Citrus Fruit Postharvest Losses Analysis using Hybrid Features

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.