Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Authors

Sanaz Shafian, University of Idaho
Nithya Rajan, Texas A&M UniversityFollow
Ronnie Schnell, Texas A&M University
Muthukumar Bagavathiannan, Texas A&M University
John Valasek, Texas A&M University
Yeyin Shi, University of Nebraska-LincolnFollow
Jeff Olsenholler, Texas A&M University

Document Type

Article

Date of this Version

2018

Citation

PLoS ONE 13(5): e0196605

Comments

© 2018 Shafian et al.

Open access

https://doi.org/10.1371/journal.pone.0196605

Abstract

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (f_c) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and f_c were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, f_c and yield with R² values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and f_c from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (f_c, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum.