Plant Disease Detection Using Deep Learning

In the lives of humans, agriculture is incredibly vital. Nearly 60% of the population is either directly or indirectly involved in agriculture. Farmers are not interested in using the traditional approach because there are no tools to identify diseases in different crops in an agricultural environment, daily raising the output of their agriculture. Early diagnosis of crop diseases is essential since they influence the growth of their species. Crop disease detection and classification has traditionally relied on a variety of Machine Learning (ML) models, and developments in a type of ML called Deep Learning (DL), this field of study seems to offer a lot of promise for increased accuracy. The suggested method effectively and precisely recognizes the symptoms of crop disease using a CNN and a deep neural network. Additionally, these solutions are evaluated using a variety of efficiency indicators. The DL models that are used to visualize agricultural diseases are fully described in this article. Also, some research holes are found that can provide more clarity for identifying plant diseases even before symptoms appear. Convolution neural network-based technology will be developed using the suggested methodology to identify plant leaf disease