Artificial Neural Network (ANN) for Predicting Crop Yield Based on Remotely Detected Crop Metrics

It is becoming increasingly well-known to use remote sensing for regional crop research. One of the hardest things for researchers utilizing remote sensing to do is anticipate and map crop yield at scales other than the field scale. In this project, the effective creation of a scientific model utilizing neural network to forecast crop productivity on a regional level utilizing wellknown feed forward and techniques for back-propagation use crop parameters that are received by remote sensing. The Meals In order to design and calibrate the Forward Back Propagating Neural Network (FFBPNN) model, Ground truth data and parameters were acquired by remote sensing in a Mat lab setting. The instance provided reliable and precise outcomes. The performance of the suggested model was statistically tested using the coefficient of determination, root mean squared error; mean absolute error, average ratio of anticipated yield to goal crop production and relative error. This project also looked at how the amount of buried neurons affected the model's functionality. Statistical investigation confirmed the applicability of the developed ANN model to parameters for paddy yield estimation based on remote sensing.