Soil Whisperer 2.0: Bio-Inspired Neural Networks for Predictive Irrigation based on Soil-Plant Feedback Loops

Water scarcity and inefficient irrigation practices are paramount issues that impair sustainable agriculture. Machine Learning (ML) has fast emerged as an important player in precision agriculture, customarily targeting crop irrigation stipulations through prediction models. Although conventional ML models are mainly directed at predicting moisture level or yield, there is insufficient integrated consideration of how to balance water efficiency with crop productivity. To fill this gap, our research proposes a hybrid ensemble-based predictive model-enabled Random forest, XGBoost, and LightGBM with a novel custom loss function that minimizes yield deviation and excessive water consumption. The loss function aims to optimize irrigation scheduling by minimizing the discrepancy between actual vs. predicted crop yield and water used vs. optimal water required, thereby forming a multi-objective optimization platform. Based on our experimental results, the proposed method proved superior to traditional moisture threshold and single model approaches, in achieving better water conservation and yield stability. The research contributes to the development of AI-driven irrigation management towards a sustainable solution for optimizing water utility under elevated agricultural productivity. Experimental outcomes show that the new model is superior to baseline single models with 12.3% RMSE reduction, 15.7% MAE improvement, and 9.6% accuracy boost for optimal irrigation prediction. The loss function with custom design showed improved control of the trade-off between under-irrigation and over-irrigation situations. These results confirm the applicability of the model in real-time precision farming environments