Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery
AbstractThe uniformity of wheat seed emergence is an important characteristic used to evaluate cultivars, cultivation mode and field management. Currently, researchers typically investigated the uniformity of seed emergence by manual measurement, a time-consuming and laborious process. This study employed field RGB images from unmanned aerial vehicles (UAVs) to obtain information related to the uniformity of wheat seed emergence and missing seedlings. The calculation of the length of areas with missing seedlings in both drill and broadcast sowing can be achieved by using an area localization algorithm, which facilitated the comprehensive evaluation of uniformity of seed emergence. Through a comparison between UAV images and the results of manual surveys used to gather data on the uniformity of seed emergence, the root-mean-square error (RMSE) was 0.44 for broadcast sowing and 0.64 for drill sowing. The RMSEs of the numbers of missing seedling regions for broadcast and drill sowing were 1.39 and 3.99, respectively. The RMSEs of the lengths of the missing seedling regions were 12.39 cm for drill sowing and 0.20 cm2 for broadcast sowing. The UAV image-based method provided a new and greatly improved method for efficiently measuring the uniformity of wheat seed emergence. The proposed method could provide a guideline for the intelligent evaluation of the uniformity of wheat seed emergence. View Full-Text
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Liu, T.; Li, R.; Jin, X.; Ding, J.; Zhu, X.; Sun, C.; Guo, W. Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery. Remote Sens. 2017, 9, 1241.
Liu T, Li R, Jin X, Ding J, Zhu X, Sun C, Guo W. Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery. Remote Sensing. 2017; 9(12):1241.Chicago/Turabian Style
Liu, Tao; Li, Rui; Jin, Xiuliang; Ding, Jinfeng; Zhu, Xinkai; Sun, Chengming; Guo, Wenshan. 2017. "Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery." Remote Sens. 9, no. 12: 1241.
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