Use of unmanned aerial vehicle imagery and a hybrid algorithm combining a watershed algorithm and adaptive threshold segmentation to extract wheat lodging

• DOI: 10.1016/j.pce.2021.103016
• Publication Date: 2021-04-02T06:33:27Z
• Authors: Wenxuan Cao; Zeyu Qiao; Zeyu Gao; Saihong Lu; Fei Tian
• Keywords: 2. Zero hunger; 0401 agriculture, forestry, and fisheries; 04 agricultural and veterinary sciences; 15. Life on land

Abstract Lodging is a major yield-limiting factor in wheat, so we need a method to collect wheat lodging information efficiently. Traditional extraction of crop lodging is cost intensive and time consuming; satellite remote sensing technology is vulnerable to the cloud and is limited by its temporal and spatial resolution. Therefore, a new method for wheat lodging extraction based on a watershed algorithm combined with an adaptive threshold segmentation hybrid algorithm is proposed. An unmanned aerial vehicle equipped with a high-resolution thermal infrared and visible sensor was used to acquire images of lodged and nonlodged wheat. Results indicate that there are morphological differences between lodged and nonlodged wheat and that the spectral reflectance and temperature of lodged wheat is higher than that of nonlodged wheat. The watershed algorithm is sensitive to the edge response and can reflect the contour characteristics of the lodging area. However, the impact of noise is obvious; there are salt-and-pepper patches. The wheat lodging ratios were calculated as 17.81%, with an accuracy of 80.26%. With adaptive threshold segmentation, the wheat lodging ratios were calculated as 18.78%, with an accuracy of 84.65%. If these two methods are fused, the influence of noise can be reduced, and an extraction accuracy of 93.58% can be achieved. This research provides a new idea for highly efficient crop lodging data collection, which can be used for precision farming and the selection of crop genotypes with high lodging resistance.