Predicting Heat Treatment Duration for Pest Control Using Machine Learning on a Large-Scale Dataset
Pest control in industrial buildings, such as silos and storage facilities, is critical for maintaining food safety and economic stability. Traditional methods like fumigation face challenges, including insect resistance and environmental concerns, prompting the need for alternative approaches. Heat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/5/1254 |
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| Summary: | Pest control in industrial buildings, such as silos and storage facilities, is critical for maintaining food safety and economic stability. Traditional methods like fumigation face challenges, including insect resistance and environmental concerns, prompting the need for alternative approaches. Heat treatments have emerged as an effective and eco-friendly solution, but optimizing their duration and efficiency remains a challenge. This study leverages machine learning (ML) to predict the duration of heat treatments required for effective pest control in various industrial buildings. Using a dataset of 1423 heat treatment time series collected from IoT devices, we applied exploratory data analysis (EDA) and ML models, including random forest, XGBoost, ridge regression, and support vector regression (SVR), to predict the time needed to reach 50 °C, a critical threshold for pest mortality. Results revealed significant variations in treatment effectiveness based on building type, geographical location, and ambient temperature. XGBoost and random forest models outperformed others, achieving high predictive accuracy. The findings highlight the importance of tailored heat treatment protocols and the potential of data-driven approaches to optimize pest control strategies, reduce energy consumption, and improve operational efficiency in industrial settings. This study underscores the value of integrating IoT and ML for real-time monitoring and adaptive control in pest management. |
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| ISSN: | 2073-4395 |