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

item CHEN, RUIZHI - Texas A&M University
item CHU, TIANXING - Texas A&M University
item LANDIVAR, JUAN - Texas A&M Agrilife
item Yang, Chenghai
item MAEDA, MURILO - Texas A&M Agrilife

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/20/2018
Publication Date: 3/28/2018
Citation: Chen, R., Chu, T., Landivar, J., Yang, C., Maeda, M. 2018. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images. Precision Agriculture. 19(1):161-177.

Interpretive Summary: Unmanned aircraft systems (UAS) are becoming an appealing remote sensing platform for assessing crop growth conditions. The objective of this study was to investigate the feasibility of using UAS technology to monitor the cotton germination process in a timely manner, which is important for cotton growers. Ultrahigh-resolution aerial images were taken with a UAS platform and plant leaves were extracted from mosaicked images to estimate the average plant size and to count the number of germinated cotton plants. Accuracy assessment showed that 89% of the germinated cotton plants were correctly identified. This result indicates that UAS can help growers objectively assess crop germination status so that timely decisions can be made on replanting and other management plans.

Technical Abstract: 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.