Pest Infestation Prediction System for Stored Beans Based on Machine Learning and IoT Sensors Strategy
DOI:
https://doi.org/10.33003/fjs-2026-1015-5866Keywords:
Buzzer, IoT Sensors, Machine Learning, Pest Infestation, Stored Beans, WhatsApp NotificationAbstract
Beans, though highly nutritious, are vulnerable to postharvest pest attacks, leading to significant food and economic losses. The traditional methods of mitigating the loss to pest attacks is tedious besides, efficiency becomes issues especially to climate factors. State of the art approaches such as Machine Learning techniques have been employed to deal with the challenges but not without room for improvement. Therefore, this study aim to design and implement a sensor-driven system capable of monitoring environmental conditions and predicting infestation risks in real time based on machine learning models. The system integrated three sensors: a DHT11 for temperature and humidity measurement, a soil moisture module for monitoring moisture content, and an ESP32 microcontroller as the processing unit. Data collected were processed and analyzed using a multivariate linear regression model, which established the relationship between temperature, humidity, and moisture content, and the probability of pest infestation. Whenever environmental conditions exceeded safe thresholds, the system triggered alerts through a buzzer, LED, and WhatsApp notification to the farmer. Performance evaluation of the system demonstrated strong predictive performance. The model achieved an accuracy of 92.4%, precision of 89.1%, recall of 93.6%, and an F1-score of 91.3%.These results indicate that the system is promising as would reliably predict infestation risks while minimizing false alarms.Thus, the developed system provides a practical, low-cost, and efficient solution for farmers to proactively manage bean storage conditions.This is by combining IoT sensors with machine learning for real-time notifications, as it has contributes to reducing postharvest losses and enhancing food security
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Copyright (c) 2026 Oluwaseun Adeniyi Ojerinde, Enesi Femi Aminu, Ayobami Ekundayo, Alfa Joshua Paul, Idris Mohammed Kolo
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