Future Farming: Advancing Agriculture with Artificial Intelligence

Enhanced Machine Learning Techniques for Pest Control and Leaf Disease Identification

Author(s): Sujatha Kesavan*, Kalaivani Anbarasan, Tamilselvi Chandrasekharan, Dahlia Sam, Nalinashini Ganesamoorthi, Kamatchi Chandrasekar, Krishna Kumar Ramaraj, Nallamilli Pushpa Ganga Bhavani, Srividhya Veerabathran, B. Rengammal Sankari and Gujjula Jhansi

Pp: 1-22 (22)

DOI: 10.2174/9789815124729123010004

* (Excluding Mailing and Handling)


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. 

Keywords: Artificial Neural Networks (ANN), Hybrid black widow optimization algorithm with mayfly optimization algorithm (BWO-MA), Improved canny algorithm, Median filtering, Plant disease.

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