  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.
