A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.
Proc. SPIE. 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
KEYWORDS: Near infrared, Agriculture, Detection and tracking algorithms, Cameras, Clouds, Atomic force microscopy, Photogrammetry, Genetics, Algorithm development, Systems modeling, RGB color model, Accuracy assessment
Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in corn. A variety of contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may lead to lodging before harvesting season. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address this problem, this research focuses on the use of unmanned aircraft systems (UAS) for automated detection of crop lodging. The study was conducted over an experimental corn field at the Texas A and M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the corn field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height, and rates of growth. A lodging index for detecting corn lodging was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates well with the ground-measured height. More importantly, the lodging index can effectively reflect severity of corn lodging and yield after harvesting.
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, the cotton plant height (ph) and canopy cover (cc) information 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 (R2=0.990) 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.
Point cloud data collected by small-footprint lidar scanning systems have proven effective in modeling the forest canopy for extraction of tree parameters. Although line-of-sight visibility (LOSV) in complex forests may be important for military planning and search-and-rescue operations, the ability to estimate LOSV from lidar scanners is not well developed. A new estimator of below-canopy LOSV (BC-LOSV) by addressing the problem of estimation of lidar under-sampling of the forest understory is created. Airborne and terrestrial lidar scanning data were acquired for two forested sites in order to test a probabilistic model for BC-LOSV estimation solely from airborne lidar data. Individual crowns were segmented, and allometric projections of the probability model into the lower canopy and stem regions allowed the estimation of the likelihood of the presence of vision-blocking elements for any given LOSV vector. Using terrestrial lidar scans as ground truth, we found an approximate average absolute difference of 20% between BC-LOSV estimates from the airborne and terrestrial point clouds, with minimal bias for either over- or underestimates. The model shows the usefulness of a data-driven approach to BC-LOSV estimation that depends only on small-footprint airborne lidar point cloud and physical knowledge of tree phenology.