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Green pixel values vs. red pixel values for soil line, 100% ground cover line, and individual plot data. GCF, ground cover fraction.
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Ground cover fraction (GCF) can be used as a proxy for leaf area index, plant radiation capture, and plant canopy characteristics in cotton (Gossypium hirsutum L.). One method of imagery-based GCF estimation is to separate plant pixels from soil pixels based on intensity of reflected green and red radiation. However, this method can be time-consumi...
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... based on green and red pixel values instead of NIR and red pixel values. Each soil line image had 680 by 664 soil pixels, and images were collected on sunny days during local solar noon. The soil images included wet soil with dark pixels as well as dry soil with bright pixels to develop a soil line with a wide range of brightness values (Fig. 2). Similar to the soil line generated by Maas and Rajan (2008), the green-red bare-soil line had a strong linear aspect and very little scatter (Fig. ...
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... on sunny days during local solar noon. The soil images included wet soil with dark pixels as well as dry soil with bright pixels to develop a soil line with a wide range of brightness values (Fig. 2). Similar to the soil line generated by Maas and Rajan (2008), the green-red bare-soil line had a strong linear aspect and very little scatter (Fig. ...
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... 100% GCF line was developed on 900 visible images consisting of soil and cotton plants were manually cropped to select pixel regions that consisted only of plant leaves, and the 100% GCF line was based on the red and green values from these cropped images (Fig. 2) in a similar manner to that of the bare-soil line. Each image used to create 100% GCF had 289 by 730 pixels consisting of pixels of fully illuminated leaves with no soil or ...
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... shown in Fig. 2, there was a strong linear relationship between red and green reflectance of the bare soil (mean green reflectance values range from 52 to 210, mean red reflectance from 70 to 247) similar to the patterns found by Rajan and Maas (2009) on the red-NIR soil line. In addition, image pixels of plant tissue also had a strong linear ...
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... Rajan and Maas (2009) on the red-NIR soil line. In addition, image pixels of plant tissue also had a strong linear relationship between green and red reflectance, albeit with higher green values and a different slope than with the soil line (mean green reflectance values range from 70 to 182, and mean red reflectance value ranges from 40 to 135; Fig. 2). In the GCF PVI-Green analysis, the individual GCF PVI-Green values for incomplete canopies occurred between the soil line and the 100% GCF line (Fig. 2). The brightness of the soil line varies based on surface wetness of the soil, shadows, and camera exposure. In this research, the camera exposure was based on the center of the ...
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... albeit with higher green values and a different slope than with the soil line (mean green reflectance values range from 70 to 182, and mean red reflectance value ranges from 40 to 135; Fig. 2). In the GCF PVI-Green analysis, the individual GCF PVI-Green values for incomplete canopies occurred between the soil line and the 100% GCF line (Fig. 2). The brightness of the soil line varies based on surface wetness of the soil, shadows, and camera exposure. In this research, the camera exposure was based on the center of the camera image, which in almost all cases consisted primarily of the green ...
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Background
Cotton growers in Egypt have experienced severe economic loss from cotton bollworms, especially the pink bollworm, Pectinophora gossypiella (Saund.), and spiny bollworm, Earias insulana (Boisd.), so a field experiment was carried out to determine efficiency of eight insecticides in their seven sequence programs to evaluate the best solut...
Crop segmentation from the images captured in the outdoor field is a complex task in agriculture automation, let alone detecting some specific crops with one method. Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. In this paper, a novel strategy based on the deep learning is util...
Citations
... FVC were calculated effectively from an orthomosaic image with hundreds of three-channel RGB images by separating and classifying vegetation pixels from soil pixels and other non-vegetation pixels (Guo et al. 2013;Sharma et al. 2015) as demonstrated by . FVC is the ratio of the vertical projected area of vegetation to the total ground area (Lin and Qi 2004). ...
Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.
... FVC were calculated effectively from an orthomosaic image with hundreds of three-channel RGB images by separating and classifying vegetation pixels from soil pixels and other non-vegetation pixels (Guo et al. 2013;Sharma et al. 2015) as demonstrated by . FVC is the ratio of the vertical projected area of vegetation to the total ground area (Lin and Qi 2004). ...
Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.
... They stressed that consistent spatial data are preferable over conventional aerial photos as well as field surveys. The inherent forest vulnerability was studied by (Sharma et al., 2015) using a pair-wise comparison matrix in the Western Ghats of India. Different index-based techniques for monitoring forest cover using satellite data exist, though (Bochenek et al., 2018;Cross et al., 1991;Hayes et al., 2008;Joshi et al., 2006;Rikimaru et al., 2002). ...
... Here, they have used the NIR and red bands to measure the perpendicular distance between the patches of vegetation above the soil surface. It is an established fact that if the PVI value is high, it reflects a high soil surface brightness with a very good quality vegetation cover and vice versa (Huete et al., 1997;Malik et al., 2019;Mallick et al., 2012;Sharma et al., 2015). PVI can be expressed as ...
