基于可见光图像的水稻颖花开花状态检测方法  

Method for detecting rice flowering spikelets using visible light images

作　　者：张亚莉[1,2] 肖文蔚 卢小阳 刘爱民 祁媛 刘含超 施泽坤 兰玉彬 Zhang Yali;Xiao Wenwei;Lu Xiaoyang;Liu Aimin;Qi Yuan;Liu Hanchao;Shi Zekun;Lan Yubin(College of Engineering,South China Agricultural University,Guangzhou 510642,China;National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology,Guangzhou 510642,China;Yuan Longping Agricultural High-tech Co.,Ltd.,Changsha 410125,China;College of Plant Protection,Hainan University,Haikou 570228,China;College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;College of Artificial Intelligence,South China Agricultural University,Guangzhou 510642,China)

机构地区：[1]华南农业大学工程学院,广州510642 [2]国家精准农业航空施药技术国际联合研究中心,广州510642 [3]袁隆平农业高科技股份有限公司,长沙410125 [4]海南大学植物保护学院,海口570228 [5]华南农业大学电子工程学院,广州510642 [6]华南农业大学人工智能学院,广州510642

出　　处：《农业工程学报》2021年第9期253-262,F0003,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省重点领域研发计划(2019B020221001);广东省科技计划项目(2018A050506073);广东省现代农业产业共性关键技术研发创新团队项目(2020KJ133);国家重点研发计划(2017YFD0701202)。

摘　　要：通过识别水稻开花张开颖花内外颖与吐出颖花花药的特征,进而准确判断颖花开花时间,是及时进行杂交水稻制种授粉的前提。该研究通过可见光相机获取水稻颖花图像,基于可见光蓝色通道串联大津法(Series Otsu,SOtsu)提取颖花花药,同时使用深度学习算法基于区域的快速卷积神经网络(Faster Regional Convolutional Neural Network,FasterRCNN)及YOLO-v3识别颖花花药与张开颖花内外颖,通过对比不同算法识别精确率、召回率、F1系数以及皮尔逊相关性系数,研究适用于识别颖花开花状态的特征与方法。结果显示,FasterRCNN算法检测张开颖花内外颖精确率达1,召回率达0.97,F1系数为0.98,皮尔逊相关系数为0.993,串联大津法检测吐出花药精确率达0.92,召回率达0.93,F1系数为0.93,皮尔逊相关系数为0.936。这表明串联大津法与FasterRCNN算法适用于水稻颖花开花状态检测,且张开颖花内外颖比吐出花药更适合作为水稻开花状态特征应用于深度学习算法检测。串联大津法可代替FasterRCNN算法在模型构建完成前进行检测,保证水稻颖花开花状态检测连续性。Rice flowering spikelets bloom generally at 10:00-12:00,especially when the temperature is 24-35℃and the relative humidity is 70%-90%.Therefore,the flowering time is necessary to be accurately determined for the timely pollination in the production of hybrid rice seed.In this study,the images were captured by a visible light camera at two flowering characteristics,including the opening of spikelet hull,and the emesis of spikelet anthers.Series Otsu(SOtsu)was applied in tandem to extract the spikelet anthers through the visible light blue channel.An attempt was made to detect the flowering status of rice glumes using visible images,in order to meet the needs of hybrid rice seed pollination.A Canon single-lens reflex(SLR)camera was adopted for data acquisition,which was a benefit to segment the image using the tandem SOtsu.Deep learning models,such as FasterRCNN and YOLO-v3,were used to identify the spikelet anthers and the opening spikelet hull.The most suitable method was selected for flowering characteristics detection to compare the precision,recall,and the F1 coefficient of different models.Two datasets of visible light images were set for spikelets(15 cm and 45 cm imaging distance),each of which used two characteristics.A labeling software was applied to label the category and position of images.As such,a sample database was established for the training of detection models with deep learning.The performance of three models,including SOtsu,FasterRCNN,and YOLO-v3,were evaluated,where the detection was verified from multiple angles.The experiment was also conducted for the model robustness as well.The maximum inter-class variance was utilized in the SOtsu to separate the foreground(rice)from the background using the grayscale image of B-channel,where the grayscale of the background was set to be zero.An analysis was then made for the maximum inter-class variance that applied independently in the pixel range of extracted region,and then the spikelet anthers were further separated from the spikelets hull.The ori

关 键 词：图像识别 对象识别 水稻 颖花 颖花内外颖 花药 串联大津法 深度学习 

分 类 号：S252.3[农业科学—农业机械化工程]