Band Selection Algorithm Research Articles

Novel discretized gravitational search algorithm for effective medical hyperspectral band selection

Medical hyperspectral imaging present a promising avenue for non-invasive diagnostic methods for diseases. Nonetheless, the sparsity of medical hyperspectral data within high-dimensional spaces introduces the “curse of dimensionality”, which diminishes the efficiency and accuracy of data processing efforts. Therefore, spectral dimensionality reduction emerges as an essential process in the analysis and utilization of MHSIs data. To retain the intrinsic properties of the spectral bands, an effective unsupervised band selection algorithm is proposed leveraging the gravitational search algorithm (GSA-UBS) to identify the optimal band subset. Taking into account the informational content and redundancy among candidate bands, a comprehensive evaluation criterion is established that incorporates a band distance matrix and an information entropy vector. Additionally, a straightforward discrete search strategy is developed that enables gravitational search algorithm to directly retrieve the original sequence numbers of the selected bands, bypassing the conventional 0–1 band weighting approach. The extensive evaluation of GSA-UBS on three publicly available invivo brain cancer MHSIs datasets and a remote sensing hyperspectral image demonstrates its superior performance compared to various state-of-the-art methods. The source code for GSA-UBS can be accessed at https://github.com/zhangchenglong1116/GSA_UBS.

Structural graph learning method for hyperspectral band selection

ABSTRACT Recently, graph learning-based hyperspectral band selection algorithms illustrate impressive performance for hyperspectral image (HSI) processing, whose goal is to select an optimal band combination containing less redundancy through the learned graph matrix. However, most of the previous methods work in single spectral domain, which neglects the rich image spatial information. Moreover, they typically model the graph matrix from a global perspective while ignoring the differences in spatial distribution that exist in diverse pixel and band regions. Based on the above considerations, to take full account of structure information in spatial and spectral domains, a structural graph learning method for hyperspectral band selection (SGLM) is designed. Specifically, SGLM constructs two matrices using the correlation between pixels and bands under the local perspective, which can capture the image structure information reasonably. Since the proposed method is modelled in both spatial and spectral dimensions, it is conducive to subsequent band subset generation task. Meanwhile, in order to guarantee local spatial consistency among bands, a Laplacian regularization term associated with the error matrix is introduced to the self-representation model. Additionally, considering that the importance of a band is consistent with its capability to reconstruct the entire band set, an adjacent bands reconstruction strategy is adopted to obtain the ultimate band combination, which can assess the significance of each band effectively. The comprehensive experimental analysis on four datasets demonstrates that the proposed SGLM model has outstanding superiority in comparison with several band selection algorithms.

Multi-objective evolutionary multi-tasking band selection algorithm for hyperspectral image classification

Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms.

An Analytical Study on the Utility of RGB and Multispectral Imagery with Band Selection for Automated Tumor Grading.

The implementation of tumor grading tasks with image processing and machine learning techniques has progressed immensely over the past several years. Multispectral imaging enabled us to capture the sample as a set of image bands corresponding to different wavelengths in the visible and infrared spectrums. The higher dimensional image data can be well exploited to deliver a range of discriminative features to support the tumor grading application. This paper compares the classification accuracy of RGB and multispectral images, using a case study on colorectal tumor grading with the QU-Al Ahli Dataset (dataset I). Rotation-invariant local phase quantization (LPQ) features with an SVM classifier resulted in 80% accuracy for the RGB images compared to 86% accuracy with the multispectral images in dataset I. However, the higher dimensionality elevates the processing time. We propose a band-selection strategy using mutual information between image bands. This process eliminates redundant bands and increases classification accuracy. The results show that our band-selection method provides better results than normal RGB and multispectral methods. The band-selection algorithm was also tested on another colorectal tumor dataset, the Texas University Dataset (dataset II), to further validate the results. The proposed method demonstrates an accuracy of more than 94% with 10 bands, compared to using the whole set of 16 multispectral bands. Our research emphasizes the advantages of multispectral imaging over the RGB imaging approach and proposes a band-selection method to address the higher computational demands of multispectral imaging.

