Design and development of eggplant harvester for gantry system

This paper describes the design and development of harvesting system for the gantry system to harvest eggplants. For this purpose, the harvesting robot was successfully designed and fabricated for the gantry system to harvest eggplants. The operation of the harvester was controlled by Programmable Logic Controller (PLC). Basically, the limit switches, DC motor, and relay are connected to the PLC. Meanwhile, a PLC ladder diagram was designed and developed to control the operation of the eggplant harvester. A visual basic programme was developed to interface the harvester with a greenhouse gantry control system. A videogrammetry method was employed to calculate the distance between the stems of eggplants and the cutter of robot end effector. The end effector used electric as its power source and it was controlled via Programmable Logic Controller (PLC). Visual Basic Programme was developed to interface the harvester with the gantry control system. The accuracy of the videogrammetry was tested to be 67.2% for X-axis, 88.2% for Y-axis and 84.7% for Z-axis. Meanwhile, the speed of the end effector for harvester is 2.4 km/h and it could lift up to 55 cm. In order to determine detachment force of eggplant, 16 samples of mature eggplants were tested in a greenhouse, and as a result, more than 22.76 N force was needed to detach a mature eggplant inside the gantry system

A Machine Vision Algorithm Combining Adaptive Segmentation and Shape Analysis for Orange Fruit Detection

 Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The two major challenges in the automation of harvesting are the recognition of the fruit and its detachment from the tree. This paper deals with fruit recognition and it presents the development of a machine vision algorithm for the recognition of orange fruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction and perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by using the red chromaticity coefficient which enabled adaptive segmentation under variable outdoor illumination. The algorithm also included detection of fruits which are in clusters by using shape analysis techniques. Evaluation of the algorithm included images taken inside the canopy (varying lighting condition) and on the canopy surface. Results showed that more than 90% of the fruits visually recognized in the images were detected in the 110 images tested with a false detection rate of 4%. The proposed segmentation was able to deal with varying lighting condition and the perimeter-based detection method proved to be effective in detecting fruits in clusters. The development of this algorithm with its capability of detecting fruits in varying lighting condition and occlusion would enhance the overall performance of robotic fruit harvesting

Development of a Neural Network-Based Camera for Tomato Harvesting Robots

Automated tomato harvesting robots were rapidly developed recently. Most of the designs were more focused on positioning of the end of robotic arm by using various methods such as combination of the sensor and vision system. This project concentrated on the artificial intelligent via the Neural Network, in order to provide a better decision making system for tomato harvesting robot. The objective of this study was to develop 3 degree of freedom tomato harvesting robotic system complete with gripper and motion program. The development of software for tomato pattern identification, determination of the X and Y coordinates from web camera captured and the determination of the tomato and tomato ripeness using decision making from Neural Network also become the main objective. The approach is to detect the desired object using vision system attached to the cylindrical automation system and perform image analysis. These features will serve as inputs to a neural net, which will be trained with a set of predetermined ripe tomato. The output is a command for harvester arm to make the movement for harvesting. The position determination was done with a conversion of the distance in pixel into a distance in metric unit (mm) of the tomato image. Whereas the depth of the tomato distance (z direction) was done by moving the actuator system towards the calculated tomato position until the object sensor senses the present of the tomato. AWIsoft07 software was developed to view the harvester vision, display the captured image analysis on the harvester vision, and display the numerical analysis output and neural network output. The harvester system with 3 degree of freedoms (3DOF) equips with specially designed tomato gripper named as AWI2007 Tomato Harvesting Robot was developed in order to realize the data from the AWISoft07 developed software. Several calibrations were made to ensure the accuracy of the AWI2007 Tomato Harvesting Robot. The AWIsoft07 and AWI2007 Tomato Harvesting Robot were subjected to several harvesting tests under the laboratory environment. The harvesting result shows the ability of the software and the harvester. Consequently, AWI2007 Tomato Harvesting Robot with the camera vision was able to recognize the tomato ripeness intelligently via neural network analysis and moved to the harvesting position. These situations provided new improvements for tomato harvesting system compared to the previous findings. Therefore the application of the neural network based on camera vision was successful perform as artificial intelligent for tomato harvesting robotic system

Design and Development of a Vision System Interface for Three Degree of Freedom Agricultural Robot

