Enhanced Machine Learning Techniques for Pest Control and Leaf Disease Identification

Sujatha Kesavan EEE Department, Dr. MGR Educational and Research Institute, Chennai, India Kalaivani Anbarasan Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical & Technical Sciences Chennai, Tamil Nadu 602105, India Tamilselvi Chandrasekharan Department of Information Technology, Dr. MGR Educational and Research Institute, Chennai, India Dahlia Sam Department of Information Technology, Dr. MGR Educational and Research Institute, Chennai, India Nalinashini Ganesamoorthi Department of EIE, R. M. D. Engineering College, Chennai, India Kamatchi Chandrasekar Department of Biotechnology, The Oxford College of Science, Chennai, India Krishna Kumar Ramaraj Department of EEE, School of Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, India Nallamilli Pushpa Ganga Bhavani Department of Electronics and Communications Engineering, Saveetha School of Engineering Chennai, Tamil Nadu, India Srividhya Veerabathran Department of EEE, Meenakshi Engineering College Chennai, Tamil Nadu 600078, India B. Rengammal Sankari EEE Department, Dr. MGR Educational and Research Institute, Chennai, India Gujjula Jhansi Department of EEE, Dr. MGR Educational and Research Institute, Chennai, India Enhanced Machine Learning Techniques for Pest Control and Leaf Disease Identification

The agricultural sector has become an important income source for our country. In terms of nutrient absorption, plant diseases affecting the agricultural yield are creating a great hazard. In agriculture, recognizing infectious plants seems challenging due to the premise of the needed infrastructure. To prevent the spread of diseases, the identification of infectious leaves in the plant is observed to be a necessary step. This work aims to propose a machine learning technique on the ANN method for plant diseases identification and classification. This paper proposes a novel hybrid algorithm, called Black Widow Optimization Algorithm with Mayfly Optimization Algorithm (BWO-MA), for solving global optimization problems. In this paper, a BWO-MA with Artificial Neural Networks (ANN) based diagnostic model for earlier diagnosis of plant diseases is developed. Comparison has been done with existing machine learning methods with the proposed BWO-MA-based ANN architecture to accommodate greater performance. The comprehensive analysis showed that our proposal achieved splendid state-of-the-art performance.
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