Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples

Stasenko, Nikita; Чернова, Е. В.; Shadrin, Dmitrii; Ovchinnikov, George; Krivolapov, I P; Pukalchik, Mariia

doi:10.1109/i2mtc50364.2021.9460071

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The interest in using hidden layers has exceeded traditional techniques in terms of popularity, particularly in object recognition, classification, and detection [21]. One of the most popular types of deep neural networks is known as a convolutional neural network (CNN) [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.

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Section: Introductionmentioning

confidence: 99%

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

et al. 2023

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“…From these images, mango quality was automatically categorized into three classes based on parameters such as defects, shape, size, and ripeness. Stasenko [23] et al trained U-Net and Deeplab models based on convolutional neural network (CNN) to detect and predict decay regions in post-harvest apples stored at room temperature, which improved the food storage process in precision agriculture by automatically detecting and quantifying decay regions. Abdallah [24] et al collected images of healthy and spoiled beef and proposed a deep learning method based on ResNet-50 as a promising classifier for grading and classifying beef.…”

Section: Introductionmentioning

confidence: 99%

Quality Grading and Prediction of Frozen Zhoushan Hairtails in China Based on ETSFormer

Hu,

Yan

et al. 2023

Sustainability

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With the increasing demand for high-quality, healthy, and nutritious food, hairtails have good potential for development in both domestic and international markets. In particular, Zhoushan hairtail is known as one of the best-tasting hairtail in the world for its unique composition and flavor. However, as a perishable food, the quality and safety of hairtails are susceptible to temperature and storage time. Therefore, the management of storage conditions and the prediction of quality changes in hairtails have become particularly important. In this study, Zhoushan hairtail is selected as an experimental subject, and its quality is assessed by collecting the physicochemical characteristics of hairtail at four different temperatures (−7 °C, −13 °C, −18 °C, and −23 °C) over time. Combined with the K-Means++ algorithm, we have constructed a hierarchy of hairtail quality and predicted its quality using the ETSFormer model. Through the validation of the self-constructed data set, our model has achieved good results in predicting the low, medium, and high quality of hairtails, with F1 values of 92.44%, 95.10%, and 98.01%, respectively. The model provides a theoretical basis for the scientific storage and quality regulation of Zhoushan hairtail.

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An improved U-Net network-based quantitative analysis of melon fruit phenotypic characteristics

Qian

Liu²,

Du³

et al. 2022

Food Measure

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Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples

Cited by 9 publications

References 21 publications

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Systematic literature review: application of deep learning processing technique for fig fruit detection and counting

Quality Grading and Prediction of Frozen Zhoushan Hairtails in China Based on ETSFormer

An improved U-Net network-based quantitative analysis of melon fruit phenotypic characteristics

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