Modeling corn (Zea mays L.) productivity under variable irrigation and nitrogen regimes using NDVI

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

The corn cultivation area in South Korea has been steadily decreasing every year since 1990. Although field cultivation of corn is continuously decreasing, cultivation in the paddy field is slightly increasing every year. In particular, field cultivation has decreased rapidly, from 25,207 ha in 1990 to 17,131 ha in 2004 and 14,183 ha in 2018. On the other hand, the cultivation of paddy field grew rapidly, from 780 ha in 1990 to 1087 ha in 2004 and 1289 ha in 2018. This phenomenon is interpreted as a result of the rapid development of alternative crops in response to the FTA (Free Trade Agreement), the opening of the agricultural products market, as well as the increase of rice production and the decrease of domestic rice consumption due to the decrease of rice farm income. The consumption of waxy corn has increased rapidly, but the cultivation area decreased rapidly from 1990 to 2008. In addition, as the importance of waxy corn as a health food and high-income crop has become emphasized, the area of waxy corn cultivation has increased, and research on the advancement of related cultivation technology has been actively carried out. Unlike other corn exporting countries, South Korea cultivates a variety of crops in a narrow cultivation area. Most of the corn cultivated has been grown mainly in field soils, but some farmers prefer to cultivate high yield crops in field soils. However, in order to increase the area of corn cultivation, these efforts are somewhat limited. Therefore, in this study, we try to understand growth information by combining unmanned aerial vehicle (UAV) and sensor technology as an experimental study to expand the cultivation area of corn and paddy soils. In this study, periodic monitoring of the corn cultivation area was carried out using a UAV and a multi spectral and thermal sensor. Corn growth was closely related to the NDVI (normalized difference vegetation index) and CWSI (crop water stress index). According to the growth of corn, high NDVI points were determined to have a low CWSI, indicating an inverse correlation. In particular, crops that grow rapidly and require moisture and nutrient absorption, such as corn, are sensitive to the vegetation index as well as CWSI, which is sensitive to water shortage. Therefore, in order to increase the yield of corn, it is important to properly supply nutrients and water at the optimum time. In the future, there will be a need for ways to manage crop growth and pests through continuous monitoring of growth conditions, as well as the NDVI and CWSI. Through this study, it was found that the use of UAVs and sensors can be very useful to determine size and growth status according to the growth of corn. In recent years, the growing cultivation of corn in paddy fields has become very important to establish the irrigation conditions and the drainage of water, so the utilization of NDVI and CWSI will be very useful.

Crop coefficient (K c ) methods have been commonly used for evapotranspiration estimation. Researchers estimate K c as a function of the vegetation index because of similarities between the K c curve and the vegetation index curve. A linear regression model is usually developed between the K c and the normalized difference vegetation index (NDVI) derived from satellite imagery. However, the spatial resolution of satellite imagery is in the range of meters or greater, which is often not enough for crops with clumped canopy structures, such as trees, and vines. In this study, the Unmanned Aerial Vehicles (UAVs) were used to collect high-resolution images in an experimental pomegranate orchard located at the USDA-ARS, San Joaquin Valley Agricultural Sciences Center, Parlier, CA. The NDVI values were derived from UAV images. The K c values were measured from a weighing lysimeter in the pomegranate field. The relationship between the NDVI and K c was established by using both a linear regression model and a deep stochastic configuration networks (DeepSCNs) model. Results show that the linear regression model has an R2 and RMSE value of 0.975 and 0.05, respectively. The DeepSCNs regression model has an R2 and RMSE value of 0.995 and 0.046, respectively. The DeepSCNs model showed improved performance than the linear regression model in predicting K c from NDVI.

<p>In the Pyrenees, as in other mid latitude mountain ranges, sub alpine areas have a long lasting snow cover that affect different mountain processes, including river discharge timing, soil erosion, primary production or animal and plant phenology. This work presents and analyzes a complete snow depth and Normalized Difference Vegetation Index (NDVI) spatial distribution dataset, generated by Unmanned Aerial Vehicles (UAV) over two years. This study aims to increase the knowledge and understanding of the relationship of the duration and timing of snowmelt and vegetation cover and its annual cycle.</p><p>The dataset was obtained in Izas Experimental Catchment, a 55 ha study area located in Central Spanish Pyrenees ranging between 2000 to 2300 m a.s.l., which is mostly covered by grasslands. A total of 18 UAV snow depth and 14 NDVI observations were obtained by a fixed wing UAV equipped with RGB and multispectral cameras during 2020 and 2021. The melt out date for the different areas of the catchment has been obtained from the snow depth distribution dataset, which in turn has been used to analyze the NDVI evolution. The NDVI values for each UAV flight have been correlated with the snow depth distribution observed in previous dates and with different topographic variables as elevation, solar radiation, curvature (through the Topographic Position Index) or slope.</p><p>The maximum seasonal NDVI happens throughout the study area simultaneously in the entire study area; however those zones with the latest snow disappearance do not reach NDVI values as high as those observed in areas with earlier snow disappearance. Oppositely areas with the soonest snow melting (in late February) have lower maximum NDVI values that those observed in areas with snow melting occurring later (around May).  NDVI correlations have shown that the snow depth distribution observed about one month prior to each NDVI acquisition has a very important control on pasture phenology. This correlation is particularly evident on the free-snow areas during first melting weeks, with a lower influence in those areas where snow melts at the end of the snow season. This field study exemplifies how intensive UAV acquisitions allow understanding snow processes over extended areas with an unprecedented spatial resolution.</p>

Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle

Flax (Linum usitatissimum L.), is a multipurpose crop, grown for fiber, seeds, and oil. This study aims to assess flax growth dynamics and agronomic traits using Normalized Difference Vegetation Index (NDVI) maps generated from multispectral imagery captured by an Unmanned Aerial Vehicle (UAV). Conducted at the Agricultural University of Athens, the field experiment followed a split-plot design with four replicates, encompassing various sowing distances (30 and 40 cm, between rows) and fertilization treatments (urea fertilization, urea with inhibitors, organic fertilizer). NDVI index maps were produced at key growth stages (60, 90, 120, 150, and 179 Days After Sowing) to facilitate comprehensive growth analysis. The findings indicate that while fertilization treatments and sowing densities influenced early growth stages, no significant differences were observed post 90 DAS. NDVI values correlated with agronomic traits, particularly during the early vegetative phase, highlighting the potential of UAV-based NDVI mapping for precise flax cultivation monitoring.

Evapotranspiration (ET) estimation is important in precision agriculture water management, such as evaluating soil moisture, drought monitoring, and assessing crop water stress. As a traditional method, evapotranspiration estimation using crop coefficient (Kc) has been commonly used. Since there are strong similarities between the Kc curve and the vegetation index curve, the crop coefficient Kc is usually estimated as a function of the vegetation index. Researchers have developed linear regression models for the Kc and the normalized difference vegetation index (NDVI), usually derived from satellite imagery. However, the spatial resolution of the satellite image is often insufficient for crops with clumped canopy structures, such as vines and trees. Therefore, in this article, the authors used Unmanned Aerial Vehicles (UAVs) to collect high-resolution multispectral imagery in a pomegranate orchard located at the USDA-ARS, San Joaquin Valley Agricultural Sciences Center, Parlier, CA. The Kc values were measured from a weighing lysimeter and the NDVI values were derived from UAV imagery. Then, the authors established a relationship between the NDVI and Kc by using a linear regression model and a stochastic configuration networks (SCN) model, respectively. Based on the research results, the linear regression model has an R2 of 0.975 and RMSE of 0.05. The SCN regression model has an R2 and RMSE value of 0.995 and 0.046, respectively. Compared with the linear regression model, the SCN model improved performance in predicting Kc from NDVI. Then, actual evapotranspiration was estimated and compared with lysimeter data in an experimental pomegranate orchard. The UAV imagery provided a spatial and tree-by-tree view of ET distribution.

Abstract: The use of Unmanned Aerial Vehicles (UAVs) is becoming very common for last few years for monitoring agricultural crops efficiently. Low altitude remote sensing (UAV) provides people with high temporal and spatial resolution for non-destructive, accurate and timely estimation of biophysical parameters like Leaf Area Index (LAI), crop growth, plant biomass and final crop yield. Collection of the data by UAV helps to reduce errors and it fills the biasness on an observational scale in precision agriculture. The main objective of this study was to estimate real time LAI and yield of groundnut crop based on Normalized Difference Vegetation Index (NDVI) using low cost multispectral UAV. A field experiment was set up  with three different groundnut cultivars (V1= BARD-479, V2 = Chakwal-2011 and V3 = Chakwal-2016) with three replications. Field data collection regarding LAI was performed in 2019 at two different growth stages (2-3 leaf stage and Peg formation stage) of groundnut on PMAS-Arid Agriculture Research Farm (Knoot), Pakistan. Final yield was calculated at the time of crop maturity. In this study, low cost UAV platform was established with DJI Phantom 4 pro and Parrot Sequoia Sensor to develop a multispectral UAV system used as the survey platform. A Parrot Sequoia camera was mounted on the UAV used as the remote sensor. The sensor provided the information in five narrow bands including Red, Blue, Green, Near infrared (NIR) and Red Edge. The processing of UAV images was performed in the Python environment and NDVI images were created. Then regression model was performed to compare the NDVI data with the LAI and final yield of groundnut crop. The results indicated that the highest value of R 2 = 0.93 was found with NDVI and LAI at peg formation stage while value of R 2 = 0.59 was at 2-3 leaf stage. The strong and positive relationship was found between LAI and yield ( R 2 = 0.97). There was also a strong and positive relationship between NDVI and yield of groundnut with value of R 2 = 0.92. The study showed that low cost multispectral UAV can be effectively used for real time estimation of LAI and groundnut yield nondestructively and accurately. The study results show that this low cost multispectral UAV platform (DJI Phantom 4 Pro with Parrot Squoia) is robust in management decisions of agriculture such as effective fertilizer application, growth monitoring, and yield estimation accurately and timely based on the vegetation indices. This study also proved the low cost multispectral UAV practicability in estimating plant biophysical parameters at a small field experiment scale reliably. Keywords: vegetation indices, UAV, LAI, yield, multispectral camera, NDVI, groundnut, growth stages DOI:  10.33440/j.ijpaa.20200301.70  Citation: Tahir M N, Lan Y B, Zhang Y L, Wang Y K, Faisal N, Shah M A A, et al.  Real time estimation of leaf area index and groundnut yield using multispectral UAV.  Int J Precis Agric Aviat, 2020; 3(1): 1–6.