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing.

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4-1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

Thermal infrared hyperspectral image band selection based on dynamic metric-guided particle swarm optimization for quantitative analysis of potash

ABSTRACT Potash plays an important role in agriculture and food security, so does the estimation of the content of substances in potash. Chemical methods for such estimation tasks are time-consuming, whereas thermal infrared hyperspectral (TIH) images provide us with an opportunity to do estimation efficiently. This paper hence applies the TIH technology to predict the content of various substances in potash. On the one hand, the TIH technology provides rich hyperspectral information for substance discovery and analysis. On the other hand, it introduces challenges of dimensionality and information redundancy, leading to potential decreased predicting accuracy. To solve the problem, as well as easing the burden of detection instruments in terms of band usage, band selection is critical. This paper proposes a dynamic metric-guided particle swarm optimization (DM-GPSO) method for band selection. It adaptively adjusts parameters to prevent local optima, avoiding the need for manual parameter tuning. Variety of band selection algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and improved PSO algorithms, are tested to extract and analyse the characteristic bands of potash. Experimental results demonstrate that the TIH technology combined with a proper band selection methodis effective in estimating the content of potash. The new DM-GPSO can select different combinations of bands, which lead to better and more stable prediction results. The analysis on the combinations of selected bands also reveals that picromerite and potassium chloride have similar intervals of characteristic bands, which can be used to guide the production process of potash in practice.

Multi-agent deep reinforcement learning for hyperspectral band selection with hybrid teacher guide

Due to the presence of noisy and highly redundant bands in hyperspectral images (HSIs), band selection serves as a key preprocessing for downstream classification tasks. Recently, deep reinforcement learning (DRL) has been developed as a new trend for band selection of HSIs. Existing DRL-based methods often adopt single-agent, which are prone to fall into local optima due to an excessive action space. The multi-agent methods provide a feasible solution, but often require too much computation. To address these problems, a novel multi-agent DRL method with hybrid teacher guide (MH-DRL) is proposed for band selection of HSIs. In MH-DRL, each agent corresponding to a spectral band decides whether this band is selected. Moreover, a presentation-evaluation network (PE-Net) is constructed to design the reward by evaluating the candidate band subsets without any fine-tuning and represent the state by extracting the spatial-spectral features of HSIs. Then, three kinds of experienced band selection models are regarded as the teachers and designed to participate in the band exploration of DRL, which can improve the learning effectiveness and efficiency by accumulating the external knowledge from diverse teacher models. Finally, deep Q-learning algorithm is designed to update the agents and improve their self-learning ability from continuous exploration. Experimental results on three widely-used HSI data verify the performance of the proposed method better than some advanced band selection algorithms of HSIs. Our source code is available at https://github.com/jiefeng0109/MH-DRL.

Research on classification methods for rubber based on terahertz time-domain spectroscopy with data fusion strategy

Rubber, an essential industrial product, plays a crucial role in industry and human life. However, some manufacturers of rubber use inferior, recycled or low-cost rubber materials to replace high-quality rubber products. Therefore, in order to ensure industrial production, protect natural environment and ensure personal safety, it is of great economic and social significance to enable rapid and non-destructive qualitative identification of rubber products. In this paper, eight different types of black rubber, including Nitrile Butadiene Rubber (NBR), Choloroprene Rubber (CR) and Ethylen Propylen Diene Monomer (EPDM), were selected as research objects. Terahertz Time-Domain Spectroscopy (THz-TDS) technology was utilized to get the absorption spectra, refractive index spectra and time-domain spectra of these samples for exploring the differences between different types of rubber in terahertz spectra. To reduce the data volume, three band selection algorithms were employed to extract features from the absorption spectra and refractive index spectra, and temporal features of time-domain spectra were extracted for subsequent data processing. In the selection of classification model algorithms, K-Nearest Neighbor (KNN), Random Forest (RF) and Supervised Kohonen Neural Networks (S-Kohonen) were employed to recognize these spectral data. To fully exploit the latent information within these data and considering the possibility of the loss of partial information in single-type signal, a feature-level data fusion method was applied to different optical parameter signals to obtain more information about terahertz spectra during the process of data modeling. The results of the research indicated that the model with the highest prediction accuracy was the KNN model, with an accuracy of 97.37%, when refractive index spectra and time-domain spectra were fused as inputs, the F1-score reached 99.64. The performance of data fusion was noticeably better than that of a single-type signal. This study validates the feasibility of using terahertz time-domain spectroscopy for qualitative identification of different types of rubber.

