Abstract
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.
Highlights
One aspiration of precision agriculture is to minimize costs while maximizing crop yield by allowing farmers to identify problem areas in the field and deploy mitigation tactics.[1]
varianceto-mean ratio (VMR) in normalized difference vegetation index (NDVI) were compared among the three flights for the late planting [Fig. 4(a)]
The late planting had the most visible ground between the plants and filtering for low values had the most impact on the mean NDVI for this planting
Summary
One aspiration of precision agriculture is to minimize costs while maximizing crop yield by allowing farmers to identify problem areas in the field and deploy mitigation tactics.[1] Remote sensing increases the efficiency of this process by gathering data associated with crop health quickly and automating processes for crop health visual display and evaluation.[2] Satellite imagery is not collected with the frequency required for precision agriculture, and atmospheric effects or cloud cover can have dramatic effects on the quality of data produced. Ground-based (tractor-mounted) sensors achieve high resolution, but operational and data efficiency measures may be much reduced compared with other approaches. Over the past decade and into the present, an increasing amount of research has been done on the use of remote sensing with UAS and other platforms for detecting measures of crop health High-resolution imagery is necessary for more precise data, and unmanned aircraft systems (UAS) are able to fly much lower and can achieve higher temporal and spatial resolutions than satellite imagery or imagery captured using manned aircraft.[1,3]
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