Effects of wind speed and wind direction on crop yield forecasting using dynamic time warping and an ensembled learning model

The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using pro fi cient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, speci fi cally focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R 2 score of 0.847 and the LSTM-RF model without these two key features R 2 score of (0.74). Both models were evaluated using the augmented Dickey -Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of con fi dence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R 2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW degrees ), Jaman South recorded (274.8 SW degrees ), and Wenchi recorded (272.6 SW degrees ). The DTW similarity distance for the annual average wind speed across these regions fell within speci fi c ranges: Jaman North (+/- 25.72), Jaman South (+/- 25.89), and Wenchi (+/- 26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential ef fi ciency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these fi ndings emphasize the signi fi cance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.