Identification and Classification of Plant Disease

Agricultural sciences have long struggled with challenges such as plant diseases, declining crop yields, and the need for sustainable farming solutions. Accurate and rapid plant identification plays a crucial role in biodiversity monitoring, crop management, and early disease detection. However, traditional plant identification methods are often time consuming, prone to human error, and require expert knowledge. Deep learning, a branch of artificial intelligence, provides an effective solution to these challenges. By leveraging neural networks, deep learning can recognize complex patterns in plant images, enabling precise identification and classification based on features such as leaf shape, colour, and texture. Among deep learning models, Convolutional Neural Networks (CNNs) have proven particularly effective in automating feature extraction and classification processes, significantly enhancing plant recognition capabilities. This project explores the application of deep learning techniques to accurately classify plant species and detect diseases. By training a deep learning model on a dataset of plant images, the goal is to develop a tool that can assist farmers, botanists, and researchers in identifying plants and diagnosing diseases more efficiently. This could lead to improved agricultural productivity, better disease management, and enhanced biodiversity conservation.