Crop Yield Prediction Based on Bacterial Biomarkers and Machine Learning

Bacteria serve as a holistic indicator of soil fertility by incorporating both biotic and abiotic aspects of past and present ecosystems. However, a research gap still exists in yield prediction models based on simple and reliable bacterial indicators. This study aims to explore whether machine learning, deep learning, and bacterial biomarker communities can be used to accurately predict crop yields. Soil moisture, nutrients, and bacterial community under different irrigation (I0, I1, I2) and fertilization (N0, N1, N2, N3, O1, O2, O3) treatments were measured using soil physicochemical properties analysis method and high-throughput sequencing approach to predict crop yield using Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Back Propagation Neural Network (BPNN) models. RF and XGBoost were superior in modeling yield, with R2 values of 0.813 and 0.818 and RMSE values of 969.420 kg ha–1 and 957.000 kg ha–1, respectively, outperforming BPNN (R2 = 0.541, RMSE = 1,519.680 kg ha–1). Soil organic carbon and bacterial biomarkers are most influential factors on yield with importance of 21.54