[1] 邵阳, 杨忍, 安悦. 中国粮食生产空间演变及其影响因素[J]. 农业工程学报, 2025, 41(15): 265−277.

SHAO Yang, YANG Ren, AN Yue. Grain production spatial evolution and its influencing factors in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(15): 265−277.
[2] ROCHA J H T, GONÇALVES J L D M, GAVA J L, et al. Forest residue maintenance increased the wood productivity of a Eucalyptus plantation over two short rotations[J]. Forest Ecology and Management, 2016, 379: 1−10. DOI: 10.1016/j.foreco.2016.07.042.
[3] 魏建霞. 大豆玉米带状复合种植技术及病虫草害防治[J]. 农业开发与装备, 2022(8): 227−229.

WEI Jianxia. Strip compound planting technology of soybean and corn and its control of diseases, pests and weeds[J]. Agricultural Development & Equipments, 2022(8): 227−229. DOI: 10.3969/j.issn.1674-5329.2025.06.022.
[4] 王蓓, 何雨, 吴蓉, 等. 杂草稻生物学特性、发生与防控研究进展[J]. 浙江农林大学学报, 2019, 36(5): 1028−1036.

WANG Bei, HE Yu, WU Rong, et al. Research progress on biological characteristics, occurrence and control of Oryza sativa f. spontanea[J]. Journal of Zhejiang A&F University, 2019, 36(5): 1028−1036.
[5] 王冠, 王建新, 孙钰. 面向边缘计算的轻量级植物病害识别模型[J]. 浙江农林大学学报, 2020, 37(5): 978−985.

WANG Guan, WANG Jianxin, SUN Yu. Lightweight plant disease recognition model for edge computing[J]. Journal of Zhejiang A&F University, 2020, 37(5): 978−985. DOI: 10.11833/j.issn.2095-0756.20190595.
[6] 郭柏璋, 牟琦, 冀汶莉. 融合注意力机制的YOLOv5深度神经网络杂草识别方法[J]. 无线电工程, 2023, 53(12): 2771−2782.

GUO Baizhang, MOU Qi, JI Wenli. YOLOv5 deep neural network weed recognition method incorporating attention mechanism[J]. Radio Engineering, 2023, 53(12): 2771−2782. DOI: 10.3969/j.issn.1003-3106.2023.12.006.
[7] MESÍAS-RUIZ G A, PEÑA J M, de CASTRO A I, et al. Cognitive computing advancements: improving precision crop protection through UAV imagery for targeted weed monitoring[J]. Remote Sensing, 2024, 16(16): 3026. DOI: 10.3390/rs16163026.
[8] 孙文峰, 刘海洋, 王润涛, 等. 基于神经网络整定的PID控制变量施药系统设计与试验[J]. 农业机械学报, 2020, 51(12): 55−64, 94.

SUN Wenfeng, LIU Haiyang, WANG Runtao, et al. Design and experiment of PID control variable application system based on neural network tuning[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(12): 55−64, 94.
[9] 王润涛, 刘瑶, 王树文, 等. 基于模糊控制的车速跟随变量喷雾系统设计与试验[J]. 农业机械学报, 2022, 53(6): 110−117.

WANG Runtao, LIU Yao, WANG Shuwen, et al. Design and experiment of speed-following variable spray system based on fuzzy control[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(6): 110−117. DOI: 10.6041/j.issn.1000-1298.2022.06.011.
[10] MURATA H, MASUI S, TSUCHIDA Y. Efficacy evaluation of low-volume concentrate application of pesticides by unmanned aerial vehicle (UAV) using an indoor spraying device[J]. Applied Entomology and Zoology, 2024, 59(2): 103−110. DOI: 10.1007/s13355-023-00858-1.
[11] CHEN Yuli, LIU Zibo, LIN Zhen, et al. UAV-UGV cooperative targeted spraying system for honey pomelo orchard[J]. International Journal of Agricultural and Biological Engineering, 2024, 17(6): 22−31. DOI: 10.25165/j.ijabe.20241706.8989.
[12] 尚文卿, 齐红波. 基于改进Faster R-CNN与迁移学习的农田杂草识别算法[J]. 中国农机化学报, 2022, 43(10): 176−182.

SHANG Wenqing, QI Hongbo. Identification algorithm of field weeds based on improved Faster R-CNN and transfer learning[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(10): 176−182. DOI: 10.13733/j.jcam.issn.2095-5553.2022.10.025.
[13] 施武, 袁伟皓, 杨梦道, 等. 一种基于改进YOLOv8n-seg的轻量化茶树嫩芽的茶梗识别模型[J]. 江苏农业学报, 2025, 41(1): 75−86.