In this study, a vision system interfaced 3DOF agricultural harvester robot was designed, developed and tested. The robot was actuated by hydraulic power for heavy tasks such as picking and harvesting oil palm FFB. The design was based on the task of that robot, the type of actuators and on the overall size. Attention was given to the stability, portability and kinematic simplicity in relation to the hydraulic actuators. The derivation of the kinematic model was based on the Matrix Algebra for the forward kinematics, and the inverse kinematics problem was based on analytical formulation. The D-H representation was used to carry out the coordinates of the end-effector as the function of the joint angles. The joint angles of the robot were computed as the function of the end-effector coordinates to achieve the inverse kinematic model. A mathematical model that related the joint angles and the actuators length was derived using geometric and trigonometric formulations. A differential system was derived for the manipulator. This differential system represents the dynamic model, which describes relationships between robot motion and forces causing that motion. The Lagrange-Euler formulation with the D-H representation was applied to formulate the differential system. The importance of the derivation of the kinematic model arises in the development of the control strategy. While the derivation of the dynamic model helps in real time simulation. The robot was enhanced by a CCD camera as a vision sensor to recognise red object as a target. Red object was to exemplify the matured oil palm FFB . The recognition process was achieved by using C++ programming language enhanced by MIL functions. An algorithm based on empirical results was developed in order to convert the target coordinates from the image plane (pixel) into the robot plane (cm). The image plane is two-dimensional while the robot plane is three-dimensional. Thus at least one coordinate of the target in the robot plane should be known. An Interface program has been developed using Visual Basics to control and simulate 2D motion of the manipulator

Development of a Field Robot Platform for Mechanical Weed Control in Greenhouse Cultivation of Cucumber

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

BLOB Analysis for Fruit Recognition and Detection

Robot application in agriculture can ease the farming process, especially as the harvesting robot for seasonal fruit that is available in a short time. The addition of "eye" as the image sensor is an important feature for a harvesting robot. Thanks to the increment of technology, the camera is getting smaller with better performance, and lower prices. The cheap sensors and components make the creation of cheap and effective robot possible. Image processing is necessary for object detection, and open source software is available now for this purpose. This paper proposes BLOB analysis for object detection of 5 fruits with different shapes and colors. The simulation results show that the proposed method is effective for object detection regardless the shapes, colors, and noises

Development of a camera-vision guided automatic sprayer

This study describes the design and development of a camera-vision guided unmanned mover sprayer for the purpose of automatic weed control. The sprayer system was mounted on the mover. Modifications were carried out for both sprayer and mover systems, so that it can be operated remotely. The automated system was developed using the electromechanical system and controllers. It is capable of directing the mover sprayer to the target location given by the user. The electromechanical system was developed to control the ignition, the accelerator and the spraying systems. The controllers consist of an I/O module (ICPCON I-87057) and also a pair of radio modems (SST-2400) for data transmission. The graphical user interface (GUI) software to control the automatic system was developed by using Visual Basic Programming. The GUI has features which enable the user to perform desired tasks using the computer instead of going directly to the sprayer/mover. The combination of the multi controllers and developed control software in the development of the camera-vision-guided unmanned mover sprayer can reduce drudgery and increase safety

Rancangan End-effector untuk Robot Pemanen Buah Paprika

A research on designing an end-effector for a sweet pepper (Capsicum grossum) harvesting robot has been conducted. The objectives of this research were to design an end-effector prototype for the sweet pepper harvesting robot and to examine the performance of the end-effector in actuating the harvesting work. The end-effector was constructed in such a way so that enable to perform cutting and gripping motion in one action. The end-effector was designed using aluminum materials in order to get as light mass as possible. It dimension was 28 cm in length, 14 cm in width, and about 90 grams in weight. The field test of the prototype was conducted based on the conditions of plantation inside the greenhouse. Three kinds of inclination slope including 0o, 10o, and 20o were treated for the end-effector installation. The experimental result show that the third installation treatment ie: the end-effector with 20° inclination slope tend to produce the best performance which has the highest number of harvesting succeed

Feature extraction of near-spherical fruit with partial occlusion for robotic harvesting

For a fruit-picking robot in natural scenes, feature extraction of fruits occluded by leaves and branches based on machine vision is a key problem. In this study, the cluster barycentre (CB), edge barycentre (EB), circular Hough transform (CHT) and least square circle fitting (LSCF) are used to extract the features of fruit. The results indicate that the first two methods cannot accurately determine the circle in the presence of partial occlusion. The objects extracted by the CHT method include false targets in addition to longer time and larger memory required. The LSCF method, on the other hand, can accurately extract the features in a real-time mode. When the occluded area ratio is less than 52%, or the occlusion angle is less than 216°, the accuracy of feature extraction using LSCF can meet the requirements of the robot operation