scholarly journals MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND

2016 ◽  
Vol XLI-B1 ◽  
pp. 991-998 ◽  
Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Dry Matter ◽  
Vegetation Index ◽  
Low Cost ◽  
Nutrient Use Efficiency ◽  
Height Distribution ◽  
Corrected Image ◽  
Surface Models ◽  
Multi Temporal

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).

scholarly journals MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND

2016 ◽  
Vol XLI-B1 ◽  
pp. 991-998 ◽  
Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Dry Matter ◽  
Vegetation Index ◽  
Low Cost ◽  
Nutrient Use Efficiency ◽  
Height Distribution ◽  
Corrected Image ◽  
Surface Models ◽  
Multi Temporal

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).

scholarly journals Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring

2014 ◽  
Vol XL-7 ◽  
pp. 43-49 ◽  
Author(s):  
S. Brocks ◽  
G. Bareth
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Low Cost ◽  
Image Acquisition ◽  
Coefficient Of Determination ◽  
Average Height ◽  
Smart Cameras ◽  
Growth Variability ◽  
Growing Period ◽  
Surface Models

Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination <i>R</i><sup>2</sup> between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (<i>n</i> = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible.

scholarly journals IDENTIFICATION OF SUITABLE AREAS FOR IRRIGATED RICE CROPPING USING MODIS IMAGES AND HAND MODEL

2016 ◽  
Vol 36 (2) ◽  
pp. 329-341 ◽  
Author(s):  
VAGNER P. MENGUE ◽  
DENISE C. FONTANA
Keyword(s):  
Vegetation Index ◽  
Low Cost ◽  
The State ◽  
Sensor Data ◽  
Irrigated Rice ◽  
Hand Model ◽  
Rice Area ◽  
Multi Temporal ◽  
Southern Half ◽  
Global Accuracy

ABSTRACT One of the most relevant activities of Brazilian economy is agriculture. Among the main crops in Brazil, rice is one of high relevance. The state of Rio Grande do Sul, in Southern Brazil, is responsible for 68.7% of domestic production (IBGE, 2013). The goal of this study was to develop a low-cost methodology with a regional scope to identify suitable areas for irrigated rice cropping in this state, using spectro-temporal behavior of vegetation index by means of MODIS images and HAND model. The rice-cropped area of this study was the southern half of the State. Using the HAND model, flood areas were mapped to identify irrigated rice cultivation. We used multi-temporal images of vegetation index from MODIS sensor, covering the period from August 2001 to May 2012. To assess the results, we used data collected in the fields and cropped area information from IBGE. The results showed that the proposed methodology was satisfactory, with Kappa 0.92 and global accuracy of 98.18%. As result, MODIS sensor data and flood areas delineation by means of HAND model generated the estimate irrigated rice area for the area of study.

scholarly journals Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

2012 ◽  
Vol 30 (2) ◽  
pp. 437-447 ◽  
Author(s):  
A. Merotto JR. ◽  
C. Bredemeier ◽  
R.A. Vidal ◽  
I.C.G.R. Goulart ◽  
E.D. Bortoli ◽  
...  
Keyword(s):  
Weed Control ◽  
Precision Agriculture ◽  
Weed Management ◽  
Dry Matter ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Weed Interference ◽  
Reflectance Indices ◽  
The Relationship

Several tools of precision agriculture have been developed for specific uses. However, this specificity may hinder the implementation of precision agriculture due to an increasing in costs and operational complexity. The use of vegetation index sensors which are traditionally developed for crop fertilization, for site-specific weed management can provide multiple utilizations of these sensors and result in the optimization of precision agriculture. The aim of this study was to evaluate the relationship between reflectance indices of weeds obtained by the GreenSeekerTM sensor and conventional parameters used for weed interference quantification. Two experiments were conducted with soybean and corn by establishing a gradient of weed interference through the use of pre- and post-emergence herbicides. The weed quantification was evaluated by the normalized difference vegetation index (NDVI) and the ratio of red to near infrared (Red/NIR) obtained using the GreenSeekerTM sensor, the visual weed control, the weed dry matter, and digital photographs, which supplied information about the leaf area coverage proportions of weed and straw. The weed leaf coverage obtained using digital photography was highly associated with the NDVI (r = 0.78) and the Red/NIR (r = -0.74). The weed dry matter also positively correlated with the NDVI obtained in 1 m linear (r = 0.66). The results indicated that the GreenSeekerTM sensor originally used for crop fertilization could also be used to obtain reflectance indices in the area between rows of crops to support decision-making programs for weed control.

scholarly journals Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

2020 ◽  
Vol 36 (5) ◽  
Author(s):  
Marcela da Silva Flores ◽  
Willian Meniti Paschoalete ◽  
Fabio Henrique Rojo Baio ◽  
Cid Naudi Silva Campos ◽  
Ariane de Andréa Pantaleão ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Block Design ◽  
Vegetation Indices ◽  
Agronomic Performance ◽  
N Content ◽  
Maize Varieties ◽  
Leaf N Content ◽  
Leaf N

