Bacterial speck caused by Pseudomonas syringae pv. tomato (Pst) is a major disease of tomatoes. Infection by Pst in the seedling stage could cause 75% of total yield loss and quality losses in tomatoes. Applying massive chemical bactericides to control this disease has caused undesirable pathogen resistance, environmental pollution, and threats to humans. Here, we developed a candidate antibacterial agent made from copper oxide nanoparticles loaded onto the surfaces of graphene oxide sheets (GO–Cu NPs). A series of characterization measurements showed that the nanocomposite had been successfully prepared. Antibacterial activity results indicated that GO–Cu NPs had a 16-times higher antibacterial activity than Kocide 3000. From further investigation, the GO–Cu NPs composite could lead to damaging the cell structure, increasing the level of reactive oxygen species, and decreasing the content of DNA in Pst cells. From an in vivo test, GO–Cu NPs at 4 and 8 μg mL⁻¹ significantly reduced the severity of bacterial speck below 25% and the tomatoes had no phytotoxicity. Comparatively, 125 and 250 μg mL⁻¹ were required for Kocide 3000 to achieve similar effects. Therefore, GO–Cu NPs composites as a high-efficiency biocide have great potential for managing crop diseases.

Bacterial leaf blight of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is a major disease of rice, leading to reduction in production by 10–50%. In order to control this disease, various chemical bactericides have been used. Wide and prolonged application of chemical bactericides resulted in the resistant strain of Xoo that was isolated from rice. To address this problem, we were searching for an environmentally friendly alternative to the commonly used chemical bactericides. In this work, we demonstrate that silicon dioxide nanospheres loaded with silver nanoparticles (SiO₂-Ag) can be prepared by using rice husk as base material precursor. The results of the antibacterial tests showed that SiO₂-Ag composites displayed antibacterial activity against Xoo. At cellular level, the cell wall/membrane was damaged and intercellular contents were leaked out by slow-releasing of silver ions from SiO₂-Ag composites. At molecular level, this composite induced reactive oxygen species production and inhibited DNA replication. Based on the results above, we proposed the potential antibacterial mechanism of SiO₂-Ag composites. Moreover, the cytotoxicity assay indicated that the composites showed mild toxicity with rice cells. Thus, this work provided a new strategy to develop biocide derived from residual biomass.

Despite much progress in modifying chitosan as an absorbent for wastewater treatment, it is still difficult for current chitosan-based adsorbents to achieve the desired removal effects towards basic dyes. In this study, chitosan-derived three-dimensional porous carbon (CTC) consisting of large-diameter channels and mesopores was prepared to remove methylene blue (MB) from wastewater. The results indicate that CTC has excellent performance for MB removal, and the maximum adsorption capacity was 925.93 mg g⁻¹ at 318 K. The adsorption isotherm and kinetics models of MB on CTC could be described well by the Langmuir isotherms and the pseudo-second-order rate model. An experiment to study the CTC removal of MB from a flowing aqueous solution was performed using a homemade device. The water treatment rate of CTC reached 250 L g⁻¹ h⁻¹, with high MB removal efficiency (>93.4%). Furthermore, the desorption–adsorption experiments indicate that CTC is also a reusable adsorbent that can be applied to recover MB from wastewater. The obtained CTC is a promising alternative for the current expensive absorbents and provides a concept for designing the three-dimensional (3D) structures of raw materials to improve adsorption capability.

Grasslands are the mainstay of terrestrial ecosystems and crucial ecological barriers, serving as the foundation for the development of grassland husbandry. However, the frequent occurrence of poisonous plants in grasslands weakens the stability of grassland ecosystems and constrains the growth of grassland livestock husbandry. To achieve early detection of the grassland weed Phlomoides umbrosa (Turcz.) Kamelin & Makhm, this study improves the YOLO-v8 model and proposes a BSS-YOLOv8 network model using UAV images. Using UAV, we can obtain early-stage image data of P. umbrosa and build a seedling dataset. To address challenges such as the complex grassland background and the dwarf seedlings of P. umbrosa, this study incorporated the BoTNet module into the backbone network of the YOLO-v8 model. Enhancing the integrity of feature extraction by linking global and local features through its multi-head self-attention mechanism (MHSA). Additionally, a detection layer was added in the model’s neck structure with an output feature map scale of 160 × 160 to further integrate P. umbrosa feature details from the shallow neural network, thereby strengthening the recognition of small target P. umbrosa. The use of GSConv, as a replacement for some standard convolutions, not only reduced model computational complexity but also further improved its detection performance. Ablation test results reveal that the BSS-YOLOv8 network model achieved a precision of 91.1%, a recall rate of 86.7%, an mAP50 of 92.6%, an F1-Score of 88.85%, and an mAP50:95 of 61.3% on the P. umbrosa seedling dataset. Compared with the baseline network, it demonstrated respective improvements of 2.5%, 3.8%, 3.4%, 3.19%, and 4.4%. When compared to other object detection models (YOLO-v5, Faster R-CNN, etc.), the BSS-YOLOv8 model similarly achieved the best detection performance. The BSS-YOLOv8 proposed in this study enables rapid identification of P. umbrosa seedlings in grassland backgrounds, holding significant importance for early detection and control of weeds in grasslands.