Work

WT-CNN-BiLSTM: A Precise Rice Yield Prediction Method for Small-Scale Greenhouse Planting on the Yunnan Plateau

Year: 2025

Type: article

Abstract: Multispectral technology and deep learning are widely used in field crop yield prediction. Existing studies mainly focus on large-scale estimation in plain regions, while integrated applications for small-scale plateau plots are rarely reported. To solve this problem, this study proposes a WT-CNN-BiLSTM hybrid model that integrates UAV-borne multispectral imagery and deep learning for rice yield prediction in small-scale greenhouses on the Yunnan Plateau. Initially, a rice dataset covering five drip irrigation levels was constructed, including vegetation index images of rice throughout its entire growth cycle and yield data from 500 sub-plots. After data augmentation (image rotation, flipping, and yield augmentation with Gaussian noise), the dataset was expanded to 2000 sub-plots. Then, with CNN-LSTM as the baseline, four vegetation indices (NDVI, NDRE, OSAVI, and RECI) were compared, and RECI-Yield was determined as the optimal input dataset. Finally, the convolutional layers in the first residual block of ResNet50 were replaced with WTConv to enhance multi-frequency feature extraction; the extracted features were then input into BiLSTM to capture the long-term growth trends of rice, resulting in the development of the WT-CNN-BiLSTM model. Experimental results showed that in small-scale greenhouses on the Yunnan Plateau, the model achieved the best prediction performance under the 50% drip irrigation level (R2 = 0.91). Moreover, the prediction performance based on the merged dataset of all irrigation levels was even better (RMSE = 9.68 g, MAPE = 11.41%, R2 = 0.92), which was significantly superior to comparative models such as CNN-LSTM, CNN-BiLSTM, and CNN-GRU, as well as the prediction results under single irrigation levels. Cross-validation based on the RECI-Yield-VT dataset (RMSE = 8.07 g, MAPE = 9.22%, R2 = 0.94) further confirmed its generalization ability, enabling its effective application to rice yield prediction in small-scale greenhouse scenarios on the Yunnan Plateau.

Source: Agronomy

DOI: 10.3390/agronomy15102256