“…Weather data was recorded during the experiment using a weather station at the experimental site. A detailed description of the experimental protocol and how the data were collected is described in the original article and in another data article related to the same work 14 , 30 . Figure 1 Experimental design: ( a ) Canada 1, after sun exposure the wheat bags were mixed and stacked in the foam boxes.…”
Section: Methodsmentioning
confidence: 99%
“…Then One-way ANOVA was conducted followed by the Tukey-Kramer HSD. DM model was estimated according to 14 , 30 to study the relationship between grain temperatures, exposure time to heat, and rice weevil mortality.…”
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.
“…Weather data was recorded during the experiment using a weather station at the experimental site. A detailed description of the experimental protocol and how the data were collected is described in the original article and in another data article related to the same work 14 , 30 . Figure 1 Experimental design: ( a ) Canada 1, after sun exposure the wheat bags were mixed and stacked in the foam boxes.…”
Section: Methodsmentioning
confidence: 99%
“…Then One-way ANOVA was conducted followed by the Tukey-Kramer HSD. DM model was estimated according to 14 , 30 to study the relationship between grain temperatures, exposure time to heat, and rice weevil mortality.…”
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.
The bioefficacy of several inert materials, such as Diatomaceous earth (DE), bentonite, and biochar enhanced with pirimiphos-methyl against Sitophilus oryzae, was used to evaluate the insecticidal activity of three new formulations using the technique of mixing with grain. DE was the most effective material (LC50 3.198 g/kg), followed by biochar LC50 3.709 g/kg, whereas bentonite was the least effective (LC50 3. 979 g/kg). The three materials were loaded with pirimiphos-methyl (0.5%). The formula of Pirimiphos-methyl with DE reported the highest significant effect compared to the other two formulations, with an LC50 value of 0.082 g/kg. DE formula had a co-toxicity coefficient value that was 6.46 times greater than pirimiphos-methyl, followed by the biochar formula, which was 1.74 times more effective, and the bentonite formula, which had a 0.69-fold increase in activity. In the laboratory examination of the wheat’s free and compact bulk densities after being treated with LC50s of each dust material, DE slightly affected wheat-free bulk density. Still, both bentonite and biochar reduced the free bulk density. However, all the tested materials decreased in compact bulk density. The germination percentages of wheat treated with LC50 values of DE and bentonite and biochar after 10 days of storage revealed no significant difference between treated and untreated seeds after 7 days of germination.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
