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Research Project: Aerial Application Technology for Sustainable Crop Production

Location: Aerial Application Technology Research

Title: Cotton growth modeling and assessment using UAS visual-band imagery

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

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/18/2016
Publication Date: 12/30/2016
Citation: Chu, T., Chen, R., Landivar, J., Maeda, M., Yang, C., Starek, M. 2016. Cotton growth modeling and assessment using UAS visual-band imagery. Journal of Applied Remote Sensing (JARS). 10(3):036018.

Interpretive Summary: Unmanned aircraft systems (UAS) offer a low-cost and flexible remote sensing platform to monitor crop growth and development with very high spatial resolution and short revisit time. This study examined the feasibility of UAS-based imagery for monitoring and modeling the life cycle of cotton growth. A multirotor copter equipped with a normal color camera was used to collect images on a cotton field on 16 dates throughout the growing season from early April to late July. Cotton plant height and canopy cover were extracted from the digital surface models and mosaics created from the images. Results showed that plant height and canopy cover could be accurately estimated from the digital surface models and mosaics, but cotton yield was overestimated for small plants and underestimated for large, full plant canopies. This study demonstrated the potential of using UAS-based imagery for monitoring crop growth conditions and estimating growth parameters.

Technical Abstract: This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, cotton plant height (ph) and canopy cover (cc) were retrieved from the point cloud-based digital surface models (DSMs) and orthomosaic images. Both UAS-based ph and cc follow a sigmoid growth pattern as confirmed by ground-based studies. By applying an empirical model that converts the cotton ph to cc, the estimated cc shows strong correlation (R squared=0.99) with the observed cc. An attempt for modeling cotton yield was carried out using the ph and cc information obtained on June 26, 2015, the date when sigmoid growth curves for both ph and cc tended to decline in slope. In a cross-validation test, the correlation between the ground-measured yield and the estimated equivalent derived from the ph and/or cc was compared. Generally, combining ph and cc, the performance of the yield estimation is most comparable against the observed yield. On the other hand, the observed yield and cc-based estimation produce the second strongest correlation, regardless of the complexity of the models.