Utilizing soil characteristics and hybrid machine learning for interpretable potato yield prediction: A study with satin-bowerbird optimization and deep neural network

Context Yield forecasting is crucial to the agricultural planning enterprise, such as input control, farm logistics and reduction of economic risks. The soils in the Maritime provinces of Canada have a great difference in their properties which affect the productivity of crops. Such variability requires a strong prediction model that could address the different characteristics of soil. Objective This research proposal is expected to establish a stable potato yield prediction model based on the soil property data of New Brunswick and Prince Edward Island and determine whether the application of optimization techniques with deep learning can enhance the prediction accuracy over the conventional machine learning approach. Methods Soil samples were taken at eight experimental sites in the 2017 and 2018 growing seasons, with 18 soil properties being captured. The feature selection techniques were used to create three input scenarios (Comb1, Comb2, Comb3). To optimize the selection of input variables, a hybrid prediction model, DNN-SBO (Deep Neural Network -Satin Bowerbird Optimization), was suggested and refined with the Boruta feature selection and Best Subset Regression-WASPAS. The performance of the model was tested in comparison with Kernel Ridge Regression (KRR), Elastic Net, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR), on the evaluation metrics of Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The model interpretability was done using SHAP (Shapley Additive exPlanation) analysis. Results and Conclusions Comb2 was the best input scenario that consisted of Total Base Saturation, Sulfur, Magnesium, Potash, Aluminum, Zinc, Phosphate, Manganese, Organic Matter, Iron, and Copper. DNN-SBO model had the best predictive power with R= 0.903 (train) and RMSE= 4.165 t/ha and MAPE= 6.766 % and R= 0.853(test) and RMSE= 5.522 t/ha and MAPE= 9.707 %. The SHAP analysis has shown that Iron was the most significant predictor (mean SHAP = +5.49), next was Copper, Zinc, Phosphorus, and Organic Matter. Significance The paper sheds light on the promise of deep learning that is based on bio-inspired optimization and feature selection techniques in order to achieve a significant increase in crop yield prediction. The findings can lead to the wider use of the similar methods in precision agriculture, which will result in smarter and data-driven farming in variably soiled areas.

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