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Machine Learning Time Series Models for Tea Pest Looper Infestation in Assam, India
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Author(s): DwijendraNath Dwivedi (Krakow University of Economics, Poland), Pradish N. Kapur (PES University, India)and Nipun N. Kapur (Indian Institute of Technology, Roorkee, India)
Copyright: 2023
Pages: 10
Source title:
Convergence of Cloud Computing, AI, and Agricultural Science
Source Author(s)/Editor(s): Avinash Kumar Sharma (ABES Institute of Technology, India), Nitin Chanderwal (University of Cincinnati, USA)and Rijwan Khan (Galgotias University, India)
DOI: 10.4018/979-8-3693-0200-2.ch014
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Abstract
In the agriculture industry, pest infestation is a significant challenge that is complicated by the nonlinear relationship with environmental factors. Given the effectiveness of machine learning models in simulating such complex nonlinear phenomena, the authors opted to employ them in the modelling of the life cycle of tea pests, which impact several other crops as well. Accordingly, multiple machine learning models were developed to forecast the occurrence of tea pest looper infestations. They utilized data for just two readily available parameters—temperature and rainfall—to investigate whether predictive models of good quality can be created even with limited data, particularly for small tea growers. After analyzing the various models generated, they discovered that neural network models can produce accurate predictions even with a restricted data set. Therefore, they are optimistic that new age technologies such as machine learning can benefit many small farmers in India who lack access to various technologies and, as a result, have limited data.
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