Plant Disease Identification from Leaf Images using Deep CNN's EfficientNet

Detecting plant sickness is urgent according to the viewpoint of agribusiness, as illnesses frequently limit plants' creation limit and will be helpful for phytopathologist. Notwithstanding, it is challenging, regularly worldly, and tedious to perceive plant sicknesses with manual methodologies. All things considered, the more significant part of the proposed models can analyze the diseases of a particular plant. Regardless of multi-class grouping, the model utilizes a multilabel format system to recognize both the plant and their disease. To examine and evaluate, we have made our own data set from different online resources that consisted leaf pictures of ten plants. In our analysis, we compared various famous CNN designs. The exploratory outcomes approve that the EfficientNet performs better in multilabel plant illness classification tasks. Additionally, we have tracked down that a CNN architecture, skip associations, spatial convolutions, and more limited secret layer network impact better outcomes in plant illness characterization.