Computer aided detection of leaf disease in agriculture using convolution neural network based squeeze and excitation network

The support rendered by artificial intelligence in plant disease diagnosis and with drastic progression in the agricultural technology, it is necessary to do pertinent research for the cause of long-term agricultural development. Numerous diseases like early and late blight have a significant influence on the quality and quantity of potatoes. Manual interpretation turns out to be a time-consuming process in sorting out leaf diseases. In order to classify various diseases like fungal, viral and bacterial infections in the potato leaf, an enhanced Convolution Neural Network based on VGG16 is used for potato leaf disease classification. Improved Median filter is also used which eradicates the noise to a greater extent. The convolution layers of VGG16 along with the Inception and the SE block are used in this research for classification. The global average pooling layer is used to reduce model training parameters, layer and Squeeze and Excitation Network attention mechanism is used to improve the model's ability to extract features. The approximate calculations can be done by using soft computing. Compared with other traditional convolutional neural networks, the proposed model achieved the highest classification accuracy of 99.3%