Impact of soybean defoliation on canopy recovery, yield, and seed quality

Normalized difference vegetation index (NDVI) and normalized difference red-edge index (NDRE) are vegetation indices commonly used in agriculture to provide information on crop’s growth and health. Here, we investigated the sensitivity of both indices to management practices in lowbush blueberry fields. Images of the experimental plots were collected with a multispectral camera installed on an unmanned aerial vehicle. Both NDVI and NDRE values were significantly higher in fertilized plots than in controls (0.88 ± 0.03 vs. 0.79 ± 0.03 for NDVI, and 0.37 ± 0.01 vs. 0.33 ± 0.01 for NDRE) due to fertilization effect on vegetative growth. The increase was higher under mineral than organic fertilization during the two first phases of the cropping system (by ∼0.3 and ∼0.2 for NDVI and NDRE, respectively). NDRE was not affected by thermal pruning and fungicide application but was negatively correlated with Septoria infection level. NDVI was more strongly correlated with stem length than NDRE, but unlike NDRE, NDVI was not impacted by the development of reproductive shoots during the harvest phases. Overall, the results indicate that although both index values are correlated, their sensitivity to changes in canopy characteristics differs depending on the cropping phase. Further research must be conducted to relate these indices to blueberry’s nutrient status.

Abstract. Sugarcane is a perennial crop that contributes to nearly 80% of the global sugar-based products. Therefore, sugarcane growers and food companies are seeking ways to address the concerns related to sugarcane crop yield and health. In this study, a spatial and spectral analysis on the peak growth stage of the sugarcane fields in Bundaberg, Queensland, Australia is performed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) derived from high-resolution WorldView-2 (WV2) images and multispectral Unmanned Aerial Vehicle (UAV) images. Two topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index, a common crop parameter for the assessment of crop yield and health stages; 2) the impact of spatial resolution on the systematic difference in the abovementioned two Vegetation Indices (VIs). The statistical correlation analysis between the WV2 and UAV images produced correlation coefficients of 0.68 and 0.71 for NDVI and NDRE, respectively. In addition, an overall comparison of the WV2 and UAV-derived VIs indicated that the UAV images produced a better accuracy than the WV2 images because UAV can effectively distinguish various status of vegetation owing to its high spatial resolution. The results illustrated a strong positive correlation between NDVI and NDRE, each derived from the WV2 and UAV images, and the correlation coefficients were 0.81 and 0.90, respectively, i.e. the correlation between NDVI and NDRE is higher in the UAV images than the WV2 images.

This study investigated the statistical relationship between defoliation in pine forests infested by nun moths (Lymantria monacha) and the spectral bands of the RapidEye sensor, including the derived normalized difference vegetation index (NDVI) and the normalized difference red-edge index (NDRE). The strength of the relationship between the spectral variables and the ground reference samples of percent remaining foliage (PRF) was assessed over three test years by the Spearman’s ρ correlation coefficient, revealing the following ranking order (from high to low ρ): NDRE, NDVI, red, NIR, green, blue, and red-edge. A special focus was directed at the vegetation indices. In both discriminant analyses and decision tree classification, the NDRE yielded higher classification accuracy in the defoliation classes containing none to moderate levels of defoliation, whereas the NDVI yielded higher classification accuracy in the defoliation classes representing severe or complete defoliation. We concluded that the NDRE and the NDVI respond very similarly to changes in the amount of foliage, but exhibit particular strengths at different defoliation levels. Combining the NDRE and the NDVI in one discriminant function, the average gain of overall accuracy amounted to 7.8 percentage points compared to the NDRE only, and 7.4 percentage points compared to the NDVI only. Using both vegetation indices in a machine-learning-based decision tree classifier, the overall accuracy further improved and reached 81% for the test year 2012, 71% for 2013, and 79% for the test year 2014.

