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Rapidly count crop seedling emergence based on waveform Method(WM) using drone imagery at the early stage
Computers and Electronics in Agriculture ( IF 8.3 ) Pub Date : 2024-04-05 , DOI: 10.1016/j.compag.2024.108867 Jie Yuan , Xu Li , Meng Zhou , Hengbiao Zheng , Zhitao Liu , Yang Liu , Ming Wen , Tao Cheng , Weixing Cao , Yan Zhu , Xia Yao
Computers and Electronics in Agriculture ( IF 8.3 ) Pub Date : 2024-04-05 , DOI: 10.1016/j.compag.2024.108867 Jie Yuan , Xu Li , Meng Zhou , Hengbiao Zheng , Zhitao Liu , Yang Liu , Ming Wen , Tao Cheng , Weixing Cao , Yan Zhu , Xia Yao
Effectively monitoring seedling emergence is critical to identify missing cotton seedling at early stages, allowing the prompt replenishment of the seedlings to maintain crop yield. However, traditional manual inspections have the limitations with low efficiency, poor timeliness, and large counting errors. Although UAV imagery has been applied in seedling monitoring, there are still chance in cotton seedling monitoring under complex environment conditions. Therefore, there is a urgent need for an efficient, rapid, and low-cost method for monitoring early seedling emergence in cotton crops. This study utilizes RGB images of early seedling emergence captured from an unmanned aerial vehicle (UAV) at a flight height of 10 m and a resolution of 0.33 cm in Shihezi, Xinjiang, on 7 and 12 days after seeding (DAS). The modified Hough transform method is constructed for line detection, and crop rows are subsequently extracted from the lines and masked images. After masking, the waveform is extracted from the excess green index (ExG) greyscale map and smoothed to determine the peaks. Seedlings are counted and located by numbering and positioning the peaks, meanwhile their coordinates and distance determine the number and location of missing seedlings. Images with varying resolutions (ranging from 0.33 to 2.5 cm) and intensity changes (0.8 to 1.2) verify the accuracy and efficiency of the proposed waveform method(WM). The results shown that at 7 DAS, the root mean square error (RMSE) is less than 2.0 plants/row, the relative root mean squared error (RRMSE) is less than 2 %, and the coefficient of determination (R) is stable at 0.95–0.96 when the resolution is below 1.0 cm. The RMSE, RRMSE and R fluctuate more when the resolution is less than 1.0 cm. At 12 DAS, the counting accuracy is stable under different conditions, and the RMSE and RRMSE are distributed smoothly over the range of 1.76–4.39 plants/row and 1.29–5.47 % with different resolutions. The R is in the range of 0.88–0.97, RRMSE is 1.90–3.39 % and the RMSE is 1.65–2.90 plants/row under different intensities. The results of cotton seedling counting using the waveform method are less affected by image resolution and intensity changes. Therefore, the proposed WM provides efficient, accurate, and automatic counting for cotton seedlings in response to complex weather conditions. Furthermore, it would give a novel approach to accurately countequally spaced row-sown crops based on UAV images.
更新日期：2024-04-05
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