[1] |
曾雄生. 从“麦饭”到“馒头”: 小麦在中国[J]. 生命世界, 2007(9): 8-13.
|
|
ZENG X S. From “wheat rice” to “steamed bread”-wheat in China[J]. Life World, 2007(9): 8-13. (in Chinese with English abstract)
|
[2] |
刘石. 种子产业的价值空间[J]. 种子世界, 2012(5): 54.
|
|
LIU S. Value space of seed industry[J]. Seed World, 2012(5): 54. (in Chinese with English abstract)
|
[3] |
李彧. 浅谈如何做好种子发芽试验[J]. 种子科技, 2007, 25(4): 45-46.
|
|
LI Y. Talking about how to do well the seed germination test[J]. Seed Science & Technology, 2007, 25(4): 45-46. (in Chinese with English abstract)
|
[4] |
国家技术监督局.农作物种子检验规程发芽试验:GB/T 3543.4—1995[S]. 北京: 中国标准出版社, 1995.
|
[5] |
党满意, 孟庆魁, 谷芳, 等. 基于机器视觉的马铃薯晚疫病快速识别[J]. 农业工程学报, 2020, 36(2): 193-200.
|
|
DANG M Y, MENG Q K, GU F, et al. Rapid recognition of potato late blight based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(2): 193-200. (in Chinese with English abstract)
|
[6] |
张晗, 王成, 董宏图, 等. 基于机器视觉的白菜种子精选方法研究[J]. 农机化研究, 2021, 43(12): 31-36.
|
|
ZHANG H, WANG C, DONG H T, et al. Study on the seed selection method of cabbage based on machine vision[J]. Journal of Agricultural Mechanization Research, 2021, 43(12): 31-36. (in Chinese with English abstract)
|
[7] |
袁加红, 朱德泉, 孙丙宇, 等. 基于机器视觉的水稻秧苗图像分割[J]. 浙江农业学报, 2016, 28(6): 1069-1075.
|
|
YUAN J H, ZHU D Q, SUN B Y, et al. Machine vision based segmentation algorithm for rice seedling[J]. Acta Agriculturae Zhejiangensis, 2016, 28(6): 1069-1075. (in Chinese with English abstract)
|
[8] |
李振, 廖同庆, 冯青春, 等. 基于机器视觉的蔬菜种子活力指数检测算法研究及系统实现[J]. 浙江农业学报, 2015, 27(12): 2218-2224.
|
|
LI Z, LIAO T Q, FENG Q C, et al. Study on vegetable seed vigor index detection algorithm and system realization based on machine vision[J]. Acta Agriculturae Zhejiangensis, 2015, 27(12): 2218-2224. (in Chinese with English abstract)
|
[9] |
张帆, 杨勇, 骆少明, 等. 穴盘苗发芽率在线视觉检测研究[J]. 西南大学学报(自然科学版), 2021, 43(10): 84-91.
|
|
ZHANG F, YANG Y, LUO S M, et al. Research of online vision detection for germination of plug seedlings[J]. Journal of Southwest University(Natural Science Edition), 2021, 43(10): 84-91. (in Chinese with English abstract)
|
[10] |
王纪章, 顾容榕, 孙力, 等. 基于Kinect相机的穴盘苗生长过程无损监测方法[J]. 农业机械学报, 2021, 52(2): 227-235.
|
|
WANG J Z, GU R R, SUN L, et al. Non-destructive monitoring of plug seedling growth process based on kinect camera[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(2): 227-235. (in Chinese with English abstract)
|
[11] |
金秀, 卢杰, 傅运之, 等. 基于深度卷积神经网络的小麦赤霉病高光谱病症点分类方法[J]. 浙江农业学报, 2019, 31(2): 315-325.
|
|
JIN X, LU J, FU Y Z, et al. A classification method for hyperspectral imaging of Fusarium head blight disease symptom based on deep convolutional neural network[J]. Acta Agriculturae Zhejiangensis, 2019, 31(2): 315-325. (in Chinese with English abstract)
|
[12] |
鲍烈, 王曼韬, 刘江川, 等. 基于卷积神经网络的小麦产量预估方法[J]. 浙江农业学报, 2020, 32(12): 2244-2252.
