Traditional methods of powdery mildew (PM) detection involve visual inspection. However, the PM symptoms must already be visible when the damage is already done and disease is already spreading. Laboratory tests are more accurate than visual inspection, but are time consuming and cannot provide information for immediate decision making. Near infrared (NIR) and shortwave infrared (SWIR) sensors can see the reflected light in 700 nm to 2500 nm spectral range, thereby helping early detection of PM and other diseases. When subjected to PM stress, grapes undergo changes in spectral reflectance due to physiological and biochemical alterations in their leaves, such as decreased chlorophyll content, destroyed cell structure, or water stress. This paper presents an investigation on the potential of hyperspectral data acquired from vineyards using unmanned aerial vehicles (UAVs) in detecting powdery mildew in grapes. A UAV equipped with a hyperspectral sensor has been flown over a Cal Poly Pomona vineyard. The hyperspectral data is used to determine various vegetation indices including normalized difference texture index (NDTI), powdery mildew index (PMI), and normalized difference water index (NDWI) that can provide information on the presence of the disease and plant stresses due to the disease. These indices are compared with the ground-truth data that include visual inspection data and proximal sensor data such as chlorophyll meter and NDVI meter.
This paper discusses the improved correlations between unmanned aerial vehicle (UAV)-based remote sensing and proximal sensor data. Better and increased correlation between remote sensing and proximal sensor data is necessary for the remote sensing data to be useful for precision agriculture. Portable ground control points (GCPs) were used for absolute positioning and will serve as an absolute georeference for the UAV data. RTK GNSS receiver and base station were used for centimeter level accuracy of ground truth data. This paper shows the results obtained from hyperspectral sensors that uses a high-performance GNSS/INS and multispectral sensor equipped with an RTK GNSS that is connected to an RTK GNSS mobile station for increased accuracy. The remote sensing data is used to calculate various vegetation indices including normalized difference vegetation index (NDVI), Green NDVI, and modified soil adjusted vegetation indices (SAVI). The indices are compared with the data obtained from proven proximal sensors that include Handheld Spectroradiometer and Chlorophyll Meter.
This paper presents the assessment of lettuce plant health using unmanned aerial vehicle (UAV)-based hyperspectral sensor, proximal sensors, and measurement of agronomic & physiological parameters. Hyperspectral data of lettuce plants at Cal Poly Pomona’s Spadra Farm was collected from a DJI Matric 600 multicopter UAV. An experimental lettuce plot was designed for the study. The plot was divided into several subplots that were subject to different water and nitrogen applications with three replications. Proximal sensors included Handheld spectroradiometer, water potential meter, and chlorophyll meter. The hypespectral data from the UAV and spectroradiometer were used in the determination of several vegetation indices including normalized difference vegetation index (NDVI), water band index (WBI), and modified chlorophyll absorption ratio index (MCARI). These indices were compared with chlorophyll meter data, water potential, plant height, leaf numbers, leaf water content, and leaf nitrogen content. With the hyperspectral data collected so far, MCARI has shown good correlation with chlorophyll meter data and WBI has shown good correlation with leaf water content. The paper will show and discuss all the vegetation indices and their relationship with proximal sensor data, agronomic measurement, and leaf water & nitrogen contents.
This paper shows the comparison between multispectral and hyperspectral data collected from UAVs in detecting citrus nitrogen and water stresses. UAVs equipped with multispectral and hyperspectral sensors were flown over Citrus trees at Cal Poly Pomona’s Spadra Farm. The multispectral and/or hyperspectral data are used in the determination of normalized differential vegetation index (NDVI), water band index (WBI), and other vegetation indices. These indices are compared with the proximal sensor data that include handheld spectroradiometer, water potential meter, and chlorophyll meter. Correlations of multispectral and hyperspectral data with the proximal sensor data are shown.
This paper presents the ground-truthing of remote sensing data of citrus plants collected from unmanned aerial vehicles (UAVs). The main advantage of the UAV-based remote sensing is the reduced cost and immediate availability of high resolution data. This helps detect crop stresses throughout the crop season. Near infrared (NIR) images obtained using remote sensing techniques help determine the crop performances and stresses of a large area in a short amount of time for precision agriculture, which aims to optimize the amount of water, fertilizers, and pesticides using site-specific management of crops. However, to be useful for the real-world applications, the accuracy of remote sensing data must be validated using the proven ground-based methods. UAVs equipped with multispectral sensors were flown over the citrus orchard at Cal Poly Pomona’s Spadra Farm. The multispectral/hyperspectral images are used in the determination of vegetation indices that provide information on the health of the plant. Handheld spectroradiometer, water potential meter, and chlorophyll meter were used to collect ground-truth data. Correlations between the vegetation indices calculated using airborne data and proximal sensor data are shown.
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