Ladies Finger Leaf Disease Detection using CNN

Early identification and diagnosis of ladies finger plant diseases using leaf pictures is a critical and difficult research issue in agriculture. Such research investigations are critical in India, where agriculture is one of the key sources of revenue and accounts for 17 percent of the Gross Domestic Product (GDP). Using efficient and better crop products may boost both farmer profits and the country's GDP. This paper presents an in-depth overview of the multiple research endeavors conducted in the field of plant disease detection utilising cutting-edge, hand-crafted features- and deep-learning-based methodologies. Here, handmade feature-based approaches are used to solve the problems of recognising diseases in ladies finger plants. The adoption of deep learning-based methodology eliminates the issues produced by manual feature-based approaches. This study reveals how deep learning models outperform models constructed using manually defined criteria in plant disease diagnostic research. Deep learning-based algorithms deliver meaningful accuracy rates on a certain dataset, but when the system is assessed in real-world circumstances or on various datasets, the model's performance may be dramatically decreased. This research study discusses about some of the difficulties that must be overcome in order to properly identify leaf diseases in ladies finger plants.