• M.Satheesh
• Mr. Dr.P.Venkateswara Rao

Automated plant disease detection using machine learning techniques plays a critical role in modern agriculture for ensuring timely intervention and improving crop yield. This research investigates the application of machine learning algorithms to predict Leaf diseases based on visible symptoms extracted from plant images. The study focuses on understanding the structure of leaf data and developing robust models capable of accurately classifying leaf health status. Visible indicators such as shape, size, and texture variations are utilized to train and evaluate machine learning models.A variety of algorithms, including decision trees, Naive Bayes, artificial neural networks, and random forest, are implemented and compared for their effectiveness in disease prediction. The research employs a dataset comprising labeled images of healthy and diseased plants to train and validate the models. Experimental results demonstrate promising accuracy levels, highlighting the potential of machine learning in revolutionizing leaf disease diagnosis and management practices. The outcomes of this research provide valuable insights for researchers and practitioners working in the field of precision agriculture and crop protectio

Leaf Detection and Analysis: Harnessing Image Processing Techniques

"Leaf Detection and Analysis: Harnessing Image Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.270-276, June-2024, Available :http://www.jetir.org/papers/JETIRGL06046.pdf

"Leaf Detection and Analysis: Harnessing Image Processing Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp270-276, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06046.pdf