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

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.

Dataset of thermal behaviour and weather data of thermal disinfestation of Sitophilus oryzae in plastic bags using solar heating.

This dataset is related to another research paper "Fawki, S., Fields, P.G., Jian, F., Yousery, A., 2022. Control of Sitophilus oryzae (Coleoptera: Curculionidae) in bags of wheat using solar radiation. Journal of Stored Products Research 96, 101941. doi:10.1016/j.jspr.2022.101941." The data was collected in Canada and Egypt. In Canada, Clear polyethylene bags of wheat were used for thermal control using solar radiation. There were four treatments of different wheat amounts, 16, 21, and 25 kg inside clear bags in wood boxes and another 21 kg of wheat in a plastic bag not in a wood box. The solar heating for all treatments was investigated in the field under two different conditions. First, the temperature profile inside the bags was recorded every morning, and the grains were mixed and stacked in foam boxes during the night over five days. Second, the temperature profile was recorded continuously during day and night over six days. Different weather data: ambient temperature, solar radiation, wind direction, and wind speed were collected during both experiments using a weather station located on the field. In Egypt, clear plastic bags and woven plastic bags with 16 kg of wheat were used for solar heating over 5 d. We present the temperature profile data inside the plastic polyethene bags under different storage conditions, grain amounts, bags materials and different weather conditions, which will allow other researchers to develop models to understand the thermal behaviour for thermal disinfestation. Solar heating is a very promising disinfestation technique that was successfully used for museum pest control, postharvest pest control, and soil disinfestation.