Plant Diseases Recognition Using Machine Learning Algorithms

This report explores the critical field of plant disease recognition through the use of various machine learning algorithms. As agriculture faces increasing challenges from pathogens and diseases, the rapid and accurate identification of plant afflictions is essential for effective crop management and improved yields. We analyzed a range of machine learning techniques, from traditional classifiers to advanced deep learning models, focusing on their ability to classify and diagnose plant diseases using symptomatology and image analysis. Our study evaluates the strengths and limitations of these approaches, providing insights into their technical details and practical applicability in agriculture. Through comprehensive experimentation and performance comparisons, we highlight the efficacy and robustness of different algorithms in plant disease management. The findings offer valuable guidance on how these methodologies can contribute to agricultural sustainability, helping stakeholders refine disease management strategies, optimize resources, and enhance resilience against environmental and pathogenic challenges. In summary, this report underscores the potential of diverse machine learning algorithms in advancing plant disease recognition, aiming to support effective and sustainable agricultural practices.