Development of an Experimental Strawberry Harvesting Robotic System (original) (raw)
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Machine Vision Algorithm for Robots to Harvest Strawberries in Tabletop Culture Greenhouses
A strawberry harvesting robot consisting of a four DOF manipulator, an end-effector with suction pad, a three camera vision system and a rail type traveling device was developed as a trial to conduct experiments in a tabletop culture greenhouse. In order to harvest the strawberries with curved or inclined peduncles, a wrist joint which can rotate 15 degrees to the left or right from its base position was added. On the algorithm side, peduncle inclination angle was measured by the center camera. Harvesting experiments show that it was possible to precisely harvest more than 75% of fruits which were not occluded by other fruits with the developed robot. Experimental data also show that peduncle length, color and inclination pattern change with the seasons. Complex situations often exist in the real field conditions such as limited visibility of back end strawberries, occluded fruits, obstructions and complex peduncle patterns. Further studies are desirable to automate the harvesting task using a robot.
2021
Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting strawberries should be equipped with four subsystems: a video object detection system, a collecting arm, a unit for the reception and possible packaging of the fruit, and a traction system unit. This paper presents a concept for the design and operation of the working section of a harvester for strawberry fruit crops grown in rows or beds, in open fields, and/or under cover. In principle, the working section of the combine should meet parameters comparable with those of manually harvested strawberries (efficiency, quality of harvested fruit) and minimise contamination in the harvested product. In order to meet these requirements, in the presented design concept, it was assumed that these activities would be performed during harvesting w...
Towards designing a robot gripper for efficient strawberry harvesting
Strawberry is a very delicate fruit that requires special treatment during harvesting. It this paper, a strawberry gripper is developed for picking by investigating the hand motion of a skilled worker. It is demonstrated that the hand motion for detaching the fruit from the stem has a significant role in the process because it can reduce the required force and consequently the damage to the fruit. Experiments are conducted using a robot arm and force sensors to measure the maximum gripping force and the required detachment force under a variety of detachment ways and gripping materials. By analysing those results a prototype of a simple and economic gripper is developed that demonstrates an efficiency comparable to the human hand for this task.
Review on fruit harvesting method for potential use of automatic fruit harvesting systems
In horticultural industry, conventional harvesting is done by 'handpicking' methods to remove hundreds of fruits such as citrus fruits in random spatial locations on the individual fruit trees. It is well known that harvesting fruits in a large scale is still inefficient and not cost effective. To solve this challenging task, mechanical harvesting systems have been investigated and practiced to enhance profitability and efficiency of horticultural businesses. However they often damage fruits in the harvesting process. Development of efficient fruit removal methods are required to maintain the fruits quality. This paper reviews fruit harvesting systems from purely mechanical based systems in which operator involvement is still required, to automatic robotic harvesting systems which require minimal or no human intervention in their operation. The researches on machine vision system methodologies used in the automatic detection, inspection and the location of fruits for harvesting are also included. The review is focused on the citrus fruits due to the fact that the research on citrus fruit harvesting mechanism is a bit more advanced than others. Major issues are addressed in the camera sensor and filter designs and image segmentation methods used to identify the fruits within the image. From this review, the major research issues are addressed as future research directions.
Autonomous Fruit Harvester with Machine Vision
Journal of Telecommunication, Electronic and Computer Engineering, 2018
This study presents an autonomous fruit harvester with a machine vision capable of detecting and picking or cutting an orange fruit from a tree. The system of is composed of a six-degrees of freedom (6-DOF) robotic arm mounted on a four-wheeled electric kart. The kart uses ZED stereo camera for depth estimation of a target. It can also be used to detect trees using the green detection algorithm. Image processing is done using Microsoft Visual Studio and OpenCV library. The x & y coordinates and distance of the tree are passed on to Arduino microcontroller as inputs to motor control of the wheels. When the kart is less than 65cm to the tree, the kart stops and the robotic arm system takes over to search and harvest orange fruits. The robotic arm has a webcam and ultrasonic sensor attached at its end-effector. The webcam is used for orange fruit detection while ultrasonic sensor is used to provide feedback on the distance of the orange fruit to end-effector. Multiple fruit harvesting ...
Method of ripe tomato detecting for a harvesting robot
Harvesting robots could be efficiently implemented in a greenhouse for picking ripe tomatoes. For such robots, tomato detection is a basic operation of visual control which provides the correct target object for precise moving of the end-effector or gripper. The tomatoes were chosen to perform this research work not only because they occupy the second place in agriculture after potatoes, but also due to their simple round shape and the apparent red color of the ripe fruit. In this study, an algorithm for automatic detection of ripe tomato and discrimination it from the natural background is proposed. The proposed algorithm is based on image processing procedures and starts with translating original color photograph to grayscale image for further treatment. An algorithm of red tomatoes detection was developed and verified using special functions and inbuilt scripting language of Matlab software.
Design and fuzzy control of a robotic gripper for efficient strawberry harvesting
Robotica, 2014
SUMMARYStrawberry is a very delicate fruit that requires special treatment during harvesting. A hierarchical control scheme is proposed based on a fuzzy controller for the force regulation of the gripper and proper grasping criteria, that can detect misplaced strawberries on the gripper or uneven distribution of forces. The design of the gripper and the controller are based on conducted experiments to measure the maximum gripping force and the required detachment force under a variety of detachment techniques. It is demonstrated that the hand motion for detaching the fruit from the stem has a significant role in the process because it can reduce the required force. By analysing those results a robotic gripper with pressure profile sensors is developed that demonstrates an efficiency comparable to the human hand for strawberry grasping. The designed gripper and fuzzy controller performance is tested with a considerable number of fresh fruits to demonstrate the effectiveness to the un...
2014
The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the setup , increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.
Review on “ Automation in Fruit Harvesting ”
2015
The challenges in developing a fruit harvesting robot are recognizing the fruit in the foliage and detaching the fruit from the tree without damaging both the fruit and the tree. In large-scale greenhouse production, technological developments can reduce production costs; mechanization of crop maintenance and harvesting is one desirable way to accomplish this. Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The objectives of this study were to develop a real-time fruit detection system using machine vision and laser ranging sensor and to develop an end effector capable of detaching the fruit similar to the human picker. This paper deals with fruit recognition and it presents the development of a various techniques for the harvesting of fruits.
Sensors, 2014
The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.