Abstract: Abstract: Precision agriculture urgently demands accurate three-dimensional phenotypic reconstruction of fruit trees. Traditional methods suffer from low modeling efficiency and high computational costs, while two-dimensional analysis is constrained by occlusion and a lack of depth information. Although Neural Radiance Fields (NeRF) demonstrate excellent performance, they are time-consuming and resource-intensive to train, facing significant challenges in learning complex fruit tree structures. This study proposes a knowledge distillation-enhanced NeRF method for three-dimensional fruit tree reconstruction (KD-NeRF). We construct a deep learning framework based on two-dimensional multi-view images and employ SIREN periodic activation functions to enhance high-frequency detail capture and spatial continuity through implicit regularization. A teacher-student knowledge distillation mechanism is designed to transfer deep representations to lightweight networks, improving the learning efficiency for complex structures. Experiments on diverse fruit tree datasets demonstrate that compared to NeRF, KD-NeRF achieves an 8% improvement in PSNR with 14 times ~16 times acceleration in training speed. The results of the ablation experiment confirm that the teacher-student architecture significantly improves PSNR, SSIM, and other metrics; SIREN enhances high-frequency detail representation; and their synergy produces performance gains exceeding simple additive effects. KD-NeRF addresses the efficiency bottleneck of NeRF in fruit tree reconstruction, providing efficient technical support for fruit tree phenotypic analysis, growth monitoring, and intelligent breeding, thereby advancing precision agriculture and smart orchard construction.

Key words: Key words: deep learning, agricultural technology, 3D reconstruction, neural radiance fields, knowledge distillation