[1] 齐月, 李俊生, 闫冰, 邓贞贞, 付刚. 化学除草剂对农田生态系统野生植物多样性的影响. 生物多样性, 2016, 24(2): 228-236.
Qi Y, Li J S, Yan B, Deng Z Z, Fu G. Impact of herbicides on wild plant diversity in agro-ecosystems: A review. Biodiversity Science, 2016, 24(2): 228-236. (in Chinese)
[2] 张小龙, 谢正春, 张念生, 曹成茂. 豌豆苗期田间杂草识别与变量喷洒控制系统. 农业机械学报, 2012, 43(11): 220-225, 73.
ZHANG X L, XIE Z C, ZHANG N S, CAO C M. Weed recognition from pea seedling images and variable spraying control system. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(11): 220-225, 73. (in Chinese)
[3] 徐艳蕾, 包佳林, 付大平, 朱炽阳. 多喷头组合变量喷药系统的设计与试验. 农业工程学报, 2016, 32(17): 47-54.
XU Y L, BAO J L, FU D P, ZHU Z Y. Design and experiment of variable spraying system based on multiple combined nozzles. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(17): 47-54. (in Chinese)
[4] 魏全全, 李岚涛, 任涛, 王振, 王少华, 李小坤, 丛日环, 鲁剑巍. 基于数字图像技术的冬油菜氮素营养诊断. 中国农业科学, 2015, 48(19): 3877-3886.
WEI Q Q, LI L T, REN T, WANG Z, WANG S H, LI X K, CONG R H, LU J W. Diagnosing nitrogen nutrition status of winter rapeseed via digital image processing technique. Scientia Agricultura Sinica, 2015, 48(19): 3877-3886. (in Chinese)
[5] 刘涛, 仲晓春, 孙成明, 郭文善, 陈瑛瑛, 孙娟. 基于计算机视觉的水稻叶部病害识别研究. 中国农业科学, 2014, 47(4): 664-674.
LIU T, ZHONG X C, SUN C M, GUO W S, CHEN Y Y, SUN J. Recognition of rice leaf diseases based on computer vision. Scientia Agricultura Sinica, 2014, 47(4): 664-674. (in Chinese)
[6] 唐俊, 邓立苗, 陈辉, 栾涛, 马文杰. 基于机器视觉的玉米叶片透射图像特征识别研究. 中国农业科学, 2014, 47(3): 431-440.
TANG J, Deng L M, CHEN H, LUAN T, MA W J. Research on maize leaf recognition of characteristics from transmission image based on machine vision. Scientia Agricultura Sinica, 2014, 47(3): 431-440. (in Chinese)
[7] 孟庆宽, 何洁, 仇瑞承, 马晓丹, 司永胜, 张漫, 刘刚. 基于机器视觉的自然环境下作物行识别与导航线提取. 光学学报, 2014, 34(7): 172-178.
MENG Q K, HE J, QIU R C, MA X D, SI Y S, ZHANG M, LIU G. Crop recognition and navigation line detection in natural environment based on machine vision. Acta Optic Sinica, 2014, 34(7): 172-178. (in Chinese)
[8] 刘哲, 李智晓, 张延宽, 张超, 黄健熙, 朱德海. 基于时序EVI决策树分类与高分纹理的制种玉米识别. 农业机械学报, 2015, 46(10): 321-327.
LIU Z, LI Z X, ZHANG Y K, ZHANG C, HUANG J X, ZHU D H. Seed maize identification based on time-series EVI decision tree classification and high resolution remote sensing texture analysis. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(10): 321-327. (in Chinese)
[9] 翟志强, 朱忠祥, 杜岳峰, 张硕, 毛恩荣. 基于Census变换的双目视觉作物行识别方法. 农业工程学报, 2016, 32(11): 205-213.
ZHAI Z Q, ZHU Z X, DU Y F, ZHANG S, MAO E R. Method for detecting crop rows based on binocular vision with Census transformation. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(11): 205-213. (in Chinese)
[10] 王璨, 李志伟. 利用融合高度与单目图像特征的支持向量机模型识别杂草. 农业工程学报, 2016, 32(15): 165-174.
