Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images

Examination of seed germination rate is of great importance for growers early in the season to determine the necessity for replanting their fields. The objective of this study was to explore the potential of using unmanned aircraft system (UAS)-based visible-band images to monitor and quantify the cotton germination process. A light-weight UAS platform was used, which carried a consumer-grade red, green, and blue camera stabilized by a built-in gimbal system. In order to obtain ultrahigh image resolution during the germination stage, the UAS platform was flown at an altitude of approximately 15-20 m above ground. By applying the structure-from-motion (SfM) algorithm, the images were rectified and orthographically mosaicked with a ground sampling distance of approximately 6-9 mm/pixel. A novel solution was then developed for calculating the average plant size and the number of germinated cotton plants according to the leaf polygons extracted from the orthomosaic images. By using the estimated number of germinated cotton plants, the plant density and the cumulative germination rate can also be estimated in a straightforward manner using field-specific parameters. An assessment of the proposed solution was conducted by comparing the estimated number of the germinated cotton plants against ground observation data collected from six cotton row segments. The results demonstrated that the average estimation accuracy achieved 88.6% in terms of identifying the number of the germinated cotton plants. The accuracy may be further improved if images with near infrared band are employed.