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Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring. [ABSTRACT FROM AUTHOR]

Enhancing extracellular electron transfer (EET) efficiency is crucial for improving the anaerobic digestion (AD) system's capability to treat recalcitrant wastewater. In this study, a novel S, N co-doped biochar (S-N-BC) was prepared through surface engineering to optimize EET within AD systems. The addition of S-N-BC significantly enhanced the performance of a mesophilic AD system treating Congo red wastewater, increasing the decolorization rate by 78 %, COD degradation rate by 82 %, and methane yield by 87 % compared to the control. Additionally, the shock resistance of anaerobic granular sludge was improved, as evidenced by the formation of the protective extracellular polymeric substances (EPS) barrier and the enhanced activities of the electron transport system. Mechanistic analysis revealed that adding S-N-BC did not alter the Congo red decolorization pathway but significantly enriched various electrochemically active bacteria and established EET pathways between microbial-pollutant and inter-microbial. This significantly accelerated EET efficiency within the AD system, ensuring stable and efficient operation under challenging conditions. This study proposed a novel approach using S-N-BC to simultaneously enhance "dual-pathway EET" between microbial-pollutant and inter-microbial while constructing an EPS protective barrier, addressing the issues of low efficiency and fragile stability of AD systems for treating recalcitrant wastewater., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)