|
|
BAO L, WANG M T, LIU J C, et al. Estimation method of wheat yield based on convolution neural network[J]. Acta Agriculturae Zhejiangensis, 2020, 32(12): 2244-2252. (in Chinese with English abstract)
|
[13] |
杨万里, 段凌凤, 杨万能. 基于深度学习的水稻表型特征提取和穗质量预测研究[J]. 华中农业大学学报, 2021, 40(1): 227-235.
|
|
YANG W L, DUAN L F, YANG W N. Deep learning-based extraction of rice phenotypic characteristics and prediction of rice panicle weight[J]. Journal of Huazhong Agricultural University, 2021, 40(1): 227-235. (in Chinese with English abstract)
|
[14] |
李昊, 刘海隆, 刘生龙. 基于深度学习的柑橘病虫害动态识别系统研发[J]. 中国农机化学报, 2021, 42(9): 195-201.
|
|
LI H, LIU H L, LIU S L. Research on dynamic identification system of citrus diseases and pests based on deep learning[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(9): 195-201. (in Chinese with English abstract)
|
[15] |
ŠKRUBEJ U, ROZMAN Č, STAJNKO D. The accuracy of the germination rate of seeds based on image processing and artificial neural networks[J]. Agricultura, 2015, 12(1/2): 19-24.
|
[16] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, 2016: 779-788.
|
[17] |
REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2017: 6517-6525.
|
[18] |
REDMON J, FARHADI A. YOLO v3: all incremental improvement[C]// 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2018.
|
[19] |
ALEXEY B, CHIEN Y W, HONG Y M L.YOLOv4: optimal speed and accuracy of object detection[C]// 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2020.
|
[20] |
刘天真, 滕桂法, 苑迎春, 等. 基于改进YOLO v3的自然场景下冬枣果实识别方法[J]. 农业机械学报, 2021, 52(5): 17-25.
|
|
LIU T Z, TENG G F, YUAN Y C, et al. Winter jujube fruit recognition method based on improved YOLO v3 under natural scene[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(5): 17-25. (in Chinese with English abstract)
|
[21] |
权龙哲, 夏福霖, 姜伟, 等. 基于YOLO v4卷积神经网络的农田苗草识别研究[J]. 东北农业大学学报, 2021, 52(7): 89-98.
|
|
QUAN L Z, XIA F L, JIANG W, et al. Research on recognition of maize seedlings and weeds in maize mield based on YOLO v4 convolutional neural network[J]. Journal of Northeast Agricultural University, 2021, 52(7): 89-98. (in Chinese with English abstract)
|
[22] |
赵德安, 吴任迪, 刘晓洋, 等. 基于YOLO深度卷积神经网络的复杂背景下机器人采摘苹果定位[J]. 农业工程学报, 2019, 35(3): 164-173.
|
|
ZHAO D A, WU R D, LIU X Y, et al. Apple positioning based on YOLO deep convolutional neural network for picking robot in complex background[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(3): 164-173. (in Chinese with English abstract)
|
[23] |
张晴晴, 刘连忠, 宁井铭, 等. 基于YOLOV3优化模型的复杂场景下茶树嫩芽识别[J]. 浙江农业学报, 2021, 33(9): 1740-1747.
|
|
ZHANG Q Q, LIU L Z, NING J M, et al. Tea buds recognition under complex scenes based on optimized YOLOV3 model[J]. Acta Agriculturae Zhejiangensis, 2021, 33(9): 1740-1747. (in Chinese with English abstract)
|
[24] |
DU J. Understanding of object detection based on CNN family and YOLO[J]. Journal of Physics: Conference Series, 2018, 1004: 012029.
|
[25] |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2017: 2261-2269.
|
[26] |
REZATOFIGHI H, TSOI N, GWAK J, et al. Generalized intersection over union: a metric and a loss for bounding box regression[EB/OL]. arXiv, 2019: 1902. 09630. https://arxiv.org/abs/1902.09630.
|
[27] |
张红生, 王州飞. 种子学[M]. 3版. 北京: 科学出版社, 2021.
|