SHI Wu, YUAN Weihao, YANG Mengdao, et al. A lightweight model for identifying the stalks of tea buds based on the improved YOLOv8n-seg[J]. Jiangsu Journal of Agricultural Sciences, 2025, 41(1): 75−86. DOI: 10.3969/j.issn.1000-4440.2025.01.010.
[14] 杨凡, 杨博凯, 李荣荣. 基于图像分割和深度学习的人造板表面缺陷检测[J]. 浙江农林大学学报, 2024, 41(1): 176−182.

YANG Fan, YANG Bokai, LI Rongrong. Surface defect detection technology of wood-based panel based on image segmentation and deep learning[J]. Journal of Zhejiang A&F University, 2024, 41(1): 176−182. DOI: 10.11833/j.issn.2095-0756.20230280.
[15] JIN Xiaojun, CHE Jun, CHEN Yong. Weed identification using deep learning and image processing in vegetable plantation[J]. IEEE Access, 2021, 9: 10940−10950. DOI: 10.1109/ACCESS.2021.3050296.
[16] MO Rongyun, LAI Shenqi, YAN Yan, et al. Dimension-aware attention for efficient mobile networks[J]. Pattern Recognition, 2022, 131: 108899. DOI: 10.1016/j.patcog.2022.108899.
[17] HE Junjie, ZHANG Shihao, YANG Chunhua, et al. Pest recognition in microstates state: an improvement of YOLOv7 based on spatial and channel reconstruction convolution for feature redundancy and vision transformer with bi-level routing attention[J]. Frontiers in Plant Science, 2024, 15: 1327237. DOI: 10.3389/fpls.2024.1327237.
[18] WU Peishu, LI Han, ZENG Nianyin, et al. FMD-Yolo: an efficient face mask detection method for COVID-19 prevention and control in public[J]. Image and Vision Computing, 2022, 117: 104341. DOI: 10.1016/j.imavis.2021.104341.
[19] 窦汉杰, 翟长远, 王秀, 等. 基于LiDAR的果园对靶变量喷药控制系统设计与试验[J]. 农业工程学报, 2022, 38(3): 11−21.

DOU Hanjie, ZHAI Changyuan, WANG Xiu, et al. Design and experiment of the orchard target variable spraying control system based on LiDAR[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(3): 11−21. DOI: 10.11975/j.issn.1002-6819.2022.03.002.
[20] 翟长远, 张焱龙, 邹伟, 等. 基于农药喷施溯源的精准变量喷药监控系统设计与试验[J]. 农业机械学报, 2024, 55(2): 160−169.

ZHAI Changyuan, ZHANG Yanlong, ZOU Wei, et al. Design and test of precision variable-rate spray monitoring and control system based on pesticide spraying traceability[J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55(2): 160−169. DOI: 10.6041/j.issn.1000-1298.2024.02.015.
[21] RAI N, SUN Xin. WeedVision: a single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images[J]. Computers and Electronics in Agriculture, 2024, 219: 108792. DOI: 10.1016/j.compag.2024.108792.
[22] LI Zhuolin, WANG Dashuai, YAN Qing, et al. Winter wheat weed detection based on deep learning models[J]. Computers and Electronics in Agriculture, 2024, 227: 109448. DOI: 10.1016/j.compag.2024.109448.
[23] 聂远, 周厚奎, 张广群, 等. 基于增强知识蒸馏的小样本植物病害识别方法[J]. 浙江农林大学学报, 2025, 42(4): 667−676.

NIE Yuan, ZHOU Houkui, ZHANG Guangqun, et al. Few-shot learning for plant disease recognition with enhanced knowledge distillation[J]. Journal of Zhejiang A&F University, 2025, 42(4): 667−676.
[24] 杨勤元, 王栋, 杨奇, 等. 小麦田禾本科杂草防治药剂筛选[J]. 陕西农业科学, 2024, 70(10): 48−51,72.

YANG Qinyuan, WANG Dong, YANG Qi, et al. Screening of weed control agents for Poaceae in wheat fields[J]. Shaanxi Journal of Agricultural Sciences, 2024, 70(10): 48−51,72. DOI: 10.3969/j.issn.0488-5368.2024.10.010.
[25] de MOL F, FRITZSCHE R, GEROWITT B. Weed biodiversity and herbicide intensity as linked via a decision support system[J]. Pest Management Science, 2025, 81: 6667−6677. DOI: 10.1002/ps.70019.