WANG C, LI Z W. Weed recognition using SVM model with fusion height and monocular image features. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(15): 165-174. (in Chinese)
[11] 陈亚军, 赵博, 李树君, 刘磊, 苑严伟, 张延立. 基于多特征的杂草逆向定位方法与试验. 农业机械学报, 2015, 46(6): 257-262.
CHEN Y J, ZHAO B, LI S J, LIU L, YUAN Y W, ZHANG Y L. Weed reverse positioning method and experiment based on multi-feature. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(6): 257-262. (in Chinese)
[12] 赵川源, 何东健, 乔永亮. 基于多光谱图像和数据挖掘的多特征杂草识别方法. 农业工程学报, 2013, 29(2): 192-198.
ZHAO C Y, HE D J, QIAO Y L. Identification method of multi-feature weed based on multi-spectral images and data mining. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(2): 192-198. (in Chinese)
[13] 王璨, 武新慧, 李志伟. 基于卷积神经网络提取多尺度分层特征识别玉米杂草. 农业工程学报, 2018, 34(5): 144-151.
WANG C, WU X H, LI Z W. Recognition of maize and weed based on multi-scale hierarchical features extracted by convolutional neural network. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(5): 144-151. (in Chinese)
[14] Mccool C S, Perez T, Upcroft B. Mixtures of lightweight deep convolutional neural networks: applied to agricultural robotics. IEEE Robotics & Automation Letters, 2017,2(3): 1344-1351.
[15] Haug S, Michaels A, Biber P, Ostermann J. Plant classification system for crop/weed discrimination without segmentation//IEEE Winter Conference on Applications of Computer Vision. IEEE, 2014: 1142-1149.
[16] Potena C, Nardi D, Pretto A. Fast and accurate crop and weed identification with summarized train sets for precision agriculture// International Conference on Intelligent Autonomous Systems. Springer, 2016: 105-121.
[17] Milioto A, Lottes P, Stachniss C. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs // IEEE International Conference on Robotics and Automation. IEEE, 2018: 1-6.
[18] Chebrolu N, Lottes P, Schaefer A, Winterhalter W, Burgard W. Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. International Journal of Robotics Research, 2017, 36(10): 1045-1052.
[19] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述. 计算机学报, 2017, 40(6): 1229-1251.
ZHOU F Y, JIN L P, DONG J. Review of convolutional neural network. Chinese Journal of Computers, 2017, 40(6): 1229-1251. (in Chinese)
[20] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation // IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2015: 3431-3440.
[21] Garcia-Garcia A, Orts-Escolano S, Oprea S, VILLENA- MARTINEZ V, GARCIA-RODRIGUEZ J. A review on deep learning techniques applied to semantic segmentation. (2017-4-22)[2018-09- 26]. https://arxiv.org/abs/1704.06857.
[22] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation // International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2015: 234-241.
[23] Howard A G, Zhu M, Chen B, KALENICHENKO D, WANG W J, WEYAND T, ANDREETTO M, ADAM H. MobileNets: Efficient convolutional neural networks for mobile vision applications. (2017-04-17) [2018-09-26]. https://arxiv.org/abs/1704.04861.
[24] Ioffe S, Szegedy C. Batch Normalization: Accelerating deep network training by reducing internal covariate shift // Proceedings of the 32nd International Conference on Machine Learning. IMLS, 2015: 448-456.
[25] Chollet F. Xception: Deep learning with depthwise separable convolutions // IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017: 1800-1807.
[26] Zeiler M D, Taylor G W, Fergus R. Adaptive deconvolutional networks for mid and high level feature learning // International Conference on Computer Vision. IEEE Computer Society, 2011: 2018-2025.
[27] Chollet F.Keras. GitHub repository. (2017-03-15) [2018-9-26]. https://github.com/fchollet/keras.
[28] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for scene segmentation.(2016-10-10)[2018-09-26]. https://arxiv.org/abs/1511.00561. |