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With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F1-score, and parameters. It holds significant promise for applications in strawberry disease identification. [ABSTRACT FROM AUTHOR]

Underwater acoustic network (UAN) is a lightweight system used in inland waterway systems. Software-defined networking (SDN) is a promising approach to improve the supervision abilities and integration services. This paper introduces SM-UAN, an SDN-based UAN with multi-controllers. Firstly, a hierarchical framework of SM-UAN is offered. Secondly, a multi-flow table structure and a multi-flow processing method is built. Followed by, a distributed deployment model of controllers and a load balancing mechanism are established. Finally, we construct an experimental scenario on Mininet with WOSS-NS3. For the multi-flow table processing method, the simulation demonstrates that the compression ratio is promoted to 24.1% and 29.4% separately, the packet forwarding rate 14.7% and the processing delay 31.2%, when comparing SM-UAN with the item-by-item matching (IM) system. For the load balancing mechanism, it shows that the maximum load for a master controller is balanced to 0.55, while the minimum load for a slave controller 0.46, which proves that a fair load distribution achieves. In addition, SM-UAN is compared with the traditional single-sink-based UAN (TS-UAN), the traditional multi-sink-based UAN (TM-UAN), and the SDN-based UAN with a single controller (SS-UAN). The results reveal that the survival time of SM-UAN is extended to 32.7%, 24.9%, and 21.6%, respectively. Also, the bit error rate (BER) of that is less than 10-4. In conclusion, advantages of SM-UAN have been highlighted, which provides theoretical support for inland waterway systems.

Ultrathin all‐solid‐state electrolytes with an excellent Li+ transport behavior are highly desirable for developing high‐energy‐density solid‐state lithium metal batteries. However, how to balance the electrochemical performance and their mechanical properties remains a huge challenge. Herein, an ultrathin solid electrolyte membrane with a thickness of only 3 µm and a weight of 11.7 g m−2 is well constructed by integrating individual functionalized organic with inorganic modules. Impressively, the optimized hybrid electrolyte membrane shows a set of merits including a high room‐temperature ionic conductivity of 1.77 × 10−4 S cm−1, large Li+ transference number of 0.65, and strong mechanical strength (strength of 29 MPa, elongation of 95%), as well as negligible thermal shrink at 180 °C. The analysis results reveal that the lithium sulfonate‐functionalized mesoporous silica nanoparticles in the membrane play a crucial role in the selective transport of Li+ through anion trapping and cation exchange. The pouch full cell is further assembled with a high‐voltage NCM cathode and thin lithium anode, which exhibits excellent long‐term cycling stability, outstanding rate performance at room temperature, and high safety against abused conditions. The current work provides an innovative strategy for achieving lithium metal batteries with ultrathin all‐solid‐state electrolytes. [ABSTRACT FROM AUTHOR]

Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model's efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model's effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model's generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model's commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance. [ABSTRACT FROM AUTHOR]