## 1 Introduction  
Above-ground biomass (AGB) is a critical indicator for evaluating crop growth status. It not only reflects the health and productive potential of crops but also correlates closely with final crop yield [1, 2, 3]. Accurate prediction of above-ground biomass provides valuable information for growers and researchers, enabling the optimization of crop management strategies. By scientifically adjusting the application rates of fertilizers, pesticides, and irrigation water, it is possible to enhance both the yield and quality of agricultural products [4]. Moreover, predicting above-ground biomass is crucial for adjusting the operational parameters of harvesting machinery. This biomass directly influences the intake rate of harvesting equipment, and inappropriate intake levels can adversely affect the efficiency and performance of these machines [5, 6]. Traditional methods for monitoring above-ground biomass face numerous limitations and drawbacks, often involving destructive processes [7,8]. With advancements in computing and the increasing maturity of deep learning algorithms, such as deep regression networks (DRNs) [9], non-destructive methods for predicting above-ground biomass have been increasingly adopted. Compared to conventional monitoring techniques, deep learning-based approaches offer significant advantages [10,11]. In deep learning, regression tasks typically encompass several research areas, including deep imbalanced regression, ordinal regression, general continuous-value regression, and contrastive regression. These areas are not mutually exclusive, as overlaps exist between different research directions. [PERSON] investigated the issue of data imbalance in deep learning regression tasks, defining deep imbalanced regression (DIR) [12]. [PERSON] proposed a probabilistic deep learning model called Variational Imbalanced Regression (VIR) [13], designed to handle imbalanced regression problems while naturally providing uncertainty estimation. [PERSON] introduced a novel rank-consistent ordinal regression method, CORN, which addresses the limitations of weight-sharing constraints in existing approaches [14]. [PERSON] studied CRGA [15], a method that leverages contrastive learning for unsupervised gaze estimation generalization in the target domain. [PERSON] proposed Rank-N-Contrast (RNC) [16], which incorporates contrastive learning to rank contrastive samples in the target space, ensuring that learned representations align with the target order. [PERSON] introduced ConR [17], a contrastive regularization method for addressing imbalanced regression problems in deep learning. By simulating global and local label similarities in the feature space, ConR effectively tackles the challenges of imbalanced regression. The Momentum Contrast (MoCo) method by [PERSON] et al. introduced a momentum encoder for updating encoder parameters [18]. Additionally, methods like Deep Infomax (DIM) and Contrastive Predictive Coding (CPC) have played pivotal roles in advancing self-supervised learning [19,20]. With the rapid development of 3D acquisition technologies, 3D sensors have become increasingly widespread. These sensors capture rich geometric, shape, and scale information. Three-dimensional data can be represented in various formats, such as depth images, point clouds, and meshes, with point clouds being the preferred format in many scenarios [21]. Methods that use LiDAR (Light Detection and Ranging) sensors to acquire point cloud data have found extensive applications in agriculture and forestry [22, 23, 24]. For instance, [PERSON] utilized drones equipped with LiDAR sensors to monitor two agricultural fields in Denmark, collecting point cloud data. They employed an extremely randomized trees regressor to predict biomass [25]. [PERSON] developed a tree-level biomass estimation model based on LiDAR point cloud data, extracting features, such as tree height and trunk diameter, and using a Gaussian process regressor to associate these features with tree biomass [26].