Food supply must be raised in accordance with the rise in world population while also safeguarding crops from a number of lethal illnesses. Plant pathologists and farmers have historically used experience-based research to identify plant diseases with their unaided eyes. Traditional methods are challenging, time-consuming, and occasionally produce erroneous diagnoses, which causes major financial loss in the agriculture. Later, a number of research used machine learning to identify plant diseases, but the results were unimpressive and the method was too slow for everyday usage. Convolution Neural Networks have recently achieved a significant success in the field of computer vision because of its advantages over machine learning, such as automated feature extraction and the ability to provide efficient results quickly with little datasets. This study covers the difficulties in diagnosing plant leaf diseases and attempts to address the issue of incorrect and time-consuming disease detection and classification by analysing several approaches and cutting-edge algorithms that are attempting to address this problem
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