Efficient plant disease detection using reinforced coati optimization algorithm (RCOA) for Precision Agriculture

Abstract

Recent advancement in Artificial Intelligence (AI) have greatly improved their application in agriculture, especially in the identification of plant leaf diseases and in facilitating enhanced decision-making. Detection of plant leaf diseases plays important role for placing crop healthy and assisting farmers to take action on time. However, despite these improvements, they are still challenging to use in the real world. The process can be difficult to analyze plant leaf images because they often have complicated background and different structural patterns. Differences in light, texture, and the way leaves naturally change make things even more complicated, making it hard for automated detection systems to be reliable. Therefore, the paper introduces novel method as Reinforced Coati Optimization Algorithm (RCOA) for determining useful selection of features from a huge set of feature set generated from feature extraction methods. The RCOA algorithm is tested on CEC 2017 Benchmark function suite. Further, the model is trained using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The outcome depicts that proposed algorithm is providing better outcomes on comparative analysis with state-of-the-art methods.

Code and data availability

The supplied article blocks describe an RCOA feature-selection pipeline for plant leaf disease classification using SVM/MLP on Apple, Cherry, Cranberry, and Grapes datasets, but contain no data availability statement, no public dataset or image deposit, no author code repository or URL, and no trained model release. No

No evidence-backed public reproduction asset is currently recorded.