Machine Learning for Adaptive Pest Management using Real-Time Data

In modern agriculture, effective pest control systems must balance efficacy with resource efficiency. Traditional pest management often relies on predetermined schedules and historical data, which may not align with current environmental conditions. This research explores the application of machine learning (ML) and real-time data analysis to develop dynamic pest control systems. We utilize algorithms such as Random Forest (RF), Neural Networks (NN), and Support Vector Machines (SVM) to predict pest infestations and guide control strategies. Input data includes weather conditions, soil moisture, and pest detection metrics collected through environmental sensors. Model performance was evaluated using accuracy, precision, recall, and predictive scores. The neural network model demonstrated the highest accuracy (91.3%), followed by RF (89.5%) and SVM (85.2%). Our findings highlight the significant role of temperature and humidity in predicting pest outbreaks. This study emphasizes the potential of real-time, data-driven pest control to reduce chemical usage, improve crop yields, and support sustainable agricultural practices.