Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral data to estimate key soil properties, including organic carbon (OC), nitrogen (N), calcium carbonate (CaCO₃), and pH (in H₂O). The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. Experimental results show that the proposed model achieves excellent prediction performance, with coefficient of determination (R²) values of 0.949 (OC), 0.916 (N), 0.943 (CaCO₃), and 0.926 (pH), along with corresponding ratio of percent deviation (RPD) values of 3.940, 3.737, 5.377, and 3.352. Both R² and RPD values exceed those of traditional machine learning models, such as partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), as well as deep learning models like CNN-LSTM and Gated Recurrent Unit (GRU). Additionally, the proposed model outperforms S-AlexNet in effectively capturing temporal and spatial patterns. These findings emphasize the potential of the proposed model to significantly enhance the accuracy and reliability of soil property predictions by capturing both temporal and spatial patterns effectively.

Deep hashing has gained widespread attention in cross-modal retrieval due to its low cost and efficient retrieval advantages. However, existing cross-modal hashing methods either focus solely on exploring relationships between data points—which inevitably leads to intra-class dispersion—or emphasize the relationships between data points and categories while neglecting the preservation of inter-class structural relationships, resulting in suboptimal hash codes. To address this issue, this paper proposes a Deep Class-Guided Hashing (DCGH) method, which approaches model learning from the category level. It ensures that data of the same category are clustered around the same class center while maintaining the structural relationships between class centers. Extensive comparative experiments conducted on three benchmark datasets demonstrate that the DCGH method achieves comparable or even superior performance compared to existing cross-modal retrieval methods.