An adaptive evolutionary-reinforcement learning algorithm for hyperspectral band selection

Band selection is one of the most important considerations for hyperspectral imaging (HSI) datasets that involves selecting fewer bands with higher classification accuracy. As a combinatorial optimization problem, evolutionary algorithm has been applied in the field, but the optimization ability and number of selected bands are limited with the control of exploration and exploitation. In this paper, an adaptive evolutionary-reinforcement learning algorithm (ERLA) is proposed for band selection of HSI datasets, only a suitable phase contributes to updating the corresponding agents in each iteration, and the coding length is adaptively lessened with the potential solution space. In addition, reinforcement learning is used to choose a feasible action in each iteration, and the unselected bands are removed from the candidate band combination in the previous state. A simulation study is conducted on four public HSI datasets, where the proposed framework has delivered an overall accuracy of 98% for 12% of bands in Chikusei dataset, 92% for 11% in KSC dataset, 93% against 19% for Xiongan dataset, and 97% in Longkou dataset with 10% bands retained, demonstrating that the proposed framework outperforms other approaches with several targets, and that the number of selected bands is obviously decreased with satisfactory classification accuracy.

Anomaly‐background separation and particle swarm optimization based band selection for hyperspectral anomaly detection

AbstractAs one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects. Recently, many BS methods for target detection have achieved promising results by making full use of the priori spectral features of the target to be detected. Conversely, anomaly detection separates the anomaly based solely on the statistical distribution difference between anomaly and background without any prior information. Therefore, the development of BS for anomaly detection has lagged far behind that of BS for target detection. To this end, this paper proposes a novel BS algorithm dedicated to anomaly detection tasks, named anomaly‐background separation and particle swarm optimization (PSO)‐based BS. Specifically, an anomaly‐background separation framework (ABSF) is established to predetermine a priori knowledge of anomaly distribution. Then, three band prioritization criteria are constructed with the anomaly‐background constraints generated by ABSF. Finally, PSO is used to find the optimal subset of bands in the solution space. The experiments on two real datasets demonstrate that the proposed method yields better detection results and greater stability compared to other BS methods discussed in this paper.

A novel framework combining band selection algorithm and improved 3D prototypical network for tree species classification using airborne hyperspectral images

Fine-grained identification of forest types and tree species represents a critical aspect of forest resource inventory and monitoring. The use of airborne hyperspectral remote sensing imagery stands out for its ability to finely differentiate among tree species, leveraging its exceptional spatial resolution and rich spectral details. However, this approach is limited by several challenges (e.g., high spectral correlation and information redundancy). In accordance, the adoption of a lightweight deep learning approach in the form of a few-shot learning model can effectively resolve the challenges of multi-forest tree species classification. Therefore, integrating a data dimensionality reduction algorithm with a few-shot classification model presents a promising avenue for resolving the fine-grained classification of forest tree species. In this study, we propose the innovative classification framework FAST 3D-CNN P-Net. This framework utilizes CNN for band selection, enhances the fine-grained identification process in hyperspectral data, and integrates an optimized FAST 3D-CNN into the P-Net classifier (a few-shot classifier). First, a CNN-based band selection method is employed to learn the nonlinear dependencies between spectral bands, assign weights to rank the bands, and reconstruct the global spectral information using the most informative bands. It then constructs a novel classification model, designated FAST 3D-CNN P-Net, through the integration of an optimal 3D-CNN with a prototypical network. To enhance classification performance, the FAST 3D-CNN P-Net utilizes reconstructed hyperspectral images derived from the band selection results as input. The effectiveness of the proposed framework was assessed with the airborne GFF dataset and the widely accessible medium-resolution hyperspectral datasets, Indian Pines (IP) and Kennedy Space Center (KSC). The overall classification accuracy reached 98.33 % for the GFF dataset and 97.21 % and 99.43 % for the IP and KSC, respectively, exhibiting performance superiority compared to the standalone 3D-CNN classification network. This classification framework demonstrates efficiency in selecting a subset of hyperspectral bands with minimal redundancy, empowering the rapid and accurate classification and mapping of tree species in complicated, multi-species forest stands, even with a limited quantity of labeled samples.

