Forecasting maize yield from growth parameters using machine learning in a biochar-inorganic fertilizer amended soil under drip irrigation - Faloye, Oluwaseun Temitope; Ajayi, Ayodele Ebenezer; Kamchoom, Viroon; Sinsamutpadung, Natdanai; Adeyeri, Oluwafemi; Ogunwole, Joshua Olalekan (My University, 2025)
The combined application of biochar and inorganic fertilizers has demonstrated significant potential to enhance crop productivity under both rainfed and irrigated conditions. However, predictive modeling approaches utilizing machine learning (ML) to simulate field outcomes under diverse agronomic scenarios remain understudied. This study addresses two critical objectives: (i) to evaluate the efficacy of ML models—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Boosted Trees (BT)—in predicting maize grain yieldin biochar-inorganic fertizer amended soil under drip irrigation; and (ii) to identify the growth stage(s) and ML models that deliver the most accurate predictions. A three-year factorial field experiment was conducted during dry seasons, testing five biochar rates (0, 3, 6, 10 and 20 t/ha), two fertilizer levels (0 and 300 kg/ha), and deficit irrigation treatments (60%, 80%, and 100% of full irrigation). Growth parameters were measured at vegetative (35 days after planting – DAP), flowering stage (49 DAP), and maturity stage (77 DAP), with grain yield recorded at harvest (90 DAP). The measured growth parameters at the different DAP were used for the grain yield forecast. 70, 15 and 15% of the dataset were used for model training, validation and testing, respectively. Field results revealed progressive increases in growth parameters from vegetative to maturity stages, with treatment efficacy following the order: control < biochar-only < fertilizer-only < combined biochar-fertilizer. ML predictions mirrored this hierarchy, with ANN achieving superior accuracy (R² = 0.73–0.85, RMSE = 0.43–0.76, NRMSE = 0.095–0.17 at maturity) compared to SVM and BT. Predictive performance was weakest at the vegetative stage (35 DAP) but improved during flowering (49 DAP) and maturity (77 DAP), underscoring the importance of later growth data for reliable yield forecasting. This study demonstrates that ML models, particularly ANN, can effectively predict maize yield using accessible growth metrics, offering a cost- and labor-efficient complement to traditional field research. By enabling rapid scenario analysis, such models empower stakeholders to optimize resource allocation and inform crop management decisions under varying irrigation and soil amendment strategies.
Keywords: Artificial intelligence; Grain yield; Growth stages; Maize; Prediction; Soil amendment