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Title: Improved YOLO object detection algorithm to detect ripe pineapple phase
Authors: Nguyen, Ha Huy Cuong
Trinh, Trung Hai
Meesad, Phayung
Nguyen, Thanh Thuy
Keywords: Deep Learning
Computer Vision
Deep Convolutional Networks
Loss Function
Issue Date: Jun-2022
Publisher: IOS Press
Abstract: A computational method for detecting pineapple ripening could lead to increased agricultural productivity. It is possible to predict fruit maturity before harvesting to increase agricultural productivity. A ripe fruit’s quality, its standard content of physical and chemical properties will increase the value of a good when traded outside the market. This paper studies and improves the Tiny YOLO-v4 model for identifying the pineapple ripening period. Researchers studied pineapples in a pineapple garden in Vietnam’s central region. They wanted to determine when pineapples were ripe. The API and the website are based on the YOLO innovation model. Apps and website APIs will be available for mobile devices so that people can monitor fruits. Technology transfer and academic research are combined in this study. We prepared the pineapple data set by using 5,000,000 pineapples harvested from the pineapple farm at different stages of growth. To make the measurements, we improved the YOLO-v4 algorithm. This results in a more accurate training model and reduced train-ing time. A 98.26% recognition accuracy is quite impressive. Research takes place at large-scale plantations, so the models are created from the data collected at the plantations and are used as labels; training takes a long time for the tiniest details about pineapples, and finding pineapple-growing regions takes a long time. The deep learning classifier was able to process pineapple plantation photos by using the camera on the mobile phone.
Description: Journal of Intelligent & Fuzzy Systems, Vol. 43, No. 1, pp. 1365-1381.
ISSN: 1875-8967
Appears in Collections:NĂM 2022

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