Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive the planet. Negative impacts are being caused agroecosystem due to rapid increase use of chemicals combat plant diseases. These include fungicides, bactericides, and insecticides. Both quantity quality output impacted when there is a high-scale prevalence diseases crops. Plant provide significant obstacle agricultural industry, which has negative impact growth plants The problem early detection diagnosis can be solved benefit farming community by employing method that both quick reliable regularly. This article proposes model leaf infection called Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, attains greater authenticity. design hybrid version Butter Fly optimization algorithm Optimization Algorithm incorporated into MALRL technique been proposed. In image, used determine acceptable aspects regions. After that, optimal characteristics aid Long Short Term Neural Network (LSTM) classifier speed up process lung disease categorization. experiment's findings analyzed compared those ANN, CNN, DNN. proposed was successful achieving high level accuracy detecting images healthy leaves comparison other conventional methods.