An Intelligent Approach for Early Detection of Potato Diseases in Desert Agriculture

The agricultural sector constantly seeks to strengthen and develop its systems. As with other fields, the agricultural sector has recently relied on artificial intelligence technologies to process agricultural data for achieving high-quality crop production. In regions as diverse as Europe, North America, and East Asia, deep learning techniques have been used to detect plant diseases, determine their causes, and even predict crop yields in specific seasons. This research is concerned with applying these techniques in the desert environment because they are completely different in terms of infertile soil quality, drought, high water salinity, extreme temperatures, etc. To solve the problem of the degree of progression of potato leaf disease, we use previously developed models based on convolutional neural networks. The present paper focuses on creating an application to identify and classify potato leaf diseases using a dataset specially collected from a local desert environment.