The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

The seed microbiome supports plant health and increases resilience under adverse environmental conditions. Seeds are also an important vector for transgenerational transfer of the plant microbiota. Even though research over the last decade has provided valuable insights into the functional roles of seed-associated microbes, these important members of the plant microbiome remain underexplored. This review systematically highlights recently discovered key functions of the seed microbiota. It covers taxonomic composition and diversity across plant species, transmission mechanisms, functional roles in germination and seedling establishment, growth promotion, and stress resistance. The review also addresses methodological challenges and highlights critical open questions regarding assembly, spatial compartmentalization, and translation into applications. Further research into seed microbiomes has the potential to not only increase the sustainability in plant production but also to increase food security in a changing climate. Reaching such outcomes will be facilitated by mechanistic studies that will disclose the remaining secrets of plant–microbe interplay at the very first developmental stage of most plants that nowadays inhabit Earth.

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.

Deep learning demonstrates strong generalisation capabilities, driving substantial progress in plant disease recognition systems. However, current methods are predominantly optimised for offline implementation. Real-time crop surveillance systems encounter streaming images containing novel disease classes in few-shot conditions, demanding incrementally adaptive models. This capability is called few-shot class-incremental learning (FSCIL). Here, we introduce VCPV—virtual contrastive constraints with prototype vector calibration—enabling sustainable plant disease classification under FSClL conditions. Specifically, our method consists of two phases: the base class training phase and the incremental training phase. During the base class training phase, the virtual contrastive class constraints (VCC) module is utilised to enhance learning from base classes and allocate sufficient embedding space for new plant disease images. In the incremental training phase, the prototype calibration embedding (PCE) module is introduced to distinguish newly arriving plant disease categories from previous ones, thereby optimising the prototype space and enhancing the recognition accuracy of new categories. We evaluated our approach on the PlantVillage dataset, and the experimental results under both 5-way 5-shot and 3-way 5-shot settings demonstrate that our method achieves state-of-the-art accuracy. At the same time, we achieved promising performance on the publicly available CIFAR-100 dataset. Furthermore, the visualisation results validate that our strategy effectively supports fine-grained, sustainable disease recognition, highlighting the potential of our approach to advance FSCIL in the field of plant disease monitoring.

Lycoris aurea Herb. (golden spider lily) exhibits a prolonged vegetative phase and delayed flowering, limiting its ornamental and commercial value. Gibbere