Retrieving wheat Biomass by using a hyper-spectral device on UAV

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

Habitat condition is a vital ecological attribute in wildlife conservation and management in protected areas, including the Burunge wildlife management areas in Tanzania. Traditional techniques, including satellite remote sensing and ground-based techniques used to assess habitat condition, have limitations in terms of costs and low resolution of satellite platforms. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) have potential for assessing habitat condition, e.g., forage quantity and quality, vegetation cover and degradation, soil erosion and salinization, fire, and pollution of vegetation cover. We, therefore, examined how the recently emerged Unmanned Aerial Vehicle (UAV) platform and the traditional Sentinel-2 differs in indications of habitat condition using NDVI and GNDVI. We assigned 13 survey plots to random locations in the major land cover types: three survey plots in grasslands, shrublands, and woodlands, and two in riverine and mosaics cover types. We used a UAV-mounted, multi-spectral sensor and obtained Sentinel-2 imagery between February and March 2020. We categorized NDVI and GNDVI values into habitat condition classes (very good, good, poor, and very poor). We analyzed data using descriptive statistics and linear regression model in R-software. The results revealed higher sensitivity and ability of UAV to provide the necessary preliminary diagnostic indications of habitat condition. The UAV-based NDVI and GNDVI maps showed more details of all classes of habitat conditions than the Sentinel-2 maps. The linear regressions results showed strong positive correlations between the two platforms (p < 0.001). The differences were attributed primarily to spatial resolution and minor atmospheric effects. We recommend further studies to test other vegetation indices.

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, <TEX>$NDVI_{UAV}$</TEX> and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And <TEX>$NDVI_{UAV}$</TEX> and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to <TEX>$NDVI_{UAV}$</TEX> and other agro-meteorological factors were well reflected in the model.

Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series

Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m2 at GS 25, then an N application at that stage is not needed, only at GS 30. However, it is often difficult to obtain an accurate representation of tiller density across an entire field. Normalized difference vegetative index (NDVI) and normalized difference red edge (NDRE) have been significantly correlated with tiller density when collected from the ground. With the advent of unmanned aerial vehicles (UAVs) and their ability to collect NDVI and NDRE from the air, this study was established to examine the relationship between NDVI, NDRE, and tiller density, and to verify whether N could be applied based on these two indices. From 2018 to 2020, research trials were established across Virginia to develop a model describing the relationship between aerial NDVI, aerial NDRE, and tiller density counted on the ground, in small plots. In 2021, the model was used to apply N in small plots at two locations, where the obtained grain yield was the same whether N was applied based on tiller density, NDVI, or NDRE. From 2022 to 2023, the model was applied at six locations across the state on large scale growers’ fields to compare the amount of GS 25 N recommended by tiller density, NDVI, and NDRE. At three locations, NDVI and NDRE recommended the same N rates as the tiller density method, while at two locations, NDVI and NDRE recommended less N than tiller density. At one location, NDVI and NDRE recommended more N than tiller density. However, across all six locations, there was no difference in grain yield whether N was applied based on tiller density, NDVI, or NDRE. This study indicated that UAV-based NDVI and NDRE are excellent proxies for tiller density determination, and can be used to accurately and economically apply N at GS 25 in winter wheat production.

Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical, physiological and structural quantities across spatial scales. The rice leaf NDVI (normalized difference vegetation index) has been developed and applied in monitoring rice growth, yield prediction and disease status to guide agricultural management practices. This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI. The test site was a japonica rice experiment located in the eastern city of Shenyang, Liaoning Province, China. This paper describes (1) the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy (R2=0.782–0.903), and (2) how the key point of the rice growth period based on these models was determined. The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented. The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data. This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future. Keywords: japonica rice, NDVI, leaf models, canopy scale, environmental data DOI: 10.3965/j.ijabe.20160905.2266 Citation: Yu F H, Xu T Y, Cao Y L, Yang G J, Du W, Wang S. Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. Int J Agric & Biol Eng, 2016; 9(5): 132－142.