• Khushwant Kaur
• Meena JIndal

Agriculture, one of the oldest human occupations, has seen significant technological advancements to address the challenges posed by a growing population and limited farmland. In India, the agriculture sector employs over 50% of the workforce and contributes around 17-18% to the GDP. However, plant diseases cause substantial economic and production losses, particularly in developing countries. Traditional disease detection methods, which rely on manual observation, are labor-intensive and impractical for large-scale farms. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to these challenges. AI-powered image processing can enhance the accuracy and efficiency of plant disease detection, enabling early intervention and better management of crop health. These technologies involve steps such as image acquisition, preprocessing, segmentation, feature extraction, and classification, providing a cost-effective alternative to traditional methods. For instance, Convolutional Neural Networks (CNNs) have demonstrated high accuracy in detecting and classifying diseases in crops like tomatoes. Despite challenges such as complex backgrounds and varying disease characteristics, AI models can effectively manage these issues through advanced algorithms and data augmentation techniques like Generative Adversarial Networks (GANs). The integration of AI and image processing in agriculture not only improves disease detection but also supports precision farming, contributing to increased crop yield and quality. This research underscores the critical role of AI and image processing technologies in sustainable agriculture, highlighting their potential to mitigate economic losses due to plant diseases and enhance global food security.

GLCM, SVM, KNN, Genetic Algorithm (GA), Radon Transform (RT

"ENHANCING AGRICULTURAL SUSTAINABILITY THROUGH AI-POWERED IMAGE PROCESSING: A COMPREHENSIVE STUDY ON PLANT DISEASE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.c770-c778, July-2024, Available :http://www.jetir.org/papers/JETIR2407295.pdf

"ENHANCING AGRICULTURAL SUSTAINABILITY THROUGH AI-POWERED IMAGE PROCESSING: A COMPREHENSIVE STUDY ON PLANT DISEASE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppc770-c778, July-2024, Available at : http://www.jetir.org/papers/JETIR2407295.pdf