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Machine Learning Algorithms for Predictive Pest Modeling in Agricultural Crops
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Author(s): Ghulam Mustafa (Hohai University, China), Yuhong Liu (Hohai University, China), Hengbiao Zheng (Nanjing Agricultural University, China), Meng Zhou (Nanjing Agricultural University, China), Imran Haider Khan (Nanjing Agricultural University, China), Saeed Arshad (Nanjing Agricultural University, China), Iftikhar Ali (Agricultural Remote Sensing Lab-UAF, National Centre for GIS and Space Applications, Pakistan), Aqib Mehmood Khan (National Research Center of Intercropping, Pakistan)and Bakhshah Zib (Nanjing Agricultural University, China)
Copyright: 2024
Pages: 28
Source title:
Revolutionizing Pest Management for Sustainable Agriculture
Source Author(s)/Editor(s): Muhammad Zia Ul Haq (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan)and Iftikhar Ali (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan)
DOI: 10.4018/979-8-3693-3061-6.ch015
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Abstract
Food security and maximum yield depend on accurate pest prediction and crop management. An in-depth analysis of this cutting-edge area is the goal of this book chapter, which will explore predictive pest modeling using machine learning (ML) algorithms. The introduction establishes the section by stressing the significance of ML in transforming crop pest management and the value of predictive pest modeling. Furthermore, it will delve into various ML techniques designed for pest modeling. Differentiating between supervised, unsupervised, and semi-supervised learning techniques, it will outline a range of ML methods. Moreover, to help practitioners make an educated decision, it will also focus on the criteria for algorithm selection in pest prediction. It concludes with a detailed overview of ML algorithms' revolutionary potential in agricultural operations and their importance in predictive pest modeling.
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