School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7BD, UK.
Faculty of Computers and Information, Assiut University, Assiut, 71515, Egypt.
Sci Rep. 2023 Feb 14;13(1):2655. doi: 10.1038/s41598-023-29594-w.
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.
本研究旨在探讨利用透明聚乙烯袋对玉米象（Sitophilus oryzae）（一种对小麦等多种战略谷物具有极强破坏力的最具破坏性的害虫之一）进行太阳能加热的效果。本文旨在找到影响储粮昆虫控制加热系统的关键参数，同时确保小麦谷物质量得以维持。我们提供了一个新的基准数据集，该数据集的实验和环境数据是基于加拿大夏季的实地工作收集的。我们使用一种新的公式来衡量解决方案对玉米象的恒温效果，从而评估解决方案的有效性。我们采用不同的机器学习模型来预测我们的解决方案达到害虫致死加热条件的效果，从而衡量参数的重要性。通过 10 折交叉验证验证了我们的机器学习模型的性能，随机森林模型的准确率达到 99.5%，召回率为 99.01%，精度为 100%，F1-Score 为 99.5%。我们在机器学习方面的实验研究，结合 SHAP 值作为可解释的事后模型，提供了在高环境温度下使用太阳能袋对玉米象进行热灭虫的最佳环境条件和参数。我们的研究结果表明，在高环境温度下使用太阳能袋进行热灭虫时，存在一个最佳的中等谷物量。机器学习为我们提供了一个灵活的模型，可预测对透明塑料袋内储存的谷物害虫最有效的致死温度。利用这项强大的技术，我们可以获得有关消除这些害虫的最佳条件的有价值信息。我们的模型可以预测在使用热控制对昆虫进行处理时，某些参数组合是否有效。我们在知识共享许可下将数据集公开，以鼓励研究人员将其用作基准进行研究。
