Comparative canopy cover estimation using RGB images from UAV and ground

Fernandez-Gallego, José A.; Kefauver, Shawn C.; Kerfal, Samir; Araus, José Luís

doi:10.1117/12.2501531

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…While not thoroughly investigated in this study, the results show that fc is very responsive and recovers considerably to cloud cover, and while fc under cloud cover is not suggested for use in estimating K s with the methods in this study, fc under cloud cover might be another opportunity to represent fc NS under the right conditions. Moreover, fc under cloud cover conditions could be acquired using remote sensing platforms that offer high-resolution imagery (Fernandez-Gallego et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Diurnal Trends of Maize Canopy Cover Under Water Stress

DeJonge,

Zhang,

Cummins

et al. 2024

Journal of Natural Resources and Agricultural Ecosystems

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Highlights RGB trail cams above maize crop collected images every 15 minutes, day and night, to monitor leaf curl. Fractional canopy cover (fc,%) was constant for full irrigation but reduced during the day for limited irrigation. Techniques were developed to estimate reductions in water use (i.e., evapotranspiration) using images of fc. Measurements of fc prior to wilting represent transpiration demand (Kcb ), and reductions of fc represent stress (Ks ). Abstract. By differentiating green and non-green pixels of RGB images of crop canopy, the fractional canopy cover (fc,%) can be estimated and subsequently used to estimate crop transpiration demand (e.g., Kcb, basal crop coefficient, following FAO-56 methodology). While unstressed maize crops maintain consistent fc through the day, leaves of heat or water stressed crops will curl, thereby reducing the instantaneous fc and the associated transpiration. This paper reports the effects of diurnal leaf curl and subsequent fc reduction on full and limited water treatments of irrigated maize through nadir images obtained every 15 minutes. Results suggest that fc images obtained in the early morning (fcNS) are typically representative of a non-stressed condition and can be used to estimate Kcb. Reduction of fc throughout the day (i.e., fc/fcNS) was compared to water stress coefficient Ks obtained from neutron probe and TDR measurement and showed that fc/fcNS from hours 11 a.m. to 2 p.m. had a high correlation (R2 = 0.773), indicating that mid-day fractional canopy cover reduction could be used as a proxy for Ks. This method creates new opportunities for estimating crop water stress during vegetative growth but also highlights potentially unknown challenges regarding lower resolution remote sensing during the same growth stages. Keywords: Basal crop coefficient, Canopy cover, Crop coefficient, Evapotranspiration, Stomatal conductance, Stress crop coefficient, Transpiration, Water stress.

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Section: Discussionmentioning

confidence: 99%

Diurnal Trends of Maize Canopy Cover Under Water Stress

DeJonge,

Zhang,

Cummins

et al. 2024

Journal of Natural Resources and Agricultural Ecosystems

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“…They therefore require suitable light conditions and calm (low wind) weather for successful operation. That said, several studies have reported GC measures from UAV platforms using RGB [ 37 , 40 , 41 ] and multispectral [ 42 , 43 ] cameras. The capacity to phenotype a large area in a short time is a key feature of UAVs; offering considerably greater throughput when compared to the Phenomobile Lite platform used herein.…”

Section: Discussionmentioning

confidence: 99%

Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

et al. 2021

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Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., Green−Red/Green+Red>0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P<0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

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“…Particularly, discrimination of soil and vegetation pixels also helps to estimate canopy cover, which is a significant agronomical component strongly related to crop growth, development, water use, and photosynthesis [64]. Estimation of the canopy cover area of crops may contribute to improving the efficiency of crop management practices and breeding programs [65].…”

Section: Soil Detection and Removalmentioning

confidence: 99%

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Sagan

Maimaitijiang

Sidike

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kerneldriven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level

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Comparative canopy cover estimation using RGB images from UAV and ground

Cited by 8 publications

References 19 publications

Diurnal Trends of Maize Canopy Cover Under Water Stress

Diurnal Trends of Maize Canopy Cover Under Water Stress

Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

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