Survey of Crop Yield Estimation Models with Emphasis on Artificial Neural Network Model.

This paper aims to provide a basis for the improvement of crop yield estimation research through a systematic review of previous work done on the subject. The review results, combined with other knowledge provide support for recommendations for future crop yield estimation research. In the review result a special emphasis is given on the Neural Network Model, which uses metrological inputs from the satellite, combined with other knowledge to provide support for recommendations for Neural Network for future crop yield estimation research. 1.0 INTRODUCTION The estimate of crop yield in early stage is useful for any nation for the plan its management strategies for providing food security to its peoples. This paper provides a crisp summery of the several classes of crop yield estimation models and techniques: Statistical model, Metrological model, simulation model, Agronomic Model and Synthetic model which includes artificial neural network method, gray systems approach, fuzzy mathematics, and systematic dynamics, etc. are introduced to the calculation of estimate models of crops yield in per unit area. A special emphasis is given on the Artificial Neural Network Model which is one of the most promising techniques for crop yield estimation in the future.