Abstract
Detection and control of plant diseases is critical to maintaining global food security. Recent advances in deep learning and computer vision have revolutionized precision agriculture, especially in automatic detection of crop diseases. This research aims to further advance this new trend using deep learning techniques. It focuses specifically on the use of convolutional neural networks (CNN), specifically the VGG19 architecture, for the accurate and efficient detection of agricultural diseases. The study utilized a large database containing numerous photographs of healthy and diseased plants. Adding this information increases the power and capabilities of the model. The VGG19 architecture is based on algorithms that use transfer learning techniques to extract complex information from images.
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