A logarithmically amortising temperature effect for supervised learning of wheat solar disinfestation of rice weevil Sitophilus oryzae (Coleoptera: Curculionidae) using plastic bags

Abdelsamea, Mohammed M. and Gaber, Mohamed Medhat and Ali, Aliyuda and Kyriakou, Marios and Fawki, Shams (2023) A logarithmically amortising temperature effect for supervised learning of wheat solar disinfestation of rice weevil Sitophilus oryzae (Coleoptera: Curculionidae) using plastic bags. Scientific Reports, 13. p. 2655. ISSN 2045-2322

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This work investigates the effectiveness of solar heating using clear polyethylene bags against rice weevil Sitophilus oryzae (L.), which is one of the most destructive insect pests against many strategic grains such as wheat. In this paper, we aim at finding the key parameters that affect the control heating system against stored grain insects while ensuring that the wheat grain quality is maintained. We provide a new benchmark dataset, where the experimental and environmental data was collected based on fieldwork during the summer in Canada. We measure the effectiveness of the solution using a novel formula to describe the amortising temperature effect on rice weevil. We adopted different machine learning models to predict the effectiveness of our solution in reaching a lethal heating condition for insect pests, and hence measure the importance of the parameters. The performance of our machine learning models has been validated using a 10-fold cross-validation, showing a high accuracy of 99.5% with 99.01% recall, 100% precision and 99.5% F1-Score obtained by the Random Forest model. Our experimental study on machine learning with SHAP values as an eXplainable post-hoc model provides the best environmental conditions and parameters that have a significant effect on the disinfestation of rice weevils. Our findings suggest that there is an optimal medium-sized grain amount when using solar bags for thermal insect disinfestation under high ambient temperatures. Machine learning provides us with a versatile model for predicting the lethal temperatures that are most effective for eliminating stored grain insects inside clear plastic bags. Using this powerful technology, we can gain valuable information on the optimal conditions to eliminate these pests. Our model allows us to predict whether a certain combination of parameters will be effective in the treatment of insects using thermal control. We make our dataset publicly available under a Creative Commons Licence to encourage researchers to use it as a benchmark for their studies.

Item Type: Article
Identification Number: https://doi.org/10.1038/s41598-023-29594-w
7 February 2023Accepted
14 February 2023Published Online
Uncontrolled Keywords: computational methods, computational models, computer science, data acquisition, data mining, data processing, entomology, machine learning, scientific data
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Aliyuda Ali
Date Deposited: 17 Feb 2023 11:07
Last Modified: 17 Feb 2023 11:07
URI: https://www.open-access.bcu.ac.uk/id/eprint/14188

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