Research on Carrot Grading Based on Machine Vision Feature Parameters

Xie, Weijun; Wang, Fenghe; Yang, Dongya

doi:10.1016/j.ifacol.2019.12.485

Cited by 27 publications

(14 citation statements)

References 14 publications

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“…Additionally, [88] applied a computer vision system and machine learning algorithms to obtain a prediction model for cherry tomato volume and mass estimation and the results achieved an accuracy of 0.97. Also, the carrot was graded using a machine vision system and the results showed that the constructed image acquisition system success to extract the feature parameters of the carrot accurately [89]. As well, [6] sorted irregular potatoes using the RGB color imaging technique.…”

Section: Visible Light Imagingmentioning

confidence: 99%

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Eissa¹,

Helyes²,

Romano³

et al. 2022

A Glance at Food Processing Applications

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In recent decades, the quality and safety of fruits, vegetables, cereals, meats, milk, and their derivatives from processed foods have become a serious issue for consumers in developed as well as developing countries. Undoubtedly, the traditional methods of inspecting and ensuring quality that depends on the human factor, some mechanical and chemical methods, have proven beyond any doubt their inability to achieve food quality and safety, and thus a failure to achieve food security. With growing attention on human health, the standards of food safety and quality are continuously being improved through advanced technology applications that depend on artificial intelligence tools to monitor the quality and safety of food. One of the most important of these applications is imaging technology. A brief discussion in this chapter on the utilize of multiple imaging systems based on all different bands of the electromagnetic spectrum as a principal source of various imaging systems. As well as methods of analyzing and reading images to build intelligence and non-destructive systems for monitoring and measuring the quality of foods.

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Section: Visible Light Imagingmentioning

confidence: 99%

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Eissa¹,

Helyes²,

Romano³

et al. 2022

A Glance at Food Processing Applications

View full text Add to dashboard Cite

show abstract

“…As a result of the study, researchers reported a better detection of harvesting size in lettuce than human. Xie et al (2019) determined the success of grading and classification according to the colour scale in carrots with the image processing technique as 96.67%. The image processing technique developed for the harvesting of products that have reached the appropriate size and colour in various plants can be safely used (Zapetony-Andersen & Lehnet 2019, Kennedy et al, 2019, Zhang et al, 2019.…”

Section: Use Of Drones In Plant Productionmentioning

confidence: 99%

Çiftçinin Gökteki Gözü: Drone

Gül

Güzey

Yıldırım

et al. 2021

Türkiye İnsansız Hava Araçları Dergisi

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Mankind develops new technics and technologies constantly to have a better life. In this way, powerful machines and robotic systems replace human and animal labour in agriculture. Animal husbandry, which is a part of agricultural activity in our country, is mostly carried out in rural areas due to its nature. Goat breeding, in particular, is carried out in highlands, scrub and forest lands and under extensive conditions. Qualified shepherd employment is an important handicap in sheep and goat breeding. Agricultural enterprises are also faced with a manpower deficit due to the decrease in the rural population. Remote sensing systems have been developed and used for about 100 years to support and enhance agricultural activities. In this study, the importance of unmanned aerial vehicles in terms of animal husbandry is mentioned and it is emphasized that they should be taken into consideration in future agricultural projections.

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“…CNN models also play an important role in medicine for understanding the genetic basis and treatment of diseases such as brain [ 27 ], breast [ 28 ], and skin cancer [ 29 ], as well as aneurysms and autism in humans [ 30 , 31 , 32 ]. CNN is also used in robotics for visual navigation [ 33 ], controlling the driving path of autonomous vehicles [ 34 ], planning the movement paths of ground robots [ 35 ], programming production manipulators [ 36 ], and assessing the quality of products, agricultural produce, and other biological materials [ 37 , 38 ]. Modern digital techniques and computer data analysis methods allow for precise and automated control of food quality [ 39 ] and the identification of weeds, diseases, or pests in crop plants [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

Rybacki,

Niemann,

Derouiche

et al. 2024

Sensors

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The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.

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Research on Carrot Grading Based on Machine Vision Feature Parameters

Cited by 27 publications

References 14 publications

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

Çiftçinin Gökteki Gözü: Drone

Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)

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