## 1 Introduction
applied Savitzky-Golay (SG) smoothing, adaptive window length SG smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC) algorithms to preprocess raw spectral data and constructed prediction models to analyze the effects of different preprocessing methods on model prediction accuracy [21]. [PERSON] et al. developed four preprocessing methods--SG, MSC, SNV, and variable sorting for normalization--to filter noise and scattering information from the raw spectra [22]. [PERSON] et al. used MSC, SNV, and SG convolution smoothing for spectral preprocessing to facilitate subsequent analysis [23]. Convolutional neural networks (CNNs) are a type of feedforward neural network characterized by its ability to automatically learn features in tasks such as image and speech processing. CNNs possess a unique ability to extract spatial-spectral features, making them highly effective for processing hyperspectral images, and they are thus widely used in this field [24]. [PERSON] et al. proposed a point-centered regression CNN to estimate the concentrations of phycocyanin and _chlorophyll a_ in water bodies using hyperspectral images, achieving coefficient of determination (R\({}^{2}\)) values greater than 0.86 and 0.73, respectively, with root mean square error (RMSE) values below 10 mg/m\({}^{3}\)[25]. [PERSON] et al. introduced an attention residual CNN, which, in combination with near-infrared HSI (900-1700 nm), predicted the fat content of salmon fillets, achieving an R\({}^{2}\) of 0.9033, RMSE of prediction of 1.5143, and residual predictive deviation (RPD) of 3.2707 [26]. [PERSON] et al. employed short-wave infrared HSI combined with a one-dimensional (1D) CNN model to determine the soluble solids content in dried Hami juibe, and they achieved an R\({}^{2}\) of 0.857, RMSE of 0.563, and RPD of 2.648 [27]. [PERSON] et al. proposed a 1D deep learning model based on CNN with a spectral attention module to estimate the total chlorophyll content of greenhouse lettuce from full-spectrum hyperspectral images. The experimental results showed an average R\({}^{2}\) of 0.746 and an average RMSE of 2.018, outperforming existing standard methods [15]. [PERSON] et al. established an attention-CNN incorporating multi-feature parameter fusion to estimate chlorophyll content in millet leaves at different growth stages using hyperspectral images. The model achieved an R\({}^{2}\) of 0.839, an RMSE of 1.451, and an RPD of 2.355, demonstrating superior predictive accuracy and regression fit compared to conventional models [17]. CNNs are currently among the most widely used models in deep learning [28]. In addition to the aforementioned regression tasks, they can also be applied to image segmentation. Image segmentation divides images into regions with different characteristics and extracts regions of interest (ROIs) [29], which can be understood as a pixel classification problem [28]. Compared to conventional RGB images, each spatial location in a hyperspectral image contains hundreds of spectral bands. Due to its high dimensionality [30], the image segmentation of hyperspectral images is more complex. Existing methods for hyperspectral image segmentation can be categorized into thresholding, watershed, clustering, morphological, region based segmentation, deep learning, and superpixel-based segmentation [31]. Among deep learning methods, commonly used CNN architectures include AlexNet [32], VGGNet [33], ResNet [34], GoogLeNet [35], MobileNet [36], and DenseNet [37]. CNN models have been widely applied to both classification and regression tasks. The combination of HSI and CNN has yielded positive results in the field of agricultural production [19; 24; 38]. However, few studies have applied this combination to chlorophyll content regression models for grape leaves. Therefore, this study integrates hyperspectral image segmentation, spectral preprocessing, and CNNs to propose, for the first time, a data-driven ensemble framework for predicting _chlorophyll a + b_ content in grape leaves.