The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments to investigate the relationship between the nitrogen content and spectral reflectance of apple canopy leaves. Two different preprocessing methods, Savitzky–Golay (SG) smoothing and multiple scattering correction (MSC), were used to extract the feature bands by combining the successive projection method (SPA) and the competitive adaptive weighting algorithm–partial least squares (CARS-PLS). The reflectance values of the feature bands screened via these two methods were used as inputs to construct the multi-factor inversion models of apple canopy leaf nitrogen content based on the long- and short-term memory (LSTM) network, the support vector regression (SVR) and the Least Squares Support Vector Machine Regression (RIME-LSSVM). The study compared the ability of three algorithmic models to estimate leaf nitrogen content, and the results showed that the model constructed with the reflectance values of the characteristic bands screened by the CARS-PLS algorithm as inputs was more effective in predicting the nitrogen content of leaves. Furthermore, the accuracy of the model constructed using RIME-LSSVM was significantly higher than that of the model constructed using the long- and short-term memory network and support vector regression, in which the coefficient of determination of the test set (R-squared) is 0.964 and the root-mean-squared error (RMSE) is 0.052. Finally, the CARS-PLS algorithm combined with the RIME-LSSVM model has a higher prediction accuracy. The study demonstrated the feasibility and reliability of hyperspectral techniques for the estimation of nitrogen content of apple leaves in the Aksu region.

The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

Leaf chlorophyll content (LCC) is a key physiological parameter affecting plant growth and development. Rapid and non-destructive monitoring of LCC using hyperspectral remote sensing is crucial for promoting precision agriculture. In this study, hyperspectral data of apple canopy leaves at different phenological stages were collected alongside their corresponding SPAD values (representing LCC) to construct a dataset. Two types of spectral features were extracted: (1) optimized spectral index combinations; and (2) feature bands selected using the Successive Projections Algorithm (SPA). Based on these features, three machine learning models—Support Vector Machine (SVM), Least Squares Support Vector Machine (LSSVM), and Chaos Evolution Optimization-enhanced LSSVM (CEO-LSSVM)—were developed to estimate SPAD values. The results indicate that the constructed optimal spectral index combinations exhibit superior sensitivity in SPAD estimation compared to the feature bands selected by SPA. Specifically, during the physiological fruit drop stage, the CEO-LSSVM model based on spectral indices achieved a test set R² of 0.851, surpassing the SPA-based model (R² = 0.813). Regarding model performance, the CEO-LSSVM demonstrated the highest accuracy and robustness across all stages. In the fruit drop period, using optimized spectral indices, it achieved an RMSE of 1.338, significantly outperforming the LSSVM (RMSE = 1.703) and SVM (RMSE = 2.409) models. This superiority was further evident in the fruit enlargement stage, where the CEO-LSSVM model reached a peak test set R² of 0.868 and the lowest RMSE of 1.254. The integrated model combining optimized spectral indices and CEO-LSSVM provides an efficient and high-precision approach for hyperspectral SPAD estimation in apple canopies, effectively addressing the challenges of inversion modeling in arid oasis environments.