... It was used in determining the ground cover fraction (GCF) which has been proved to be useful for outdoor and soil-based vegetation with low sensitivity for multi-crop cultivation. Brightness coming from wet and dry soils was eliminated using a threshold of 1.1 with pixel-by-pixel count of individual brightness [2]. According to [3], light intensity has direct positive correlation with triangular greenness index (TGI) as it defines color purity. ...
Lightness signal and color reflectance constitute the reflected luminance spectra from camera captured image to camera lenses. The intensity of lightness and visible RGB signals deviates as the camera distance to object varies. The presence of uneven distribution of photosynthetic light causes angular light effect of shadowing on the focal object and light emitting objects placed on the visually noisy background added a challenge in materializing an efficient greenness index for crop phenotyping. The proposed method in this study compensates excessive relative brightness on the image by introducing lightness rectification coefficient and employing genetic algorithm to derive a novel visible tetrahedron greenness index (gvTeGI) based on normalized green waveband. Hybrid neighborhood component analysis and Pearson's correlation coefficient approach for feature selection resulted to retaining photosynthetic canopy area, and correlation and homogeneity texture features as highly important descriptors for biophysical signatures considered in this study which are lettuce fresh weight, height and number of spanning leaves. The selection, crossover and mutation rates used to optimize the genetic algorithm model are 0.2, 0.8 and 0.01 respectively. Indoor and outdoor aquaponic system was deployed for 6-week full crop life cycle cultivation. Regression machine learning models were used to estimate biophysical signatures from extracted gvTeGI channels. Optimized Gaussian processing regression model bested regression support vector machine and regression tree in estimating fresh weight, height and number of spanning leaves with R 2 values of 0.7939, 0.7662 and 0.7446. The proposed gvTeGI proved to be more accurate than previously published greenness index for the estimation of biophysical signatures of lettuce using consumer-grade RGB camera.
... In this study, PVI offers improved crop transpiration fraction (EF c ) estimations because it is highly correlated with variations in vegetation water stress by adjusting vegetation densities according to environmental changes [86], [87]. Relative to NDVI, PVI saturates less for dense canopy cover, whereas NDVI saturates asymptotically [88]. ...
The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)-red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013-2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients (R²) of 0.77-0.84 (p < 0.01) and root-mean-square error (RMSE) values of 17.9-21.5 W/m² among all three sites. The site-calibrated statistics are improved by 0.14-0.25 for R² and decreased by 4.2-8.3 W/m² for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.
... The assessment of growth and development in cotton (Gossypium hirsutum L.) relies on the measurement of phenotypic characteristics such as leaf structure, leaf area index (LAI), leaf and canopy temperature, and canopy height (Sharma, Ritchie, & Rajan, 2015a;Wiegand, Richardson, quickly, reliably, and nondestructively (Araus & Cairns, 2014). Several commercial high-throughput sensors measure reflectance, canopy temperature, and plant height of crops. ...
... In contrast, the 60% ET treatment would have reached canopy closure. Sharma et al. (2015a) observed that ground cover fraction, which is the number of pixels containing vegetation versus scene (vegetation and soil pixels), approached 1.0 as the crop reached complete canopy coverage in the higher irrigation treatments. Ritchie et al. (2010) reported similar results, where NDRE was insensitive to changes in ground cover fraction as it approached 1.0. ...
The use of high‐throughput phenotyping aids breeding programs in making more informed selections and advancements. This study's objectives were to determine which proximal remote sensing parameters (normalized difference red edge [NDRE], normalized difference vegetation index [NDVI], difference between canopy and air temperatures [∆T], and plant height) are robust estimators of cotton lint yield and to use a time‐integrated function of one parameter as a single phenotypic measurement for predicting yield. This study evaluated remote sensing parameters (NDRE, NDVI, ∆T, and plant height) measured weekly from squaring through boll production and development. Of these measurements, NDRE was most consistent in terms of r², slope, and normality in predicting yield. From these findings, a temporal analysis was calculated as NDRE integrated over the season, namely NDRE‐days. Significant r² values were detected for the individual remote sensing measurements, with the largest r² occurring around peak bloom (80 d after planting). An r² of 0.81 was identified between ∆T and lint yield in 2015, whereas in 2017 the largest r² value with lint yield was with NDRE (r2 = .71). The temporal analysis showed a significant relationship between NDRE‐days and lint yield (P < .0001; r2 = .58 in 2015 and r2 = .68 in 2017) that was not cultivar specific. This study presents a suitable method that breeders could use to efficiently evaluate plant growth and estimate yield for variety selections while cutting resource requirements.
