Defect Detection of Fruit Based on Deep Learning Approaches
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
SECONGRESS02_009
تاریخ نمایه سازی: 19 مرداد 1403
Abstract:
Fruits have a relatively short lifespan and are one of the most important sources of nutrition for humans. Fruit deterioration may happen at a number of times, including during harvest, transit, storage, etc. One criterion for determining the fruit's quality is its freshness. Before being consumed by people, around ۲۵% of the produced fruits get spoiled for a variety of reasons. The rotting of one fruit directly affects the nearby ones. It is also one of the markers that estimate how long a fruit may be kept in storage. It is easier to remove rotten fruits from the whole lot by taking the proper action as soon as the deterioration is identified. In order to aid in keeping the fruits next to it from being spoiled. Recent advancements in technology, specifically in the field of deep learning, have greatly aided in the automated detection of damaged fruit. However, it is important to note that current methods only consider the surface characteristics of the fruit, disregarding any internal factors that may contribute to spoiling. To assess the freshness of fruits, a supervised learning approach is used with bananas being the chosen subject of this study. The researchers utilized a detection method, allowing for reliable differentiation between rotten and high-quality fruit.
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Authors
Amirreza Rouhbakhshmeghrazi
Department of Electronic Information, Northwestern Polytechnical University, Xi’An, Shaanxi, China
Shayan Nalbandian
Department of Software, Northwestern Polytechnical University, Xi’An, Shaanxi, China