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

Abstract

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.

Trait‐dependent plasticity erodes rapidly with repeated intergenerational acclimation in an invasive agricultural pest

“…Furthermore, this study indicates potential improvements on existing heat‐based phytosanitary disinfestation protocols, especially on pests like P. truncatus , that are hard to control with conventional synthetic pesticides. The study points to opportunities for pest physical control options through cooling or heating, for example, by solarization to suppress beetle populations, especially with increased warming (see also discussions in Abdelsamea et al, 2023; Fields et al, 1992).…”

Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest

“…Sitophilus oryzae and Sitophilus zeamais are the two primary insect pests that infest stored grain globally, according to Yang et al [108]. Because of their similar appearances, it can be difficult to identify the two pests quickly and accurately.…”

“…The information gathered in this review contributes positively to the entire grain production chain, from the application of remote sensing in the field to the post-harvest stages (Figure 7). The remote sensing associated with the techniques of monitoring and prediction of results helps in a precise way the management of agricultural properties, increasing the chances of hits at the time of taking decisions [107,108]. The present work reviewed grain storage strategies focusing on different environmental factors and measurable variables, and how current sensor and computational technologies improve the indirect predictive monitoring of grain quality.…”

“…This scheme is based on the systematic review and focuses on various environmental factors and measurable variables. Based on novel studies [107,108,[115][116][117][118][119][120][121][122][123][124][125][126][127][128][129][130], Figure 8 was created in order to calculate the equilibrium moisture content and dry matter loss of the grain mass during transportation, drying, and storage as well as to predict the physical and physicochemical changes in the grains, we suggested that technology and devices be developed to obtain intergranular temperature, relative humidity, and CO 2 concentration in real time (Figure 8).…”

The Effect of Solar Heating on Lesser Grain Borer, Rhyzopertha dominica (Fab.) (Coleoptera: Bostrichidae)

“…It should be noted that the study aims to use multi-use night learning to predict temperatures, which is the best lethal model for warehouse insects. In the filtration of insects inside transparent polyethylene packaging bags, this makes data available on the optimal conditions for the elimination of warehouse insects by using solar [15]. The results about highly temperature showed that pupae is the most temperature-tolerant insect phase.…”