Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques Shashank Chaudhary 1 , Upendra Kumar 2* 1 Department of Computer Science & Engineering, Dr. Abdul Kalam Technical University, Lucknow 226031, India 2 Department of Computer Science & Engineering, Institute of Engineering and Technology, Dr. Abdul Kalam Technical University, Lucknow 226031, India Corresponding Author Email: ukumar@ietlucknow.ac.in Copyright: ©2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.18280/ts.410216 ABSTRACT Received: 22 April 2023 Revised: 28 November 2023 Accepted: 20 December 2023 Available online: 30 April 2024 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. Keywords: classification, image processing, feature extraction, machine learning 1. INTRODUCTION India boasts some of the world's largest rice fields. It is renowned for its extensive rice growing. Rice agriculture has a crucial role in India's economy. Historians claim that the cultivated variant of this plant originated in the foothills of the Eastern Himalayas. Subsequently, it was disseminated to more nations, including Burma, Thailand, and Vietnam [1]. Rice is widely recognized as a highly significant staple crop. It is also considered the second most productive cereal in the world, following wheat. India is anticipated to have a surge in population and a growing demand for rice, which would consequently necessitate enhanced food security measures and heightened agricultural output. Regrettably, illnesses have the potential to inflict damage upon the agricultural produce of a nation. Plant disease control is more complex due to multiple variables. These factors encompass the rising need for secure and nourishing sustenance, the exhaustion of ecological assets, and the rivalry over scarce land resources. Monocultures and agricultural intensification are responsible for the growing threat of disease outbreaks [2]. Detecting the initial phases of a botanical ailment is crucial to preempt its detrimental impact on agricultural yields. Tian introduced a sophisticated technique for identifying diseases in rice crops by utilizing a computer vision system. The researchers employed a range of sophisticated methodologies, including neural networks, machine learning, and image processing, to detect and classify plant illnesses [3]. A visual method for recognizing different types of rice illnesses based on their distinct leaf texture characteristics. The objective of this study is to present the principles of image processing for the categorization of plant diseases [4]. An inherent drawback of the existing classification systems is their lack of generalizability. Consequently, their ability to recognize a specific disease is limited to the extent of their training on a particular dataset [5]. After successfully recognizing a rice plant disease, the model had an accuracy rate of 90%. However, subsequent exposure to varying conditions resulted in a decline in its accuracy, so casting doubt on its utility. This is a significant factor contributing to the diminished precision of the outcomes. Presently, this endeavor suffers from a want of a deep learning-based solution capable of discerning various forms of diseases in rice plants. The identification of diseases is a crucial aspect of agricultural research since it enables accurate diagnosis and monitoring of crop conditions on a farm. The objective of this study is to enhance the efficacy of the image-based detection system by investigating several rice leaf diseases [6]. The primary goals are to create an automated system that use image processing and machine learning approaches to detect and Traitement du Signal Vol. 41, No. 2, April, 2024, pp. 739-752 Journal homepage: http://iieta.org/journals/ts 739