Machine Learning Based Models to Forecast in-Season Cotton Growth.

In the recent past, there have been many studies on forecasting the yield of cotton crops taking the weather and geographical information of the management zones into account. However, there is a lot of space for predicting the yield based on in-season forecasting of growth parameters like canopy cover, canopy height and Excessive Green Index. In this study, the data is recorded for the first few weeks of cultivation and then, transfer learning approaches as well as Deep Learning based models like LSTM and traditional time-series approaches like ARIMA models are used to predict the growth parameters at a future date.