Design and assessment of an automated sweet pepper seed sorting machine

Kanjanawanishkul, Kiattisin; Chupawa, Prarin; Nuantoon, Thanaphat

doi:10.1016/j.eaef.2018.05.001

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…By applying image processing and fuzzy logic [16] or deep learning networks [17], modern pepper farmers can choose the best species to be planted in their farming area. Then, using a seed sorting machine [18], qualified seeds can be selected. Vision technology is used in many aspects of agriculture [19].…”

Section: Non-conventional Methodsmentioning

confidence: 99%

The Role of Thermal and Electrical Energies to Increase the Pepper Quality: A Review

Rustandi,

Irmansyah,

Wijonarko

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Indonesia is one of the most prominent pepper producers and consumers worldwide. Indonesian pepper quality, however, is still low. The pepper’s low-quality problem is caused by the processing that still uses the traditional (conventional) method. This study reviews many techniques that have the potential to improve pepper quality. The review result from the references shows that waiting time (delaying) in the ripening process can increase the pepperine level from 4.33 % to 6.60 %. The pepperine level is the most vital parameter to show pepper quality. However, a more comprehensive result that compares the conventional and non-conventional methods for all kinds of pepper quality parameters still could not be obtained.

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Section: Non-conventional Methodsmentioning

confidence: 99%

The Role of Thermal and Electrical Energies to Increase the Pepper Quality: A Review

Rustandi,

Irmansyah,

Wijonarko

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Automated analysis and sorting systems have been developed [1], [2], [3], [4], [5], [6], [7], [8] for mm-sized objects, such as seeds, beans, and kernels, in the agriculture and food sector. Instead of artificial neural networks (ANNs), some works favour non-ANN image processing [2], [3], [6] or signal processing with multi-spectral sensors [8]. A relative advantage of these approaches is the analysis efficiency.…”

Section: Introductionmentioning

confidence: 99%

Incremental two-network approach to develop a purity analyzer system for canola seeds

Singh¹,

Fernando²,

Dileepan³

2022

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Given a suitable dataset, transfer learning using deep convolutional neural networks is an effective method to develop a system to detect and classify objects. Despite having models pretrained on large general-purpose datasets, the requirement to manually label an application-specific dataset remains a limiting factor in system development. We consider this wider problem in the context of the purity analysis of canola seeds, where end users wish to distinguish species of interest from contaminants in images taken with optical microscopes. We use a Detector network, trained only to detect seeds, to help label the dataset used to train an Analyzer network, capable of both seed detection and classification. We present results, over three experiments that involve 25 contaminant species, including Primary and Secondary Noxious Weed Seeds (as per the Canadian Weed Seeds Order), to validate our incremental approach. We also compare the proposed system to competing ones in a literature review.

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“…There are also simpler methods for checking seed coloration by counting pixels of a particular color [29].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Sorting Systems for Transparent Plastic Granulate

Peršak

Viltužnik²,

Hernavs

et al. 2020

Applied Sciences

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Granulate material sorting is a mature and well-developed topic, due to its presence in various fields, such as the recycling, mining, and food industries. However, sorting can be improved, and artificial intelligence has been used for this purpose. This paper presents the development of an efficient sorting system for transparent polycarbonate plastic granulate, based on machine vision and air separation technology. The developed belt-type system is composed of a transparent conveyor with an integrated vision camera to detect defects in passing granulates. The vision system incorporates an industrial camera and backlight illumination. Individual particle localization and classification with the k-Nearest Neighbors algorithm were performed to determine the positions and conditions of each particle. Particles with defects are further separated pneumatically as they fall from the conveyor belt. Furthermore, an experiment was conducted whereby the combined performances of our sorting machine and classification method were evaluated. The results show that the developed system exhibits promising separation capabilities, despite numerous challenges accompanying the transparent granulate material.

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Design and assessment of an automated sweet pepper seed sorting machine

Cited by 10 publications

References 9 publications

The Role of Thermal and Electrical Energies to Increase the Pepper Quality: A Review

The Role of Thermal and Electrical Energies to Increase the Pepper Quality: A Review

Incremental two-network approach to develop a purity analyzer system for canola seeds

Vision-Based Sorting Systems for Transparent Plastic Granulate

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