http://ssu.ac.ir/cms/fileadmin/user_upload/Mtahghighat/tfood/ARTICLES/koliat/Improving_quality_inspection_of_food_products.pdf

Improving quality inspection of food products by computer vision: a review

This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.

Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

Agricultural robots for field operations: Concepts and components

Sensors and systems for fruit detection and localization

This paper describes the concept of an autonomous robot for harvesting cucumbers in greenhouses. A description is given of the working environment of the robot and the logistics of harvesting. It is stated that for a 2 ha Dutch nursery, 4 harvesting robots and one docking station are needed during the peak season. Based on these preliminaries, the design specifications of the harvest robot are defined. The main requirement is that a single harvest operation may take at most 10 s. Then, the paper focuses on the individual hardware and software components of the robot. These include, the autonomous vehicle, the manipulator, the end-effector, the two computer vision systems for detection and 3D imaging of the fruit and the environment and, finally, a control scheme that generates collision-free motions for the manipulator during harvesting. The manipulator has seven degrees-of-freedom (DOF). This is sufficient for the harvesting task. The end-effector is designed such that it handles the soft fruit without loss of quality. The thermal cutting device included in the end-effector prevents the spreading of viruses through the greenhouse. The computer vision system is able to detect more than 95% of the cucumbers in a greenhouse. Using geometric models the ripeness of the cucumbers is determined. A motion planner based on the Aa-search algorithm assures collision-free eye-hand co-ordination. In autumn 2001 system integration took place and the harvesting robot was tested in a greenhouse. With a success rate of 80%, field tests confirmed the ability of the robot to pick cucumbers without human interference. On average the robot needed 45 s to pick one cucumber. Future research focuses on hardware and software solutions to improve the picking speed and accuracy of the eye-hand co-ordination of the robot.

An Autonomous Robot for Harvesting Cucumbers in Greenhouses

Machine vision based quality evaluation of Iyokan orange fruit using neural networks

Two types of robotic end-effectors capable of harvesting tomato fruits were manufactured based on the physical properties of tomato plant and tested The first prototype end-effector consisted of two parallel plate fingers and a suction pad The fingers pick a fruit off at the joint of its peduncle after the suction cup singulates it by vacuum from other fruits in the same cluster From the results of harvesting experiment, the end-effector could not harvest fruits with a short peduncle because the fruits were detached from the suction pad before they were gripped by the fingers Therefore, the second prototype in which the functions to detect the fruit position and the air pressure in the pad were installed, was made, so that the fruits were harvested regardless of the length of their peduncle Experimental results using the improved end-effector showed that the fruits were harvested successfully with no damage

End-Effectors for Tomato Harvesting Robot

Fruit harvesting robots in Japan.

A multipurpose agricultural robot which works in vineyard has been studied. This robot, which consists of a manipulator, a visual sensor, a travelling device and end-effecters, is able to do several works by changing the end-effecters. Four end-effectors for harvesting, berry thinning, spraying and bagging have been developed for this robot system. The harvesting end-effector which grasps and cuts rachis was able to harvest bunches with no damage. The berry thinning end-effector which consists of three unified bunch shape parts. The spraying end-effector sprays the target uniformly, and the bagging end-effector is able to put bags on growing bunches one by one continuously. From the experimental results in a field and laboratory, it was observed that each end-effector could perform efficiently.

Agricultural robot in grape production system

An end-effector was developed for a tomato cluster harvesting robot. This end-effector can harvest not individual fruits but a whole fruit cluster to improve the robot's harvest efficiency. Experiments for harvesting tomato clusters were conducted in a high-density plant training system. According to a harvesting algorithm, the end-effector was able to perform well, even when target peduncle orientations were not given. Although the success rate of harvesting tomato clusters was 50 %, it is considered that this rate would improve if an end-effector is used for the high-wire tomato plant training systems in Dutch systems where the node lengths of plants are long enough to loosely hold the main stems.

Mitsuji Monta

Papers

Machine vision based quality evaluation of Iyokan orange fruit using neural networks

End-Effectors for Tomato Harvesting Robot

Fruit harvesting robots in Japan.

Agricultural robot in grape production system

Development of an End-Effector for a Tomato Cluster Harvesting Robot