The effect of plant leaf disease on environment and detection of disease using convolutional neural network

Agriculture significantly influences people's daily lives and financial well-being, making it crucial to enhance crop productivity. Recognising and preventing plant diseases are vital in achieving this goal, as diseases can severely impact production and the environment. Traditional methods of disease detection, relying on human visual inspection, are time-consuming, expensive, and prone to errors. In contrast, employing image processing techniques and convolutional neural networks (CNNs) can offer fast and accurate results. This paper compares deep CNN, VGG19, and ResNet, to detect plant diseases from leaves images. We train these models on a dataset containing images of newly discovered plant diseases. The results demonstrate that the ResNet model outperforms the customised deep CNN and VGG models, achieving an impressive accuracy of 99.3% with the fewest parameters.