Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Abstract

Agriculture, a pivotal sector in the Indian economy, plays a crucial role in national development. A significant challenge within this domain is the detection of crop diseases, with brown spot, leaf blast, and bacterial blight being prevalent afflictions in rice crops. This study presents an innovative approach, integrating Gray-level Co-occurrence Matrix (GLCM) and Intensity-Level Based Multi-Fractal Dimension (ILMFD) for feature extraction in disease identification. The efficacy of this integrated technique was evaluated through a comparison with various classifiers. Specifically, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Neuro-Genetic Algorithm (Neuro-GA) were employed to ascertain their precision in disease detection. It was observed that the combination of GLCM and ILMFD with the Neuro-GA classifier achieved an accuracy exceeding 90%. Remarkably, when paired with the SVM classifier, this integrated approach yielded a precise accuracy of 96.7% in detecting brown spot disease in rice. These findings not only validate the effectiveness of the GLCM and ILMFD methods in feature extraction but also highlight the superior performance of the SVM classifier in crop disease detection. This research contributes significantly to the field, offering a robust solution for accurate disease diagnosis in rice crops, thereby aiding in the sustainable management of agricultural practices.