Citrus Fruit Postharvest Losses Analysis using Hybrid Features

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ID: 312779
2025
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Abstract
The citrus fruit is an essential food source for humans, as it contains several vitamins and minerals, which makes it one of the most significant fruit crops globally. A wide variety of fascinating citrus fruits can be found in Pakistan. Most postharvest losses, 30-50% of the total production, are due to improper handling of fruits throughout the packing, shipping, and storing processes. Manual sorting of horticulture items increases postharvest losses however innovative algorithms and technology offer solutions to mitigate these losses. The focus of this research is to enhance current methods of fruit grading, decrease post-harvest losses and preserve fruit quality. Artificial intelligence (AI) model grades citrus fruits for real-world applications. With the use of AI classifiers, we can categories citrus fruits into four distinct stages based on their ripeness: initial-ripe, semi-ripe, fully ripe, and defective or damaged. Each stage is distinguished by its own unique hybrid feature, color attributes (RGB), texture and size. Three Convolutional neural network (CNN) models namely, MobileNet, VGG16, and Inception V3 used to achieve accuracy of 97%, 98%, 95.6% on citrus fruit dataset. The VGG16 gives the best results on grading.
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imported_1777056753_69ebbbf1eb14a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Salman Qadri
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
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URL
https://jcbi.org/index.php/Main/article/view/958
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