Development of Crop Yield Estimation Model using Soil and Environmental Parameters
This addresses food security by providing accurate yield forecasts for tea farmers, though it is incremental as it applies existing ensemble neural network methods to a specific dataset.
The researchers developed a crop yield estimation model for tea farms in Pakistan using soil and environmental parameters, achieving an R-squared of 0.9461 and RMSE of 0.1204 to predict pre-harvest yield.
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.