... Remote sensing can provide a convenient solution to largely replace ground-based manual scouting for weeds. In the past two decades, there has been significant progress made in utilizing remote sensing technologies in agricultural production systems, including its use in soil mapping and fertility assessments (López-Granados et al., 2005), irrigation scheduling (Meron et al., 2010), assessment of crop growth status (Rajan et al., 2014;Sharma et al., 2015) and monitoring abiotic (Choudhary et al., 2012) as well as biotic (Mirik et al., 2011;Yang et al., 2015) stress factors. Remote sensing provides precise and timely data collection, which helps in implementing short-and long-term strategies for crop management. ...
Modern precision weed management relies on site-specific management tactics to maximize resource use efficiency and yield, while reducing unintended environmental impacts caused by herbicides. Scouting for weeds is an important activity to assist weed management decision making, and has been carried out by trained specialists through extensive and routine visual examination of the fields. Recent advancements in Unmanned Aircraft Systems (UAS)-based tools and geospatial information technology have created enormous applications for efficient and economical assessment of weed infestations as well as site-specific weed management. The utilization of UAS-based technologies for weed management applications is currently in its infancy, but this field has witnessed rapid growth in recent times in terms of aerial data acquisition and analysis. Challenges exist in UAS platform reliability, sensor capability and integration, image pre-processing, quantitative assessment and prediction, final product development, and product delivery. This review summarizes current knowledge on the utility of UAS platforms and remote sensing tools for weed scouting and precision weed management. Further, it critically examines potential opportunities and limitations to current UAS technologies, with particular emphasis on the lessons learned from UAS-based weed management research conducted at Texas A&M University.
... The high irrigation treatments had differences in NAWF among different cultivars, but maximum NDRE values occurred with canopy closure, rather than when the crop had completed growth. Sharma et al. (2015b) observed that ground-cover fraction approached 1.0 as the crop reached complete canopy coverage in the higher irrigation treatments. Ritchie et al. (2010) reported similar results, where NDRE was insensitive to changes in ground cover fraction as it approached 1.0, and Ritchie and Bednarz (2005), where NDRE was insensitive to leaf area index values >2.0. ...
Temporal remote sensing measurements of plant growth may give breeders a better understanding of crop growth habits, yield, fiber quality, and maturity in cotton (Gossypium hirsutum L.) genotypes. The objective of this research was to derive a spectral index maturity scoring method based on the normalized difference red edge (NDRE) index. Seasonal NDRE measurements were collected from 2015 to 2017. A growth inflection point (GIP) was generated based on a quadratic fit of the NDRE growth curves for nine commercial cotton cultivars under three irrigation treatments. This GIP was the number of heat units associated with the inflection point of the NDRE values during the season. It was compared with manual measurements of crop maturity, including nodes above white flower (NAWF), percentage open boll (POB), and end‐of‐season plant mapping indices. Each year had environmental conditions that changed growth habits and maturity of the cultivars. Cultivar and irrigation affected maturity in all 3 yr. The GIP correlated with each maturity assessment, with the highest correlations found with NAWF (r² from 0.38 to 0.88) by irrigation. In most cases, the relationship between NAWF and GIP was not cultivar specific, suggesting that GIP may be used across multiple cotton genotypes within multiple growing environments. The GIP method provides a method to more rapidly and objectively evaluate maturity characteristics of cotton cultivars, as well as the effects of management on these characteristics.
... High correlations of UAV-NDVI with biomass and GY illustrated the efficiency of platform that had made this system useful for practical breeding. Previously, several reports were published on the benefits of hand-held and vehicle-based ground sensors to evaluate canopy traits in wheat, cotton and fruit crops [17,[37][38][39][40]. Auto-vehicle based remote sensing only decreased labour costs, but provided no improvement in time and operational costs. ...
Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.
... Several representative systems have been developed recently [14][15][16][17][18][19][20] . Some of these systems [14][15][16] have demonstrated usefulness in breeding programs and genomics studies [21][22][23] . It is noteworthy that imaging techniques were frequently applied in recent systems due to their ample capacity for extracting complex traits. ...
Imaging sensors can extend phenotyping capability, but they require a system to handle high-volume data. The overall goal of this study was to develop and evaluate a field-based high throughput phenotyping system accommodating high-resolution imagers. The system consisted of a high-clearance tractor and sensing and electrical systems. The sensing system was based on a distributed structure, integrating environmental sensors, real-time kinematic GPS, and multiple imaging sensors including RGB-D, thermal, and hyperspectral cameras. Custom software was developed with a multilayered architecture for system control and data collection. The system was evaluated by scanning a cotton field with 23 genotypes for quantification of canopy growth and development. A data processing pipeline was developed to extract phenotypes at the canopy level, including height, width, projected leaf area, and volume from RGB-D data and temperature from thermal images. Growth rates of morphological traits were accordingly calculated. The traits had strong correlations (r = 0.54-0.74) with fiber yield and good broad sense heritability (H2 = 0.27-0.72), suggesting the potential for conducting quantitative genetic analysis and contributing to yield prediction models. The developed system is a useful tool for a wide range of breeding/genetic, agronomic/physiological, and economic studies.
