A Deep Convolutional Neural Network for Leaf Disease Detection of Sugarcane

Agriculture, which is closely related to the four pillars of society, is the industry that has the biggest impact on the economic development of a country. The most promising crop in India's vast array of agricultural resources is sugarcane. However, there are other difficulties that a farmer must overcome when raising these crops, such as crop illnesses, leaf problems, etc. These problems can be solved using the interesting technology known as deep learning. In order to identify and detect sugarcane leaf diseases with the highest level of accuracy feasible, this research effort integrates deep learning models. For this, a customized dataset made up of four classes has been used. The proposed modified VGG16 model was trained on an NVIDIA DGX server with RMSProp optimizer to reach a maximum accuracy of 96.50%. With the help of this research, farmers will be able to successfully combine artificial intelligence with their superior skill set to produce healthier, richer crops without worrying about these difficulties.