IDENTIFICATION OF PLANT DISEASES THROUGH MACHINE LEARNING TECHNIQUES

Abstract

Abstract- The identification of plant diseases through machine learning techniques is becoming increasingly crucial for sustainable agriculture. Traditional methods often rely on manual observation, which can be time-consuming and prone to errors. In this paper, we propose a novel approach for automated identification of plant diseases, focusing specifically on potato leaf plants. Leveraging advancements in machine learning, our methodology involves image processing and deep learning algorithms to accurately diagnose common diseases such as late blight, and early blight. We present the process of dataset collection, preprocessing techniques, and the architecture of our convolutional neural network (CNN) model. Evaluation results demonstrate the effectiveness of our approach in achieving high accuracy and reliability in disease identification. This research contributes to the development of efficient tools for precision agriculture, enabling farmers to promptly detect and mitigate crop diseases, thereby enhancing overall yield and sustainability in agricultural practices. Keywords: Potato leaf disease detection, CNN, Deep learning, image processing.