An Ensemble Machine Learning-Based Data-Centric Framework for Agrochemical Optimization in Precision Agriculture for Sustainable Farming

Abstract

Precision agriculture is an important application scenario for chemical engineering to carry out the clean production and sustainable resource use. In this paper, we propose an explainable data-centric ensemble machine learning framework for the optimization of agrochemical (fertilizer and pesticide) inputs based on utilizing their input efficiency and reducing environmental pollution based on chemical engineering aspects. We utilize the multiple source agricultural data (soil N, P, K, pH, temperature, humidity and rainfall) to make it possible for the precise application and site-specific planting adaptation of agrochemicals. Integrating three models including Logistic Regression, Support Vector Machine, Decision Tree, we utilize the Voting Classifier and Stochastic Gradient Boosting (SGB) to implement the classification. Together with controlled noise addition and cross validation that utilize data-centric methods, the Voting Classifier performs the best accuracy 90.7% with perfect score balance between precision, recall and F1-score. SHAPley Additive ExPlanations (SHAP) and permutation feature importance methods are adopted for model interpretation and illustrate the dominant features are rainfall, humidity and nitrogen that are consistent with agricultural chemical transport and nutrient conversion process. The framework can be further used for VRT systems to realize automated and quantitative inputs and reduce input of fertilizer and pesticides; soil and water pollution is limited, resource use efficiency is high. A generalized, interpretable and engineering-practical framework is proposed which is important for the chemical engineering application of clean production in precision agriculture.