How similar is the zoning of different vegetation indices: Defining the optimal framework for monitoring grapevines’ growth within vigorous vineyards

One of the indicators of maintaining the quality of rice plants is monitoring and managing nitrogen requirements. Nitrogen (N) absorption in rice plants can be detected by remote sensing technology using Sentinel 2-A satellite imagery data using the NDRE (Normalized Difference Red Edge) method. This research aims to determine a mathematical model to predict nitrogen absorption in rice plants. This study uses the NDRE (Normalized Difference Red Edge) index value. The image used is Sentinel 2 imagery, namely channels 4 and 8 to see the Normalized Difference Vegetation Index (NDVI) value. Besides, the Normalized Difference Red-Edge Index (NDRE) is channels 5 and 8. The results of spatial and tabular data processing are analyzed per pixel and time trend to obtain patterns during one phase of the planting period. Based on the analysis of the NDVI and NDRE values of rice plants in Nagari Singkarak, the NDVI index pattern is in line with the NDRE index. At the beginning of planting (age ± 1 month) and the ripening period (age > ± 90 days) the NRDE value of rice plants is dominated by the low category NDRE value (red color). While when the rice is ± 60 days old, it is dominated by the high category NDRE value (green color). The estimation model for nitrogen uptake of rice plants in Nagari Singkarak based on NDRE data is y = 141.37 X + 0.0412, with a correlation coefficient (r) value of 0.97, which indicates a high correlation between NDRE values and nitrogen uptake.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Cover crops provide valuable roles in sustainable agriculture, provided they produce enough biomass. To accurately measure their services to field management, spatial estimates would be useful to producers. This study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots on sandy, Mid‐Atlantic soils. Cover crops included cereal rye (Secale cereale), mixtures of rye and crimson clover (Trifolium incarnatum), and mixtures of rye and hairy vetch (Vicia villosa). Their biomass was sampled in the spring of 2019, 2020, and 2021, dried, weighed, and analyzed for total nitrogen (N) content. Measurements of NDVI became saturated (i.e., reached a linear plateau) at 3.86 Mg biomass ha−1, NDRE at 5.72 Mg biomass ha−1, and the DSM at 5.11 Mg biomass ha−1. The measured N content became saturated at 80.9, 139.1, and 75 kg N ha−1 for NDVI, NDRE, and the DSM, respectively. Based on log transformations, NDVI was a stronger predictor of biomass and N, but not C:N. The NDRE was important for biomass, N, and C:N, while the DSM interactions with cover crop species helped predict both the N content and C:N of cover crop tissues. Accumulated growing degree days was important as an individual variable for biomass and N and as an interaction with cover crop species.

On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

ABSTRACT: Volunteer corn occurrence with soybean is favored by the glyphosate-resistant (GR) corn cultivation preceding soybean and no-tillage systems. Volunteer corn interference causes significant losses in soybean grain yield. The levels of crop losses change with the corn density, origin, and time of emergence. High levels of weed interference in crops can result in the production of reactive oxygen species and lead to the occurrence of oxidative stress. The objectives of this study were to evaluate the effects of interference of (1) different origins (individual plants and clumps) and times of emergence of volunteer corn on soybean growth, yield components, and grain yield loss; and (2) if the volunteer corn interference causes oxidative stress in soybean. Field experiment and laboratory analyses were performed. The evaluated variables were soybean yield components, grain yield, hydrogen peroxide - H2O2 content, and antioxidant enzyme superoxide dismutase - SOD, catalase - CAT, and ascorbate peroxidase - APX activities. Volunteer corn interference reduced the yield components and soybean yield. The highest yield losses were observed with volunteer corn clumps regarding individual plants. The interference of volunteer corn emerged 10 days before or on the same day as soybean caused the greater yield losses than those emerged 10 days after, independently of its origin. The content of H2O2 and enzyme SOD, CAT and APX activities changed in soybean leaves in response to the interference of volunteer corn plants and clumps. However, the results indicate that the volunteer corn interferences does not cause oxidative stress in soybean.

