From Pixels to Protection: Deep Learning Approaches for Plant Leaf Disease Detection

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Plants are essential for human survival. However, diseases affecting plant leaves can lead to significant reductions in crop yield and economic losses. Detecting these diseases early is crucial in agriculture. To overcome these limitations, machine learning has been employed to automate the identification of plant leaf diseases. By analysing features such as colour, intensity, and shape, machine learning models classify diseases into specific categories, offering faster and more accurate results than conventional approaches. Various ML techniques are used to identify diseases in plant leaves, with deep learning gaining attention for its ability to automate learning and perform advanced feature extraction. CNNs have become a highly effective tool for plant leaf disease identification, thanks to their ability to automatically extract features from images and achieve high classification accuracy. Their hierarchical structure enables them to detect simple patterns in initial layers and progressively learn more complex features in deeper layers, capturing the intricate details of disease symptoms. Additionally, CNNs can process large datasets and classify multiple diseases accurately, even with limited labelled data, by leveraging pre-trained models through transfer learning.