The inversion of Ground-Penetrating Radar data is beneficial for studying the subsurface media distribution, especially for the root system and their environment. Emerging GPR inversion models focus on constructing a direct mapping from radar wave B-Scan image to permittivity distribution. They not only rely on the diversity of the dataset, but also hard to specify the effects of electromagnetic wave propagation properties. These make it difficult to accurately recover complex underground scenes. Addressing the above issues, this paper proposes a radar wave inversion model based on the embedded velocity field (Velocity Field Inversion UNet, VFIUNet), which utilizes the estimated global velocity information within the detection domain to reconstruct the subsurface permittivity distribution from B-Scan data. First, the propagation velocity of radar wave in the detection domain is estimated through self-focusing FK migration. Then, an embedded physical velocity field inversion model based on the multi-scale transformation module was designed, which integrates the prior velocity field and the collected B-Scan data to estimate the difference in permittivity distribution. Finally, the parameters of the VFIUNet are optimized with the hybrid loss function (including L2 and SSIM) and the root scene radar wave dataset. Comparative studies with existing inversion models exhibit that inversion models based on a priori velocity information provide better performance in the permittivity distribution reconstruction. The predictions of numerical experiments and in situ root detection indicate that the permittivity, shapes, and positions of the subsurface media could be restored by the proposed inversion model with high accuracy. Accurate GPR inversion would facilitate the three-dimensional reconstruction of subsurface root scenes.

Ground Penetrating Radar (GPR) forward and inversion methods are key techniques for studying radar imaging mechanisms and investigating subsurface scenes. Efficiently interpreting radar wave data will facilitate the development of subsurface structure detection applications, especially in the intricate plant root distribution. Existing forward and inversion models are constrained by the highly computational and time-consuming forward process, making it difficult to be applied to complex real-world subsurface scenarios. Inspired by the spatio-temporal properties during radar wave imaging, a spatial and temporal fusion cycle U-shaped model named GPR-CUNet was proposed. The model is more adapted to the transformation between permittivity distribution and GPR B-Scan data in complex environment. Firstly, to extract the spatial and temporal features from the permittivity distribution and radar data, a Spatio-Temporal Feature Fusion Module (STFM) based on CNN and BiLSTM was designed. Then, for the translation between the permittivity distribution and the radar wave data, two identical U-shaped networks with the STFM module were constructed. Finally, guided by predictive consistency and cyclic consistency, a hybrid loss function based on multi-scale structural similarity (MS-SSIM) and L1 norm was configured to boost the performance of both the forward and inversion networks. The numerical simulation experiments revealed that the proposed model imparted exceptional performance and efficiency in the prediction of radar wave features and reconstruction of permittivity distribution under complex scenarios. In pre-burial experiments and field root testing, our inversion model can effectively recover the subsurface root and soil horizons distribution. Accurate permittivity distribution of subsurface scene can provide a theoretical basis for imaging and three-dimensional reconstruction of the physical media distribution in plant root zones.

This study focuses on existing pruning equipment; cutting blades show cutting resistance and lead to high energy consumption. Using finite element (FEA) numerical simulation technology, the branch stress wave propagation mechanism during pruning was studied. The cutting performance of the bionic blade was evaluated with cutting energy consumption as the test index and the branch diameter and branch angle as the test factors, respectively. The test results showed that the blades imitating the mouthparts of the three-pecten bull and the beak of the woodpecker performed well in pruning, and the energy consumption during cutting was reduced by 18.2% and 16.3% compared to traditional blades, making these blades significantly better. These two blades also effectively reduced the cutting resistance and branch splitting by optimizing the edge angle design and increasing the slip-cutting action. In contrast, the imitation shark’s tooth blade increased cutting energy consumption by 14.4% due to the large amount of cutting resistance in the cutting process when cutting larger-diameter branches, making it unsuitable for application in the pruning field. Therefore, the blades imitating the mouthparts of the three pectins and the beak of the woodpecker have significant advantages in reducing the cutting resistance and improving the pruning quality. These findings provide an important theoretical reference for the development of energy-efficient pruning equipment.