| 基于深度学习的玉米拔节期冠层识别 |
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| 引用本文： | 孙红,乔金博,李松,李民赞,宋迪,乔浪.基于深度学习的玉米拔节期冠层识别[J].农业工程学报,2021,37(21):53-61. |
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| 作者姓名： | 孙红 乔金博 李松 李民赞 宋迪 乔浪 |
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| 作者单位： | 1. 中国农业大学，现代精细农业系统集成研究教育部重点实验室，北京 100083;2. 中国农业大学，农业部农业信息获取技术重点实验室，北京 100083 |
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| 基金项目： | 国家重点研发计划(2019YFE0125500)；国家自然科学基金资助项目(31971785和31971786)；中国农业大学研究生培养项目（QYJC202101，JG202026，JG2019004和YW2020007） |
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| 摘 要： | 为了满足田间玉米植株快速识别与检测的需求，针对玉米拔节期提出了基于深度学习的冠层识别方法，比较并选取了适于玉米植株精准识别和定位的网络模型，并研制了玉米植株快速识别和定位检测装置。首先拍摄玉米苗期和拔节期图像共计3 000张用于训练深度学习模型，针对拔节期玉米叶片交叉严重的问题，提出了以玉米株心取代玉米整株对象的标记策略。其次在Google Colab云平台训练SSDLite-MobileDet网络模型。为了实现田间快速检测，开发了基于树莓派4B+Coral USB的玉米冠层快速检测装置。结果表明，田间玉米冠层识别模型精度达到91%，检测视频的帧率达到89帧/s以上。研究成果可为田间玉米高精度诊断和精细化作业管理奠定基础。
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| 关 键 词： | 深度学习 目标检测 识别 拔节期 玉米冠层 |
| 收稿时间： | 2021/7/16 0:00:00 |
| 修稿时间： | 2021/11/17 0:00:00 |
Recognition of the maize canopy at the jointing stage based on deep learning |
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| Sun Hong,Qiao Jinbo,Li Song,Li Mingzan,Song Di,Qiao Lang.Recognition of the maize canopy at the jointing stage based on deep learning[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(21):53-61. |
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| Authors: | Sun Hong Qiao Jinbo Li Song Li Mingzan Song Di Qiao Lang |
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| Institution: | 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 1000831, China;;2. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China |
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| Abstract: | Abstract: A rapid and accurate detection of a maize plant has been widely determined the food safety and quality assurance in modern agriculture. However, there is a great challenge on the complex environment of canopy leaves at the jointing stage. It is also necessary to select a feasible network model suitable for the precise identification and positioning of maize plants in the field. In this study, a novel device was developed for canopy recognition during the maize jointing stage using deep learning. A total of 3000 images of maize in the seedling and jointing stages were also taken to train the model of deep learning. Furthermore, a labeling strategy was proposed for the sample set, where the whole maize object was replaced with the center of the maize plant. Some data enhancement techniques were utilized to increase the robustness and generalization ability of the identification model for maize plants, including the passive brightness, chroma, rotation, mirroring, and blurring. After that, three datasets of maize plants were achieved, including 10 500 training sets, 3 000 cross-validation sets, and 300 test sets. In the model training, the SSDLite-MobileDet network model was first trained on the Google Colab cloud platform, and then compared with the SSDLite-MobileNetV3 model. Finally, an optimal SSDLite-MobileDet model was achieved with a speed of 110 frames/s and an accuracy of 92.4%. More importantly, some strategies were proposed to improve the performance of the model. 1) The specific procedure of extraction was visualized for the maize images during model training using a convolutional neural network (CNN). It was found that the target information was significantly enriched in the output feature mapping, as the number of CNN layers deepened gradually. 2) The heart of the plant was labelled to avoid the serious leaf crossing at the jointing stage of maize. Thus, the novel model presented an average recognition accuracy of 92.4% under severe leaf crossing and different lighting conditions. 3) A rapid detection device was built for the maize crops with Raspberry Pi 4B+Coral USB, including Raspberry Pi 4B control, accelerator fast calculation, camera image acquisition, power, and image display module. As such, a rapid and accurate platform was obtained to collect, process, and display the images of the maize canopy in the field. 4) The model that trained on the GPU was quantized to transplant the trained model to the device. The data type of FP32 was converted to INT8, thereby ensuring that the quantized model occupied less memory. 5) A software was designed to run the SSDLite-MobileDet lightweight model on Raspberry Pi 4B, further to realize the acquisition, recognition, and display of maize images. The specific operation included system initialization, model loading, images acquisition, model processing, maize canopy recognition, positioning, and display. Finally, a field experiment was carried out to evaluate the rapid detection device for the maize crops, where the frame rate of the detected video was more than 89 frame/s, and the application recognition accuracy rate reached 91%. The findings can also offer strong support to the high-precision diagnosis and refined operation of maize in the field. |
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| Keywords: | deep learning target detection recognition jointing stage maize canopy |
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