Design of an automatic apple sorting system using machine vision

Sofu, Mehmet Mahir; Er, Orhan; Kayacan, Mehmet Cengiz; Çetişli, Bayram

doi:10.1016/j.compag.2016.06.030

Cited by 157 publications

(65 citation statements)

References 18 publications

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“…The use of such techniques is well known in processing machines. 8,9 On one hand, these systems are flexible in use for different types of measurements, so they are often used in analysing processing machines. On the other hand, HS cameras are very expensive compared to other types of sensors and for getting satisfying results, the operator has to consider multiple requirements of the system.…”

Section: Online Process Controlmentioning

confidence: 99%

Commissioning new applications on processing machines: Part II – implementation

Troll

Schebitz

Majschak

et al. 2018

Advances in Mechanical Engineering

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The subject of this splitted article is the commissioning of a new application that may be part of a processing machine. At the example of the intermittent transport of small sized goods, for example, chocolate bars, ideas for increasing the maximum machine performance are discussed. Therefore, optimal process motion profiles are synthesised with the help of a computer simulation. In the first part of the paper, the modelling of the process was shown. This second part focusses on implementing the simulated motion approaches on an experimental test rig, whereby the new motion approach is compared to the conventional approach. Hence, the increasing of the performance can be proven. Eventually, possibilities for an online process control are observed which are necessary to prevent unstable process conditions.

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Section: Online Process Controlmentioning

confidence: 99%

Commissioning new applications on processing machines: Part II – implementation

Troll

Schebitz

Majschak

et al. 2018

Advances in Mechanical Engineering

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“…Effective culling of fruits on packing lines is influenced by both fruit characteristics, that is colour, size of insect injury, the co-occurrence of other visual defects and operational factors, such as the numbers of fruits sorted per worker-second (Knight and Moffitt 1991). Progress in developing automated defect inspection processes based on machine vision and learning is progressing rapidly (Sofu et al 2016;Gupta et al 2018) and the possibility of detecting internal pests in apples in packinghouses has been explored but does not seem practical due to the handling times required (Li et al 2018).…”

Section: Post-harvest Managementmentioning

confidence: 99%

Integrated management of tortricid pests of tree fruit

Knight¹,

Judd²,

Gilligan³

et al. 2019

Burleigh Dodds Series in Agricultural Science

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“…Furthermore, distributed autonomous gardening systems have been utilized for urban/indoor precision agriculture applications, where multi-robot teams are networked with the environment [10]. Post-harvest robotic technologies have been developed based on machine vision for disease detection as well as fruit and product sorting [42][43][44].…”

Section: Robotic Harvestingmentioning

confidence: 99%

iPathology: Robotic Applications and Management of Plants and Plant Diseases

2017

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The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

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Design of an automatic apple sorting system using machine vision

Cited by 157 publications

References 18 publications

Commissioning new applications on processing machines: Part II – implementation

Commissioning new applications on processing machines: Part II – implementation

Integrated management of tortricid pests of tree fruit

iPathology: Robotic Applications and Management of Plants and Plant Diseases

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