Collaborative Hyperspectral Image Processing Using Satellite Edge Computing

The advancement of nanosatellite techniques has boosted the growth of satellite-originated data and applications. Satellite edge computing (SEC) is envisioned to provide in-orbit processing of the sensed data to save the scarce terrestrial-satellite communication resources and support mission-critical services. While most of the existing SEC studies mainly focus on general computing tasks, we present a two-tier collaborative processing framework for the important and unique hyperspectral image (HSI) processing task. Our framework carefully selects bands out of the collected HSIs and sends them back for further analysis. We first conduct a comprehensive data analysis to reveal the non-trivial relationship between the band selection and the eventual analytic performance. We then formulate the band selection problem in this collaborative setting as a utility maximization problem that jointly considers the analytic, energy, and communication factors. A novel multi-agent reinforcement learning approach, named MaHSI, is proposed to solve it in the dynamic SEC environment. Our multi-agent design judiciously embeds the complex correlations among bands as collaborations among agents and significantly reduces the exploration space. Extensive experiments on real-world HSI datasets prove that our approach not only outperforms the existing classical band selection algorithms in accuracy and inference speed but also brings the highest utility to the satellites.

Almond defect and freshness inspection system using hyperspectral imaging and deep learning techniques

Almonds, recognized as a nutritious and low-calorie snack rich in unsaturated fats and essential nutrients, are primarily imported from California. Despite undergoing grading control by the United States Department of Agriculture (USDA), concerns persist regarding the presence of defective kernels. Manual hand-picking is the conventional method for almond processing; however, prolonged picking can reduce accuracy due to visual fatigue. Consumers prioritize freshness due to fungi’s potential for aflatoxin production, emphasizing the need for vigilant almond consumption. In response to these challenges, this study presents an innovative approach that employs a lightweight VGG16 and an enhanced MobileNet model for identifying defects based on RGB images of almonds. This method effectively classifies broken, scratched, and malformed kernels. Additionally, hyperspectral imaging (HSI) data in the 400–1000 nm range were collected to distinguish insect-damaged from undamaged kernels and assess almond freshness. For comparative analysis, various band selection algorithms were applied to extract distinct spectral bands. For insect damage identification, we propose a merged CNN model created by simultaneously combining 1D- and 2D-CNNs, allowing for the concurrent extraction of spectral and spatial characteristics. This approach achieved a remarkable ACC of 0.9905 and a Kappa value of 0.9521. Meanwhile, in evaluating freshness, PCA was employed to select the first 30 principal components as vital bands. Our proposed lightweight 1D-CNN delivered an ACC of 0.9768 and a Kappa value of 0.9527 in freshness assessment.

