An Effective Citrus Disease Detection And Classification Using Deep Learning Based Inception Resnet V2 Model

Abstract

Plant disease is a main challenge which affects the productivity and quality of the agricultural sector. The citrus plants like lemon, mandarin, orange, tangerine, grapefruit, and lime are usually grown fruits globally. The citrus production industries produce a massive quantity of waste annually where half of the citrus peel gets degraded because of various plant diseases. The recognition and classification of citrus diseases is a crucial process which improves the quality of the fruits and increase productivity. Keeping this view, this paper presented an automated citrus disease diagnosis by incorporating several processes namely preprocessing, segmentation, feature extraction, and classification. Here, Otsu based segmentation process is employed followed by ResNet with Inception v2 based feature extractor. Besides, random forest (RF) classifier is also utilized to classify the different kinds of citrus diseases. A detailed experimentation of the presented model takes place on benchmark Citrus Image Gallery dataset and the outcome pointed out the excellent disease identification and classification with the highest accuracy of 99.13%.