authors declare no conflict of interest.
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