Xin Zhang

Site-Specific Weed Management (SSWM) provides precise weed control and reduces the use of herbicides, which not only reduces the risk of environmental damage but also improves agricultural productivity. Accurate and efficient weed detection is the foundation for SSWM. However, complex field environments and small-target weeds in fields pose challenges for their detection. To address the above limitation, we developed WeedDETR, a real-time end-to-end detection model specifically designed to enhance the detection of small-target weeds in unmanned aerial vehicle (UAV) imagery. WeedDETR incorporates RepCBNet, a backbone network optimized through structural re-parameterization, to improve fine-grained feature extraction and accelerate inference. In addition, the designed feature complement fusion module (FCFM) was used for multi-scale feature fusion to alleviate the problem of small-target weed information being ignored in the deep network. During training, varifocal loss was used to focus on high-quality weed samples. We experimented on a new dataset, GZWeed, which contains weed imagery captured by an UAV. The experimental results demonstrated that WeedDETR achieves 73.9% and 91.8% AP0.5 (average precision at 0.5 intersection over union threshold) in the weed and Chinese cabbage [Brassica rapa subsp. chinensis (L.) Hanelt] categories, respectively, while achieving an inference speed of 76.28 FPS (frames per second). In comparison to YOLOv5-L, YOLOv6-M, and YOLOv8-L, WeedDETR demonstrated superior accuracy and speed, exhibiting 3.5%, 6.3%, and 3.6% higher AP0.5 for weed categories, while FPS was 14.9%, 12.9%, and 1.4% higher, respectively. The innovative architectural design of WeedDETR significantly enhances the detection accuracy of small-target weeds, enables efficient end-to-end weed detection. The proposed method establishes a solid technological foundation for UAV-based precision weeding systems in field conditions, advancing the development of deep learning-driven intelligent weed management.

BACKGROUND: Semantic segmentation of weed and crop images is a key component and prerequisite for automated weed management. For weeds in unmanned aerial vehicle (UAV) images, which are usually characterized by small size and easily confused with crops at early growth stages, existing semantic segmentation models have difficulties to extract sufficiently fine features. This leads to their limited performance in weed and crop segmentation of UAV images.RESULTS: We proposed a fine‐grained feature‐guided UNet, named FG‐UNet, for weed and crop segmentation in UAV images. Specifically, there are two branches in FG‐UNet, namely the fine‐grained feature branch and the UNet branch. In the fine‐grained feature branch, a fine feature‐aware (FFA) module was designed to mine fine features in order to enhance the model's ability to segment small objects. In the UNet branch, we used an encoder–decoder structure to realize high‐level semantic feature extraction in images. In addition, a contextual feature fusion (CFF) module was designed for the fusion of the fine features and high‐level semantic features, thus enhancing the feature discrimination capability of the model. The experimental results showed that our proposed FG‐UNet, achieved state‐of‐the‐art performance compared to other semantic segmentation models, with mean intersection over union (MIOU) and mean pixel accuracy (MPA) of 88.06% and 92.37%, respectively.CONCLUSION: The proposed method in this study lays a solid foundation for accurate detection and intelligent management of weeds. It will have a positive impact on the development of smart agriculture. © 2024 Society of Chemical Industry.

Accurate identification of plant diseases is important for ensuring the safety of agricultural production. Convolutional neural networks (CNNs) and visual transformers (VTs) can extract effective representations of images and have been widely used for the intelligent recognition of plant disease images. However, CNNs have excellent local perception with poor global perception, and VTs have excellent global perception with poor local perception. This makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition tasks. In this paper, we propose a local and global feature-aware dual-branch network, named LGNet, for the identification of plant diseases. More specifically, we first design a dual-branch structure based on CNNs and VTs to extract the local and global features. Then, an adaptive feature fusion (AFF) module is designed to fuse the local and global features, thus driving the model to dynamically perceive the weights of different features. Finally, we design a hierarchical mixed-scale unit-guided feature fusion (HMUFF) module to mine the key information in the features at different levels and fuse the differentiated information among them, thereby enhancing the model's multiscale perception capability. Subsequently, extensive experiments were conducted on the AI Challenger 2018 dataset and the self-collected corn disease (SCD) dataset. The experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the AI Challenger 2018 dataset and the SCD dataset, with accuracies of 88.74% and 99.08%, respectively.

The aging phenomenon is commonly observed in quantum-dot light emitting diodes (QLEDs), involving complex chemical or physical processes. Resolving the underlying mechanism of these aging issues is crucial to deliver reliable electroluminescent devices in future display applications. Here, we report a reversible positive aging phenomenon that the device brightness and efficiency significantly improve after device operation, but recover to initial states after long-time storage or mild heat treatment, which can be termed as warming-up effects. Steady and transient equivalent circuit analysis suggest that the radiative recombination current dramatically increases but electron leakage from the quantum dots (QDs) to hole transport layer becomes more accessible during the warming-up process. Further analysis discloses that the notable enhancement of device efficiency can be ascribed to the filling of shell traps in gradient alloyed QDs. This work reveals a distinct positive aging phenomenon featured with reversibility, and further guidelines would be provided to achieve stable QLED devices in real display applications.

In a cognitive satellite network (CSN) with GEO and LEO satellites, there is a large propagation losses between the sensing satellite and the ground station. The results of spectrum sensing from a single satellite may be inaccurate, which will create serious interference in the primary satellite system. Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model‐driven. They are difficult to model and implement in CSNs since their detection performance is strongly dependent on an assumed statistical model. Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self‐attention (SA) modules, long short‐term memory networks (LSTMs), and soft fusion networks, called CSL‐SFNet. This scheme combines the advantages of CNNs, SA modules, and LSTMs to extract the features of the input signals from the spatial and temporal domains. Additionally, the CSL‐SFNet makes use of a novel soft fusion technique that improves detection performance while also considerably reducing communication overhead. The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal‐to‐noise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.

Introduction: Corn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.Methods: To address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.Results: Compared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.Discussion: Overall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control.