Long-Term Cotton Node Count Prediction Using Feature Selection, Data Augmentation, and Multivariate Time-Series Forecasting

In this paper, we present an approach to performing long-term cotton node count prediction using feature selection, data augmentation, and forecasting using a multivariate long short-term memory (LSTM) model. Specifically, we used in situ measurement data that was collected from a cotton research field from Tifton, GA, USA, to perform feature selection to select the most important input measurements and enable random data generation using Gaussian distribution to increase the size of our dataset. We concatenated the generated data to create longer, usable time-series and trained a multivariate LSTM model to predict average cotton node count. Our model’s prediction results on both the training and testing data had a low RMSE of less than 3, low MAE of less than 2.5, and high R2 score of at least 0.85. Our model also showed promise in accurately forecasting cotton node count in subsequent seasons via transfer learning. In particular, our transfer learned model maintained a low RMSE of less than 7.5 and MAE of less than 3.5, despite the subsequent season data having a shorter temporal scale. Moreover, we validated our results by performing hypothesis testing against a similar time-series forecasting model, namely, the gated recurrent unit (GRU) model.