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

A hyperspectral band selection algorithm for identifying high oleic acid peanuts

High oleic acid peanuts have higher oleic acid content and stronger oxidation stability than common peanuts, but their appearances are similar, which imposes difficulties for classifying. Based on this, the study aims to classify high oleic acid peanut to ensure its purity by using hyperspectral imaging technology. However, classification accuracy and efficiency are limited given the large amount of redundant information of hyperspectral images. The band iteration algorithm (BIA) is proposed to select characteristic bands by reducing the redundant information between spectral bands for the peanut classification. Hyperspectral images with 616 bands (from 400 nm to 1100 nm) of 126 high oleic acid peanuts and 126 common peanuts were collected. Then, BIA selected optimal bands as characteristic bands from adjacent bands according to the classification accuracy of each band subsets. Thirdly, three classification models, namely linear discriminant analysis, support vector machine, and partial least squares-discriminant analysis (PLS-DA), were employed to compare the performance of BIA with successive projections algorithm and competitive adaptive reweighted sampling, respectively. The experimental results show that BIA can effectively improve the classification ability of spectral data. The BIA-PLS-DA model had the best classification efficiency, and the accuracy of the test set reached 93.26%. For peanut individuals, only one peanut sample was misclassified with a classification error rate of 1.43%.

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them lack the self-supervision of representations and ignore the multi-level spectral–spatial information of HSI and the connectivity of subspaces. To this end, this paper proposes a novel self-supervised multi-level representation learning fusion-based maximum entropy subspace clustering (MLRLFMESC) method for hyperspectral band selection. Firstly, to learn multi-level spectral–spatial information, self-representation subspace clustering is embedded between the encoder layers of the deep-stacked convolutional autoencoder and its corresponding decoder layers, respectively, as multiple fully connected layers to achieve multi-level representation learning (MLRL). A new auxiliary task is constructed for multi-level representation learning and multi-level self-supervised training to improve its capability of representation. Then, a fusion model is designed to fuse the multi-level spectral–spatial information to obtain a more distinctive coefficient matrix for self-expression, where the maximum entropy regularization (MER) method is employed to promote connectivity and the uniform dense distribution of band elements in each subspace. Finally, subspace clustering is conducted to obtain the final band subset. Experiments have been conducted on three hyperspectral datasets, and the corresponding results show that the proposed MLRLFMESC algorithm significantly outperforms several other band selection methods in classification performance.

Deep learning-based spectral reconstruction in camouflaged target detection

Camouflaged target detection aims to detect targets that blend into their surroundings, but RGB has difficulty distinguishing between targets and backgrounds. While methods using multispectral image (MSI) can distinguish targets from background via spectral information, they are limited by imaging speed, resolution, and high cost for camouflaged target detection. Here, we propose a novel camouflaged target detection workflow based on reconstructed MSI from RGB image. Specifically, we propose a spectral reconstruction model, S2HFormer, which utilizes the deep neural network to fit the mapping of RGB image to MSI without additional information. And the reconstructed MSI based on S2HFormer achieves higher accuracy in both reconstruction and target detection, outperforming existing methods. Furthermore, we integrate a spectral band selection algorithm to optimize the number of bands used for improving detection efficiency. Experimental results show that the proposed method acquires MSI at 55 frames per second (FPS) and achieves an F-score of 0.925, achieving real-time (24 FPS) MSI acquisition. The evaluation indicates the effectiveness and efficiency of our method for camouflaged target detection.

Design of Information Management System Based on Random Leapfrog Band Selection Algorithm

With the rapid development of information technology, the traditional information management system faces the challenge of efficiency and accuracy when dealing with complex data and decision-making problems. In order to solve these problems, this paper integrates the random leapfrog band selection algorithm into the information management system. This algorithm shows excellent performance in dealing with large-scale, nonlinear and multi-variable optimization problems, resource allocation, scheduling optimization and data mining. This paper mainly explains the design framework of information management system, the optimization of information management by random leap-band selection algorithm and the design of information management system. Finally, two groups of simulation experiment results are as follows: the accuracy of the optimized information management system is 96%, the running time is 7.1min, the algorithm performance is better than the traditional system, and the response time of the system is reduced by 195ms. This research provides an efficient, flexible and scalable solution for dealing with various complex information management system optimization problems.

Efficient Onboard Band Selection Algorithm for Hyperspectral Imagery in SmallSat Missions With Limited Downlink Capabilities

Efficient Onboard Band Selection Algorithm for Hyperspectral Imagery in SmallSat Missions With Limited Downlink Capabilities

Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model

Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R2), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry.