Automation and robotics for bulk sorting in the food industry

Hamid, Gargazi; Deefholts, B.; Reynolds, Nikolai; McCambridge, D.; Mason-Palmer, K.; Briggs, Calum R.

doi:10.1533/9780857095763.2.267

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Commonly on European bottom trawlers, the catch is hauled on board and large volumes of catch are immediately placed on the conveyor belt. A method or protocol for managing the volume of catch on the conveyer belt to allow recording of all individual fish would improve the documentation of the catch by video-based monitoring (Hamid et al, 2012;Ruiz et al, 2013). Images of individual fish would ensure that all fish are counted and would facilitate species recognition by video reviewers.…”

Section: Discussionmentioning

confidence: 99%

How effective is electronic monitoring in mixed bottom-trawl fisheries?

Helmond

Chen

Poos

2014

ICES Journal of Marine Science

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In the context of the landing obligation under the European Common Fisheries Policy, electronic monitoring (EM) is often presented as one of the solutions to fully document catches. EM includes video monitoring to record the catch handling process on board the vessels. This study evaluated the efficacy of EM for cod (Gadus morhua) catches on vessels in a mixed bottom-trawl fishery and tested the hypothesis that cod catches are difficult to detect with video monitoring, specifically in catches with large volumes of bycatch. In 2011, a catch quota pilot study started for cod in the Dutch bottom-trawl fishery in which EM was used as an audit system to review the consistency of reported cod catches. Eleven vessels joined the pilot study on a voluntary basis. Participants received a 30% increase in individual quota for cod and were compensated with extra effort in days at sea. In return, all cod catches were counted against their cod quota. This mixed bottom-trawl fishery differs from fisheries where EM was proven to be a successful method, e.g. hook and line or single-species fisheries with low bycatch volumes. We conclude that distinguishing small numbers of cod in catches of mixed bottom-trawl fisheries is difficult because there is a low correlation between logbook and video data (Pearson r ¼ 0.17). We expect similar difficulty in other mixed demersal trawl fisheries with large bycatch volumes, when similar-looking species are targeted. Meanwhile, implementing a landing obligation will pose large challenges for fisheries with large volumes of bycatch. Limitations in the applicability of EM to control one of the most common types of fisheries in Europe will be a burden on the implementation of the European landing obligation. Improved protocols and technical adaptations may reduce some of the limitations encountered in this study.

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Section: Discussionmentioning

confidence: 99%

How effective is electronic monitoring in mixed bottom-trawl fisheries?

Helmond

Chen

Poos

2014

ICES Journal of Marine Science

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“…The increasing demand for high-quality cereal grains and the necessity of identifying and removing defective kernels have made automated sorting an essential component of the production process (Hamid et al, 2013). Currently, there are several technologies available for kernel sorting that leverage mechanical or optical properties (Henry and Jie, 2017; Inamdar and Suresh, 2014; Pearson, 2010).…”

Section: Introductionmentioning

confidence: 99%

Identifying Defects and Varieties of Malting Barley Kernels

Kozłowski,

Szczypiński,

Reiner

et al. 2024

Preprint

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This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models with complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach's advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

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“…2). Examples of sorting processes abound in the food industry, where food safety certifications and high production volumes make effective sorting critical, such as the ability to separate ripe from unripe fruit [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

SoftER: A Spiral Soft Robotic Ejector for Sorting Applications

Zournatzis,

Kalaitzakis,

Polygerinos

2023

IEEE Robot. Autom. Lett.

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For over thirty years optical belt drive configurations are being used in food industries to automatically sort produce in high operating speeds. Despite their benefits, these multi-component assemblies are prone to faults, difficult to clean, and require frequent maintenance that halts the production lines for considerable amount of time. In this paper, we adopt the abundantly occurring spiral motions encountered in nature and translate them to the proof-of-concept design and development of a soft pneumatic actuator (SPA), the SoftER. This novel actuator has the ability to rapidly unwind when pressurized to deliver impact forces. We explore this inherently low-cost and simple design and its potential to replace current systems based on the results of an application case study presented in this paper. Simulation driven optimization methods are leveraged, utilizing quasi-static and dynamic finite element methods models, to create a scalable framework for selecting the best performing design parameters for each application. Using rapid manufacturing processes the optimized actuator is constructed and physical testing validates its high-speed and impact force delivering capabilities.

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Automation and robotics for bulk sorting in the food industry

Cited by 7 publications

References 2 publications

How effective is electronic monitoring in mixed bottom-trawl fisheries?

How effective is electronic monitoring in mixed bottom-trawl fisheries?

Identifying Defects and Varieties of Malting Barley Kernels

SoftER: A Spiral Soft Robotic Ejector for Sorting Applications

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