Accurate cotton yield prediction is crucial for agricultural production management, resource optimization, and market supply–demand balance. However, achieving high-precision cotton yield prediction faces significant challenges mainly because cotton growth is influenced by complex, nonlinear environmental factors. Traditional machine learning models struggle to fully capture these complex factors, and deep learning models typically rely on large amounts of high-quality data. The high cost of obtaining field measurement data leads to a scarcity of high-quality datasets, further limiting the performance of prediction models. To overcome these challenges, this study proposes a novel cotton yield prediction architecture—Gaussian distribution data augmentation and variational autoencoder (GD-VAE). This architecture’s configuration offers the following advantages: (1) it calculates the mean and covariance of existing data, with new samples conforming to the original data distribution being sampled and generated to effectively expand the training dataset by utilizing Gaussian distribution data; (2) it uses an end-to-end variational autoencoder (VAE) that automatically learns the low-dimensional, compact, and discriminative feature representations of the input data. Specifically, GD-VAE uses a Gaussian distribution to model the original cotton yield data and generates augmented data through sampling. The VAE then learns deep feature representations from these data, which are fed into a regressor for final yield prediction. To evaluate the performance of GD-VAE, we conducted extensive tests under challenging cross-year and cross-district conditions. In the cross-year test in Bahawalnagar, Pakistan, GD-VAE achieved a root mean square error (RMSE) of 58.4 lbs/acre, a mean absolute error (MAE) of 38.19 lbs/acre, and a coefficient of determination (R2) of 0.65 between the actual and predicted yields. In the more challenging cross-year and cross-district test in Turkey, GD-VAE achieved an RMSE of 46.46 kg/da, an MAE of 37.74 kg/da, and an R2 of 0.14. The results indicate that the GD-VAE architecture significantly improves the accuracy of cotton yield prediction under limited data conditions through effective data augmentation and deep feature learning. This research provides an effective technical means for predicting challenges in agriculture with limited samples, which has important practical significance for ensuring global food security and sustainable agricultural development (to enhance analytical tractability, we use each district’s value by converting kg/ha to 1 lbs/acre, with 1.121 kg/ha converting to 1 kg/da, which is equivalent to 10 kg/ha).
Lan et al. (Thu,) studied this question.