Abstract: Peanut is one of the most widely cultivated oil crops globally, with China leading in both production and consumption. As the demand for oil crops increases, ensuring stable peanut production and oil supply security has become a key agricultural goal. Peanut biomass, as a crucial parameter reflecting crop growth status, is essential for precision agriculture management and efficient resource utilization. The aboveground parts of peanut plants can be used not only as animal feed but also as a resource for bioenergy production. Therefore, comprehensive and accurate biomass estimation provides valuable references for yield prediction and resource management. Traditional biomass measurement methods are often labor-intensive and time-consuming, with spatial and temporal limitations. Recently, with the development of UAV remote sensing, especially the widespread application of hyperspectral imaging technology, crop biomass estimation has become more efficient. Hyperspectral imaging, known for its high resolution and rich spectral information, has been used for growth monitoring and yield estimation of crops such as soybean, rice, and wheat, demonstrating superior performance in predicting parameters like yield, chlorophyll, and nitrogen content, as well as in disease diagnosis. However, research on peanut remains limited, particularly regarding the spectral characteristics of different peanut varieties and their impact on biomass estimation accuracy. This study, using UAV hyperspectral imaging, investigated sensitive spectral bands and feature combinations for efficient and accurate field-scale peanut biomass estimation. An experimental field with 11 peanut varieties in Xingyang, Henan, was used as the study area. First, UAV hyperspectral images of the test field were collected and preprocessed with radiometric calibration and atmospheric correction to ensure data accuracy. Spectral reflectance data from ground sampling points were then extracted, and the first derivative of spectral reflectance and multiple vegetation indices were calculated to enhance the feature dimensions related to biomass. The Variable Importance in Projection (VIP) method was used to select sensitive spectral bands and feature combinations closely related to biomass, effectively eliminating data redundancy and isolating highly relevant features. Using the selected features and ground-truth data, Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Random Forest Regression (RFR) models were constructed, and the estimation accuracy of different machine learning models was compared. Additionally, selected sensitive features were combined in multiple ways and input into the models to further improve estimation accuracy. The Particle Swarm Optimization (PSO) algorithm was employed to optimize model hyperparameters, achieving the best model performance. Results showed that the sensitive features derived from the first derivative of spectral reflectance were highly correlated with peanut biomass, yielding better model performance than those derived from raw spectral reflectance and individual vegetation indices. The RF model combining the first derivative of spectral reflectance and vegetation indices achieved the highest estimation accuracy (R² = 0.75, RMSE = 0.08). Further improvement was achieved with the PSO-optimized RF model (PSO-RF), which resulted in an accuracy of R² = 0.80 and RMSE = 0.07. This study demonstrates the potential of combining UAV hyperspectral imaging with machine learning models for non-destructive peanut biomass estimation, providing essential theoretical and technical support for large-scale agricultural biomass monitoring. These findings are valuable for advancing precision agriculture, optimizing resource allocation, and improving crop management efficiency.