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This study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants' growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs. Core Ideas: A small unmanned aircraft system was utilized to collect multispectral imagery.Soil background noise was assessed and removed via image thresholding and maximum likelihood classification.A total of 25 vegetation indices were evaluated with and without soil.A two‐class distance measure assessed which vegetation index best detected iron deficiency chlorosis.Normalized green, normalized difference red‐edge, and MERIS terrestrial chlorophyll indices performed best. [ABSTRACT FROM AUTHOR]

Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment. [ABSTRACT FROM AUTHOR]

The use of unmanned aircraft systems (UAS) delivering imaging technologies in agricultural settings has become more prevalent over the past five years and is growing in pest management programs. Here, spectral data from a three-band consumer-grade camera with a filter to obtain Near Infrared (NIR) data, mounted on a fixed-winged UAS, was used to assess the ability to detect cotton fleahopper, Pseudatomoscelis seriatus (Reuter) (Hemiptera: Miridae), injury to immature fruiting bodies on cotton. In a small plot experiment conducted two years and two planting periods each year, cotton fleahopper densities were manipulated with insecticide. Variable populations of cotton fleahopper across the plots were achieved in 2015, ranging between 0 and 3.5 cotton fleahopper-days over a five-week period when squares were forming. Derived from spectral data of multiple UAS flights, unexpected but inconsistent trends (by regression analysis) of increasing Normalized Difference Vegetation Index (NDVI), values with increasing cotton fleahopper days were detected in both plantings and years (five of 12 regressions were significant). Our preliminary data suggest that differences in cotton fleahopper activity on cotton may be reflected in NDVI values using a modified consumer-grade camera in-season. But the interpretation of NDVI may be complicated by the feeding site of cotton fleahopper, leading to unexpected and inconsistent regressions. Exploration of image resolution and bandwidth to define optical sensor needs appears important for cotton fleahopper, given its feeding habitat and injury to cotton. The application of UAS-derived remotely sensed data to detect insect-induced plant stress continues to have merit, but a merging of best suited UAS technology to the needs of detecting insect-induced cotton stress will be a researchintensive endeavor. [ABSTRACT FROM AUTHOR]