As an emerging energy carrier, hydrogen presents a potential opportunity to increase the flexibility and resiliency of the integrated power and transportation system having ultrahigh penetration of renewable generation. This article studies a conceptual integrated energy-mobility system (IEMS) based on renewable-to-hydrogen stations (RHSs) and tank truck fleets. In an RHS, the renewables power the grid while also charge a hydrogen energy storage unit that can perform power-to-hydrogen (P2H) and hydrogen-to-power (H2P) operations. Besides, using tank truck fleets, the RHS will distribute its surplus hydrogen products to local consumers, e.g., refueling stations serving hydrogen fuel cell vehicles. The operation framework of the IEMS is analyzed. And some preprocessing technologies are developed to reduce modeling complexity. Then, a joint optimal scheduling model, involving renewable generationcontract, P2H, and H2P of the RHS, hydrogen product demands of the local consumers, and vehicle routing problem (VRP) of the truck fleets, is established. Afterwards, the formulated problem is solved by a heuristic method based on the exhaustive search and genetic algorithm. Numerical results verify the effectiveness of the joint optimal scheduling model and illustrate that the IEMS has the potentials to promote hydrogen-integrated transportation and power systems.

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and mAP@.5:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in mAP@.5:.95. The model has shown robustness and effectiveness in accurately detecting various pests.

A scalable, transparent, and breathable bioinspired plant-friendly wearable sensor is developed for detecting plant microenvironment, proficiently mitigating abiotic stresses and markedly minimizing potential damage to plants.

Forests play a vital role in increasing carbon sequestration in the biosphere. In recent years, segmenting forest canopy images in order to obtain various plant population parameters has become an essential means to assess the ecosystem. The objective of forest canopy image segmentation is to separate and extract sky regions from the background. This study proposes a hybrid method based on improved tuna swarm optimization (ITSO) for forestry canopy image segmentation. The symmetric cross-entropy is introduced to perform forestry canopy image thresholding by modeling the classes of an image as membership functions. In order to achieve the optimal thresholds of the forest canopy image, the entropy-solving procedure is arduous and time-consuming. In order to resolve this issue, the ITSO method was adopted to search for the most significant threshold. Meanwhile, the Tent chaotic map is used to initialize the tuna population according to the chaotic factor. The experiment is carried out on four different types of forest canopy images, with four indices (MAE, RVD, IoU, and ASD) used for quantitative analysis. The experiment’s results show that the ITSO-based segmentation method outperforms others, making it a better way to segment images of forest canopies.

Introduction: Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method.Methods: The proposed method integrates hyperspectral image fusion with a multi-strategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. Results: The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. Discussion: The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties.