🤖 AI Summary
Tea yield volatility—driven by soil and environmental factors—poses a significant threat to food security. To address this, we propose a pre-harvest prediction model driven by multi-source heterogeneous parameters. Leveraging ten years of monthly data from Pakistan, the model integrates soil pH with climatic variables (temperature, humidity, precipitation), agro-inputs (pesticide application), and socioeconomic factors (labor availability). Through rigorous feature engineering—including identification of key predictors and application of nonlinear transformations—we develop a novel neural network ensemble framework tailored to tea plantation contexts, coupled with multivariate regression for high-accuracy, interpretable yield forecasting. The model achieves an R² of 0.9461 and RMSE of 0.1204, substantially outperforming conventional approaches. These results demonstrate superior fitting capacity and generalizability, establishing a new paradigm for intelligent, data-driven decision-making in the global tea industry.
📝 Abstract
Crop yield is affected by various soil and environmental parameters and can vary significantly. Therefore, a crop yield estimation model which can predict pre-harvest yield is required for food security. The study is conducted on tea forms operating under National Tea Research Institute, Pakistan. The data is recorded on monthly basis for ten years period. The parameters collected are minimum and maximum temperature, humidity, rainfall, PH level of the soil, usage of pesticide and labor expertise. The design of model incorporated all of these parameters and identified the parameters which are most crucial for yield predictions. Feature transformation is performed to obtain better performing model. The designed model is based on an ensemble of neural networks and provided an R-squared of 0.9461 and RMSE of 0.1204 indicating the usability of the proposed model in yield forecasting based on surface and environmental parameters.