Zoysia grass (Zoysia spp.) is the most widely used warm-season turf grass in Korea due to its durability and resistance to environmental stresses. To develop new longer-period greenness cultivars, it is essential to screen germplasm which maintains the greenness at a lower temperature. Conventional methods are time-consuming, laborious, and subjective. Therefore, in this study, we demonstrate an objective and efficient method to screen maintaining longer greenness germplasm using RGB and multispectral images. From August to December, time-series data were acquired and we calculated green cover percentage (GCP), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Soil-adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) values of germplasm from RGB and multispectral images by applying vegetation indexs. The result showed significant differences in GCP, NDVI, NDRE, SAVI, and EVI among germplasm (p < 0.05). The GCP, which evaluated the quantity of greenness by counting pixels of the green area from RGB images, exhibited maintenance of greenness over 90% for August and September but, sharply decrease from October. The study found significant differences in GCP and NDVI among germplasm. san208 exhibiting over 90% GCP and high NDVI values during 153 days. In addition, we also conducted assessments using various vegetation indexes, namely NDRE, SAVI, and EVI. san208 exhibited NDRE levels exceeding 3% throughout this period. As for SAVI, it initially started at approximately 38% and gradually decreased to around 4% over the course of these days. Furthermore, for the month of August, it recorded approximately 6%, but experienced a decline from about 9% to 1% between September and October. The complementary use of both indicators could be an efficient method for objectively assessing the greenness of turf both quantitatively and qualitatively.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush) at the Blue Oak Ranch Reserve (BORR) ~20 km east of San Jose, California. We recorded multispectral imagery at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index), leaf water content and water potential as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit across treatments; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of leaf water content. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope).We found that PlanetScope data was able to capture the overall trend in treatments but unable to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) ofPac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.

Double cropping soybean [Glycine max (L.) Merr.] after winter wheat (Triticum aestivum L.) increases total food production without additional land. However, double‐crop soybean usually yields less than full‐season soybean, mainly due to late planting. We evaluated double‐crop soybean growth and yield as affected by early planting immediately after high‐moisture wheat harvest across 20 site‐years in five Mid‐Atlantic states during 2015–2017. At each site, six soybean cultivars from relative maturity group (rMG) 3.1–5.9 were planted at three to five dates in a 4‐ to 14‐d interval. Soybean growth, measured by normalized difference vegetation index (NDVI) across the growing season, was affected only by planting date. Although NDVI peaked near the R5 stage, it took 9–27 more days to reach the peak NDVI (0.84–0.98) for early‐planted soybean than for late‐planted soybean. Relative yield declined with planting dates, which explained 41–81% of the relative yield variability. The yield loss from delayed planting was greater in the north (33–80%; Pennsylvania, Maryland, and Delaware) than in the south (20–27%; Virginia, North Carolina) due to longer delay in planting and shorter growing season in the north. Soybean NDVI from the R1–R6 stages was associated with yield, with the strongest association (R2 = .55–.57) at the R2 and R3 stages. The area under the NDVI curve (AUNDVIC) was also strongly associated (R2 = .77) with relative yield, indicating an excellent tool for explaining double‐crop soybean yield loss due to poor growth. High‐moisture wheat harvest facilitated soybean planting 4–21 d earlier, which increased growth and yield.

The objective of this study was to evaluate the ability of an unmanned aerial system (UAS) equipped with a multispectral sensor to track plant health in the presence of plant-parasitic nematodes in conjunction with nematicide applications. Four nematicides were evaluated for their ability to suppress Belonolaimus longicaudatus and Meloidogyne incognita in microplots, and three nematicides were evaluated on a golf course for their ability to suppress multiple plant-parasitic nematode genera. Visual ratings, Normalized Difference Vegetation Index (NDVI), and Normalized Difference RedEdge Index (NDRE) were reported throughout the trial to assess plant health. B. longicaudatus and M. incognita population density was significantly lowered by nematicide treatments in microplots and correlated with visual ratings, NDVI, and NDRE plant health ratings. On the golf course, all nematicides reduced total plant-parasitic nematode population density at 28, 56, and 84 days after treatment (DAT). Visual turf quality ratings, NDVI, and NDRE were positively correlated with lower nematode population density in the majority of evaluation dates. In the microplot and golf course settings, the parameters evaluated for plant health were correlated with plant-parasitic nematode population density: visual ratings, NDVI, and NDRE improved as nematode population density declined. These results show that remote sensing has the potential to be a beneficial tool for assessing plant-parasitic nematode infected bermudagrass.