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Smart Agriculture ?? 2021, Vol. 3 ?? Issue (2): 88-99.doi: 10.12133/j.smartag.2021.3.2.202103-SA003

? 信息處理與決策 ? 上一篇    下一篇

田間玉米苗期高通量動態監測方法

張小青1,2,3(), 邵松1,2, 郭新宇1,2, 樊江川1,2()   

  1. 1.北京農業信息技術研究中心,北京 100097
    2.國家農業信息化工程技術研究中心/數字植物北京市重點實驗室,北京 100097
    3.上海海洋大學 信息學院,上海 201306
  • 收稿日期:2021-03-11 修回日期:2021-05-17 出版日期:2021-06-30 發布日期:2021-08-25
  • 基金資助:
    國家自然科學基金面上項目(31871519);現代農業產業技術體系專項資金資助(CARS-02);北京市農林科學院院改革與發展項目
  • 作者簡介:張小青(1995-),女,碩士研究生,研究方向為深度學習與圖像處理。E-mail:15151935830@163.com。
  • 通訊作者: 樊江川 E-mail:15151935830@163.com;fanjc@ nercita.org.cn

High-Throughput Dynamic Monitoring Method of Field Maize Seedling

ZHANG Xiaoqing1,2,3(), SHAO Song1,2, GUO Xinyu1,2, FAN Jiangchuan1,2()   

  1. 1.Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    2.Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3.College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
  • Received:2021-03-11 Revised:2021-05-17 Online:2021-06-30 Published:2021-08-25
  • corresponding?author: Jiangchuan FAN E-mail:15151935830@163.com;fanjc@ nercita.org.cn

摘要:

目前對玉米出苗動態檢測監測主要是依靠人工觀測,耗時耗力且只能選擇小的樣方估算整體出苗情況。為解決人工出苗動態管理不精準的問題,實現田間精細化管理,本研究以田間作物表型高通量采集平臺獲取的高時序可見光圖像和無人機平臺獲取的可見光圖像兩種數據源構建了不同光照條件下的玉米出苗過程圖像數據集??紤]到田間存在環境背景復雜、光照不均等因素,在傳統Faster R-CNN的基礎上構建殘差單元,使用ResNet50作為新的特征提取網絡來對Faster R-CNN進行優化,首先實現對復雜田間環境下玉米出苗識別和計數;進而基于表型平臺所獲取的高時序圖像數據,對不同品種、不同密度的玉米植株進行出苗動態連續監測,對各玉米品種的出苗持續時間和出苗整齊度進行評價分析。試驗結果表明,本研究提出的方法應用于田間作物高通量表型平臺出苗檢測時,晴天和陰天的識別精度分別為95.67%和91.36%;應用于無人機平臺出苗檢測時晴天和陰天的識別精度分別91.43%和89.77%,可以滿足實際應用場景下玉米出苗自動檢測的需求。利用表型平臺可獲取時序數據的優勢,進一步進行了玉米動態出苗檢測分析,結果表明利用本模型得到的動態出苗結果與人工實際觀測具有一致性,說明本研究提出的模型的具有魯棒性和泛化性。

關鍵詞: 玉米苗期, Faster R-CNN, 識別, 計數, 出苗動態監測

Abstract:

At present, the dynamic detection and monitoring of maize seedling mainly rely on manual observation, which is time-consuming and laborious, and only small quadrats can be selected to estimate the overall emergence situation. In this research, two kinds of data sources, the high-time-series RGB images obtained by the plant high-throughput phenotypic platform (HTPP) and the RGB images obtained by the unmanned aerial vehicle (UAV) platform, were used to construct the image data set of maize seedling process under different light conditions. Considering the complex background and uneven illumination in the field environment, a residual unit based on the Faster R-CNN was built and ResNet50 was used as a new feature extraction network to optimize Faster R-CNN to realize the detection and counting of maize seedlings in complex field environment. Then, based on the high time series image data obtained by the HTPP, the dynamic continuous monitoring of maize seedlings of different varieties and densities was carried out, and the seedling duration and uniformity of each maize variety were evaluated and analyzed. The experimental results showed that the recognition accuracy of the proposed method was 95.67% in sunny days and 91.36% in cloudy days when it was applied to the phenotypic platform in the field. When applied to the UAV platform to monitor the emergence of maize, the recognition accuracy of sunny and cloudy days was 91.43% and 89.77% respectively. The detection accuracy of the phenotyping platform image was higher, which could meet the needs of automatic detection of maize emergence in actual application scenarios. In order to further verify the robustness and generalization of the model, HTPP was used to obtain time series data, and the dynamic emergence of maize was analyzed. The results showed that the dynamic emergence results obtained by HTPP were consistent with the manual observation results, which shows that the model proposed in this research is robust and generalizable.

Key words: field maize, Faster R-CNN, recognition, counting, dynamic seedling detection

中圖分類號: 

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