Citation :

Gaurav Sharma, Mohit Chhabra, Neha Bhatia, Ashima Arya, Sandeep Kumar, "AI-Driven Identification of Rice Crop Disorders Using Multi-Model Classification Framework," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 321-328, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P123

Abstract

Cultivated in many nations across the globe, rice is a staple food of great importance. Rice leaf diseases can severely affect crop cultivation, resulting in low crop yields and financial losses. In an older method of identifying leaf diseases, they are classified based on their color, morphology, texture, and shape. Fully automated instructional systems can quickly identify diseased leaves with minimal human assistance. Most of the earlier research on identifying leaf diseases in rice crops used machine learning and feature extraction methods. Characteristics like its shade, surface, patterns of veins, and lesion extent were retrieved from photos of sick leaves. Stated differently, machine learning identifies the illness by extracting characteristics. Instead, machine learning-based feature vector extraction is not totally superior because it involves retraining and missing one dimension. The proposed hybrid model predicts the diseased leaves of rice crops with 97% accuracy and minimum training and validation losses of 0.80 and 1.25, respectively.

Keywords

Accuracy, CNN, Disease Detection, Rice Leaf, SVM.

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