A Long Short-Term Memory Guided Conditional Dynamic Variational Auto-Encoder for Crop Yield Prediction and Crop Type Classification
Received: 6 June 2025 | Revised: 3 September 2025 and 30 September 2025 | Accepted: 5 October 2025 | Online: 27 October 2025
Corresponding author: Nandini Geddlehally Renukaradya
Abstract
Agriculture plays a significant role in India's economy and directly impacts food security, which faces the difficulties of population growth and rising food demand. The analysis of environmental factors and soil properties data to predict key determinants is difficult due to variability in weather conditions and soil quality, which affects the yield of Jowar and Ragi crops. This research proposes a Long Short-Term Memory (LSTM)-guided Conditional Dynamic Variational Auto-Encoder (LSTM-CDVAE) for crop yield prediction and classification. Using LSTM's ability to obtain temporal patterns and CDVAE's generative modeling capability, this approach provides better feature representation, thereby enhancing generalization for various crop conditions. In this study, two crop datasets, All Crops (Dataset 1) and Filtered Crops (Dataset 2), were considered and preprocessed using a standard scaler and label encoding. The standard scaler normalizes numerical features to uniform scales, while label encoding converts categorical values into numerical representations, thus preserving the integrity of various classes. The Synthetic Minority Oversampling Technique (SMOTE) is applied to balance data by oversampling minority classes. The LSTM-CDVAE achieves 99.85% accuracy and 0.0015 MSE for Dataset 1, which is better than the Weight-Tuned Deep Convolutional Neural Network (WTDCNN).
Keywords:
crop type classification, crop yield prediction, feature representation, label encoding, standard scalerDownloads
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