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

scholarly journals USE OF RHIZOBIUMBACTERIA TO INCREASE THE PRODUCTION OF FIVE TROPICAL LEGUMES WITH FORAGE POTENTIAL

2021 ◽  
Vol 9 (11) ◽  
pp. 617-624
Author(s):  
Sosa-Rubio Edgar Enrique ◽  
◽  
Herrera-Cool Gilbert Jose ◽  
Zavaleta-Cordova Maria Del Carmen ◽  
Montoya-Reyes Francisco ◽  
...  
Keyword(s):  
Plant Height ◽  
Aerial Part ◽  
Dry Matter ◽  
Low Cost ◽  
Forage Production ◽  
Forage Legumes ◽  
Randomized Design ◽  
Rhizobium Strains ◽  
Tropical Legumes ◽  
Completely Randomized Design

The objective of the work was to evaluate the effect of the application of Rhizobium bacteria in the biomass production of forage legumes. Rhizobiumstrains were isolated and selected from the rhizosphere of five forage species. Characterization and subsequent cultivation were carried out to inoculate plants in a greenhouse. Subsequently, the biofertilizer was prepared to apply it to five forage legumes: C. ternatea, L. leucocephala, C. macrocarpum, M. pruriens and C. cajan. Plants were germinated in petri dishes and on substrate, after 10 days of germination, they were transplanted into Leonards Jugs and watered with distilled water. The plants were placed in a completely randomized design with three replicates. The evaluations were carried out every week measuring plant heightand at the end of the experiment root weigh, dry matter of aerial part and radicular volume. The best values obtained were in M. pruriens which showed from 49 to 50 cm of plant height. For cross inoculation in weight variable was observed that the strains from C. ternatea and L. leucocephala showed the best results with 0.22 and 0.25 g/plant respectively. Although the best data of dry matter of aerial part was observed in L. leucocephalawith 0.40 g, better response of Radicular volume and plant height was observed in strains that came from C. ternatea with 2 mL and 7 cm respectively. In this study it can be conclude that the use of biofertilizers can be an alternative for low-cost forage production, as long as it contains Rhizobium strains capable of associating with legumes and fixing atmospheric nitrogen.

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

scholarly journals Photogrammetry for assessment of pasture biomass

2019 ◽  
pp. 33-40 ◽  
Author(s):  
Kathryn Wigley ◽  
Jennifer L. Owens ◽  
Matthias Westerschulte ◽  
Paul Riding ◽  
Jaco Fourie ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Plant Height ◽  
Vegetation Index ◽  
Digital Camera ◽  
Proof Of Concept ◽  
Destructive Sampling ◽  
Normalised Difference Vegetation Index ◽  
Surface Models ◽  
The Relationship ◽  
Strong Linear Relationship

New tools are required to provide estimates of pasture biomass as current methods are time consuming and labour intensive. This proof-of-concept study tested the suitability of photogrammetry to estimate pasture height in a grazed dairy pasture. Images were obtained using a digital camera from one site on two separate occasions (May and June 2017). Photogrammetry-derived pasture height was estimated from digital surface models created using the photos. Pasture indices were also measured using two currently available methods: a Rising Plate Meter (RPM), and Normalised Difference Vegetation Index (NDVI). Empirical pasture biomass measurements were taken using destructive sampling after all other measurements were made, and were used to evaluate the accuracy of the estimates from each method. There was a strong linear relationship between photogrammetry-derived plant height and actual biomass (R2=0.92May and 0.78June) and between RPM and actual biomass (R2=0.91May and 0.78June). The relationship between NDVI and actual biomass was relatively weaker (R2=0.65May and 0.66June). Photogrammetry could be an efficient way to measure pasture biomass with an accuracy comparable to that of the RPM but further work is required to confirm these preliminary findings.

scholarly journals Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

2018 ◽  
Author(s):  
Lukas Roth ◽  
Bernhard Streit
Keyword(s):  
Plant Height ◽  
Cover Crops ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Color Space ◽  
Canopy Cover ◽  
Ground Truth ◽  
Blue Color

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R 2 = 0.80−0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R 2 = 0.74 for four selected species, R 2 = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.

scholarly journals Soil and Crop/Tree Segmentation from Remotely Sensed Data by Using Digital Surface Models

2017 ◽  
Author(s):  
Adriano Mancini ◽  
Jack Dyson ◽  
Emanuele Frontoni ◽  
Primo Zingaretti
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Geometric Approach ◽  
Sensor Data ◽  
Remotely Sensed Data ◽  
Fourier Plane ◽  
Tree Structures ◽  
Surface Models ◽  
Integrated Or

The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.

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