Analyzing and Predicting Organic Pesticide Market Trends with Machine Learning

The increasing adoption of organic farming has amplified demand for organic pesticides, but predicting market trends remains challenging due to dynamic environmental, regulatory, and consumer influences. This study develops a machine learning (ML) framework to forecast organic pesticide sales, leveraging the USDA Agricultural Pesticide Sales Dataset (2010-2022), which includes 12,000+ entries on sales volumes, regional organic acreage, regulatory policies, and seasonal variables. To address prediction robustness, we implement Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM) models, selecting LSTM for its capacity to capture temporal dependencies in time-series data. The dataset was preprocessed using SMOTE to mitigate class imbalance and normalized to reduce feature skewness. Model performance was validated through 10-fold cross-validation and a 70-30 temporal split, with LSTM achieving superior accuracy: RMSE (18.76), MAPE (9.89%), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.94). Exploratory analysis identified organic acreage expansion (+0.82 correlation) and stringent environmental regulations as key drivers. By enabling precise, long-term market forecasts, this framework assists policymakers in optimizing subsidy allocations and promoting integrated pest management, directly supporting UN Sustainable Development Goals (SDGs) 2 and 12. This work advances ML applications in sustainable agriculture, demonstrating deep learning's efficacy in navigating nonlinear market dynamics while cautioning against overreliance on historical trends through hybrid forecasting approaches.