Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime

Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN

In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions

Immune-Enhancing Activity of Aqueous Extracts from Artemisia rupestris L. via MAPK and NF-kB Pathways of TLR4/TLR2 Downstream in Dendritic Cells

Artemisia rupestris L. has long been used as a traditional herbal medicine owing to its immunomodulatory activity. Aqueous extracts of Artemisia rupestris L. (AEAR) contain the main functional component and can activate the maturation of dendritic cells (DCs) and enhance the adaptive immunity as the adjuvant against infections. To explore the underlying mechanism of immunomodulatory activities of AEAR, DCs were produced from bone-marrow cells of mice and the effects of AEAR on cell viability were assessed by the Cell Counting Kit 8 (CCK8) method and annexin V/propidium iodide staining assays. Then, the effects of AEAR on the morphology, maturation, and function of DCs were detected using a microscope, flow cytometry-based surface receptor characterization, and endocytosis assays. The secretion levels of cytokines were then analyzed with enzyme-linked immunosorbent assay (ELISA). The activation state of DCs was evaluated by the mixed lymphocyte reaction (MLR). The activity of MAPKs and NF-κB pathways, which were involved in the regulation of AEAR on DCs, was further detected by Western blot. AEAR did not have a cytotoxic effect on DCs or mouse splenocytes. AEAR remarkably enhanced the phenotypic maturation of DCs and promoted the expression of costimulatory molecules and the secretion of cytokines in DCs. AEAR also significantly decreased the phagocytic ability of DCs and augmented the abilities of DCs to present antigens and stimulate allogeneic T-cell proliferation. Simultaneously, AEAR potently activated toll-like receptor (TLR)4-/TLR2-related MAPKs and induced the degradation of IκB and the translocation of NF-κB. In short, AEAR can profoundly enhance the immune-modulating activities of DCs via TLR4-/TLR2-mediated activation of MAPKs and NF-κB signaling pathways and is a promising candidate immunopotentiator for vaccines

Acupuncture Induces the Proliferation and Differentiation of Endogenous Neural Stem Cells in Rats with Traumatic Brain Injury

Purpose. To investigate whether acupuncture induced the proliferation and differentiation of endogenous neural stem cells (NSCs) in a rat model of traumatic brain injury (TBI). Methods. 104 Sprague-Dawley rats were randomly divided into normal, model, and acupuncture groups. Each group was subdivided into three-day (3 d), seven-day (7 d), and fourteen-day (14 d) groups. The rat TBI model was established using Feeney’s freefall epidural impact method. The rats in the acupuncture group were treated at acupoints (Baihui, Shuigou, Fengfu, Yamen, and bilateral Hegu). The normal and model groups did not receive acupuncture. The establishment of the rat TBI model and the therapeutic effect of acupuncture were assessed using neurobehavioral scoring and hematoxylin-eosin staining. The proliferation and differentiation of NSCs in TBI rats were analyzed using immunofluorescence microscopy. Results. The levels of nestin-expressing cells and bromodeoxyuridine/glial fibrillary acidic protein- (BrdU/GFAP-) and BrdU/S100 calcium-binding protein B-positive and BrdU/microtubule-associated protein 2- and BrdU/galactocerebrosidase-positive cells were more significantly increased at various time points in the acupuncture group than in the model group (P<0.01), except for a decreased level of BrdU/GFAP-positive cells at 7 d and 14 d. Conclusion. Acupuncture induced the proliferation and differentiation of NSCs, thereby promoting neural repair in the TBI rats

High-Performance All-Solid-State Polymer Electrolyte with Controllable Conductivity Pathway Formed by Self-Assembly of Reactive Discogen and Immobilized via a Facile Photopolymerization for a Lithium-Ion Battery

All-solid-state polymer electrolytes (SPEs) have aroused great interests as one of the most promising alternatives for liquid electrolyte in the next-generation high-safety, and flexible lithium-ion batteries. However, some disadvantages of SPEs such as inefficient ion transmission capacity and poor interface stability result in unsatisfactory cyclic performance of the assembled batteries. Especially, the solid cell is hard to be run at room temperature. Herein, a novel and flexible discotic liquid-crystal (DLC)-based cross-linked solid polymer electrolyte (DLCCSPE) with controlled ion-conducting channels is fabricated via a one-pot photopolymerization of oriented reactive discogen, poly­(ethylene glycol)­diacrylate, and lithium salt. The experimental results indicate that the macroscopic alignment of self-assembled columns in the DLCCSPEs is successfully obtained under annealing and effectively immobilized via the UV photopolymerization. Because of the existence of unique oriented structure in the electrolytes, the prepared DLCCSPE films exhibit higher ionic conductivities and better comprehensive electrochemical properties than the DLCCSPEs without controlled ion-conductive pathways. Especially, the assembled LiFePO₄/Li cells with oriented electrolyte show an initial discharge capacity of 164 mA h g^–1 at 0.1 C and average specific discharge capacities of 143, 135, and 149 mA h g^–1 at the C-rates of 0.5, 1, and 0.2 C, respectively. In addition, the solid cell also shows the first discharge capacity of 124 mA h g^–1 (0.2 C) at room temperature. The outstanding cell performance of the oriented DLCCSPE should be originated from the macroscopically oriented and self-assembled DLC, which can form ion-conducting channels. Thus, combining the excellent performance of DLCCSPE and the simple one-pot fabricating process of the DLC-based all-solid-state electrolyte, it is believed that the DLC-based electrolyte can be one of the most promising electrolyte materials for the next-generation high-safety solid lithium-ion batteries