Published Paper Details:

One of the most significant elements that pose a significant risk to agricultural productivity is the presence of leaf diseases. Finding and naming diseases and pests as soon as they appear is one of the most effective ways to cut down on the financial damage they incur on the farmer. In this study, a convolutional neural network was utilized to automatically detect illnesses that might affect crops. Here we have taken an image dataset containing more than 20,000 images. Training is carried out with the help of the Inception-ResNet-v2 model. The direct edge in the cross-layer and the multi-layer convolution in the residual network unit of the model. Following the completion of the combined convolution process, it is triggered by the connection into the ReLu function. The experimental results suggest that this model achieves an overall recognition accuracy of 98.0%, which substantiates the claim that it is successful. Following the completion of this model's training, we developed and deployed the UI of agricultural disease and insect pest identification. After that, we began the real testing process. The findings demonstrate that the system is capable of correctly identifying crop illnesses so that the farmer can choose to suitable method to overcome the crop from the identified disease.