AI-IoT driven system for agricultural pest outbreak risk prediction

Abstract Invasive pests pose significant threat to agricultural production, specifically, Maize crops production, with severe implications for food security in many regions. Therefore, timely detection of pest development stages and accurate prediction of potential outbreak risks are essential for effective pest management. This study introduces a hybrid model, integrating Explainable Artificial Intelligence (XAI), a lightweight Convolutional Neural Network (CNN), and Fuzzy Logic (FL) for Fall Armyworm (FAW) pest detection and weather-based outbreak risk prediction. The model leverages a Lightweight CNN model “Tiny-MobileNet-SE” for image classification, XAI model based on Grad-CAM to provide transparency and interpretability of predicted image, enabling users to understand the decision-making process, as well as FL inference with environmental parameters including Temperature, Humidity, and Rainfall to predict FAW pest outbreak risks. The Tiny-MobileNet-SE model achieved impressive results of 98.6% accuracy, 98.5% F1-score, 98.6% Recall, 0.72 MB size, and 80 ms when deployed on Raspberry pi 5, outperforming state- of-the-art lightweight models including EfficientNetB0, Squeezenet, MobileNet_v2, MobileNet_v3, and ShuffleNet tested on the same settings, making it suitable for edge deployment. The proposed system offers a power efficient, scalable, and user-friendly solution for precision agriculture, providing actionable insights for pest management and contributing to sustainable crop protection strategies.