Honey yield prediction and neonicotinoid risk assessment utilizing a machine learning framework in smart agriculture.

Reduction in pollinator abundance (predominantly honeybees) stemming from environmental and chemical stressors notably neonicotinoid pesticides poses serious threats to biodiversity and agricultural productivity. This study presents a scalable machine learning framework to foresee honey yield and assess the impact of neonicotinoid exposure. Drawing upon a curated dataset, of 825 records encompassing 16 agro-environmental and chemical parameters including colony counts, yield metrics, and pesticide residue concentrations. Subsequently, we assessed 13 classification models, marked improvement was evident, as ensemble models consistently outperformed individual learners. Notably, our proposed voting classifier (AgriBuzzEnsemble), which synergistically fuses Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB), surpassed all baseline models, showcasing robust accuracy of 98%, a Matthews Correlation Coefficient (MCC) of 0.9751, a specificity of 0.9917, and an ROC AUC of 0.9985. Data preparation encompassed missing value imputation, outlier detection, feature scaling, and SMOTE based class balancing. For subsequent analysis, we employed Z-score filtering to detect and remove outliers, followed by a log1p transformation to mitigate skewness adhering to standard and well established preprocessing standards. Correlation analysis and statistical tests comprising McNemar's Test, ANOVA, and Tukey's HSD validated model reliability and confirmed the negative association between neonicotinoid burden and honey yield. Our proposed framework AgriBuzzEnsemble facilitates precision focused beekeeping by identifying yield risk zones and can be generalized to other regions facing pollinator stress, offering a robust and interpretable solution for sustainable agricultural planning.