Underground winter bamboo shoots, prized for their high nutritional value and economic significance, face harvesting challenges owing to inefficient manual methods and the lack of specialized detection technologies. This review systematically evaluates current detection approaches, including manual harvesting, microwave detection, resistivity methods, and biomimetic techniques. While manual methods remain dominant, they suffer from labor shortages, low efficiency, and high damage rates. Microwave-based technologies demonstrate high accuracy and good depths but are hindered by high costs and soil moisture interference. Resistivity methods show feasibility in controlled environments but struggle with field complexity and low resolution. Biomimetic approaches, though innovative, face limitations in odor sensitivity and real-time data processing. Key challenges include heterogeneous soil conditions, performance loss, and a lack of standardized protocols. To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network-transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. These advancements aim to enhance detection accuracy, reduce labor dependency, and increase yields. Future research should prioritize edge-computing solutions, cost-effective sensor networks, and cross-disciplinary collaborations to bridge technical and practical gaps. The integration of intelligent technologies is poised to transform traditional bamboo forestry into automated, sustainable smart forest farms, addressing global supply demands while preserving ecological integrity.

The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

This article presents a critical discussion of the repercussion of overshooting effects on element tests and finite-element simulations. For exemplification purposes, three advanced constitutive models for sands that had already achieved a certain level of accuracy in the simulation of monotonic and cyclic loading were carefully selected; namely, a bounding surface plasticity model, a hypoplastic model with intergranular strain, and a hypoplastic model with elastoplastic anisotropic intergranular strain. Cyclic loading laboratory data and scale tests on Karlsruhe fine sand were considered to support the analyses. The obtained results suggest that the overshooting issue is one of the most serious limitations of the selected models and has a major impact on elemental and finite-element simulations. Therefore, models' end-users should be aware of this drawback when performing simulations under certain conditions involving unloading-reloading episodes with different strain amplitudes.

Background and aimsCadmium (Cd) contamination poses a potential threat to plant growth and human health. In this study, we aimed to determine the effect of selenium nanoparticles (SeNPs) on Cd and selenium (Se) uptake and accumulation in bok choy, and investigate the detoxification mechanism of SeNPs on bok choy under Cd stress.MethodsA pot culture was performed in Cd-contaminated soil with soil applied and foliar-sprayed SeNPs, including SLow, SHigh, FLow, FHigh, and corresponding control treatment. The soil available Cd content, Cd and Se fractions in soil, elements accumulation, subcellular Cd/Se distribution, MDA content, SOD activity, and Fourier transformed infrared spectroscopy (FTIR) were evaluated.ResultsSoil applied SeNPs significantly reduced Cd concentration by 25.9-42.4%, and Cd uptake rate by 33.4-37.8%. Further, soil applied SeNPs had no significant effect on available Cd but did affect Se fractions in soil. Additionally, soil applied SeNPs increased Se concentration by 3.1 - 6.3 times in bok choy and caused a higher Se concentration in root than in shoot, with the residual and organic matter-bound Se mainly affecting Se accumulation in shoot. However, foliar-sprayed SeNPs had no significant effect on Cd uptake but increased Se accumulation by 2.4 - 33.0 times in bok choy. Soil applied and foliar-sprayed SeNPs prompted the distribution of Cd in cell wall and in soluble component in shoot, respectively, which reduced the damage of Cd on organelle.ConclusionSoil applied SeNPs was an effective method for reducing Cd accumulation and improving Se biofortification and mineral elements accumulation in bok choy.