scholarly journals A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera

2018 ◽  
Vol 10 (7) ◽  
pp. 1138 ◽  
Author(s):  
Jibo Yue ◽  
Haikuan Feng ◽  
Xiuliang Jin ◽  
Huanhuan Yuan ◽  
Zhenhai Li ◽  
...  
Keyword(s):  
Digital Camera ◽  
Parameters Estimation ◽  
Growth Stages ◽  
Least Squares Regression ◽  
High Definition ◽  
Area Index ◽  
Near Surface ◽  
Crop Height ◽  
Crop Parameters ◽  
Hyperspectral Sensor

Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.

scholarly journals Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1296 ◽  
Author(s):  
Huilin Tao ◽  
Haikuan Feng ◽  
Liangji Xu ◽  
Mengke Miao ◽  
Huiling Long ◽  
...  
Keyword(s):  
Growth Parameters ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Growth Stages ◽  
Complex Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Red Edge ◽  
Highly Correlated

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

scholarly journals Impact of irrigation strategies on maize (Zea mays L.) production in the savannah region of Northern Togo (West Africa)

Water SA ◽  
2020 ◽  
Vol 46 (1 January) ◽  
Author(s):  
Agossou Gadédjisso-Tossou ◽  
Tamara Avellán ◽  
Niels Schütze
Keyword(s):  
Grain Yield ◽  
Deficit Irrigation ◽  
Canopy Cover ◽  
Moderate Level ◽  
Yield Response ◽  
Growth Stages ◽  
Area Index ◽  
Vegetative And Reproductive Growth ◽  
Crop Parameters ◽  
Response To Water

In northern Togo where rainfed maize is one of the major crops grown, agriculture is subject to frequent yield losses due to erratic rainfall. To ensure food availability and improve agricultural productivity, it is necessary to produce maize during the dry season under irrigation. A sound application of full and deficit irrigation requires a thorough understanding of the crop parameters and yield response to water. Thus, this study investigated the effect of full and deficit irrigation on maize plant above-ground biomass, leaf area index, canopy cover, plant height, and grain yield. A field experiment was carried out from December 2017 to April 2018 in northern Togo at the agronomic research institute.  Full irrigation (FI), 80% FI, and 60% FI treatments were applied. The results showed that in the late-season stage, the differences in biomass between FI and 60% FI were significant (p < 0.05). On average, FI had the greatest grain yield (2 200.4 kg/ha), while the lowest grain yield was recorded under 60% FI (1,068.3 kg/ha). The grain yield differences between FI and 60% FI were significant. Nevertheless, the grain yield differences between FI and 80% FI were not significant (p > 0.05). 80% FI had water use efficiency (WUE) (0.22 kg/m3) similar to that of FI (0.21 kg/m3), on average. The results of this study illustrate that deficit irrigation must be carefully managed since slight differences in the application volumes affect the biomass and yield of maize significantly. Under a moderate level of deficit irrigation (vegetative and reproductive growth stages) the biomass and the grain yield of maize are reduced. However, a moderate level of deficit irrigation during the vegetative growth stage could result in similar values of WUE to that of FI.

scholarly journals Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

2020 ◽  
Vol 12 (6) ◽  
pp. 12
Author(s):  
Tengku Adhwa Syaherah Tengku Mohd Suhairi ◽  
Siti Sarah Mohd Sinin ◽  
Eranga M. Wimalasiri ◽  
Nur Marahaini Mohd Nizar ◽  
Anil Shekar Tharmandran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Ground Level ◽  
Bambara Groundnut ◽  
Growth Stages ◽  
Remotely Sensed Data ◽  
Nutrition Security ◽  
Lack Of Information ◽  
Normalised Difference Vegetation Index ◽  
Underutilised Crops ◽  
Crop Parameters

In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p &lt; 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p &lt; 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

Using Hemispherical Photography to Determine Leaf Area Index of Landscape Trees in Urban Green Space of Dongguan, South China

2014 ◽  
Vol 955-959 ◽  
pp. 4034-4038
Author(s):  
Luo Jian Mo ◽  
Wen Bin Li ◽  
Yong Chang Ye ◽  
Yong Wen Zhou ◽  
Song Song Liu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Space ◽  
Urban Green Space ◽  
Growth Stages ◽  
Hemispherical Photography ◽  
Area Index ◽  
Urban Green ◽  
Large Trees ◽  
Landscape Trees

Transect sampling method was used to measure structural attributes of landscape trees in urban green space of three city parks and one residential greenbelt in Dongguan. Leaf area index (LAI) of the landscape trees in each urban green space was determined using hemispherical photography. Average DBH (diameter at the breast height) and CW(crown width) in Wenhua Square were the largest due to the presence of heritage large trees, while the landscape trees were species diverse in Renmin Park. A comparison of LAI in the green space gave a result in descending order: Renmin Park > Wenhua Square > Jinhuwan greenbelt > Yuanmei Park. The case of Renmin Park indicated that when a green space consisted of various structural attributes, landscape trees in different growth stages tended to have large LAI. Findings of our study suggested that a diversity of trees with potentially different LAI should be selected when planning urban green space.

scholarly journals An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

2008 ◽  
Vol 35 (10) ◽  
pp. 1070 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Anthony R. Palmer ◽  
Daniel Taylor ◽  
Melanie Zeppel ◽  
Rhys Whitley ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gap Analysis ◽  
Digital Camera ◽  
Field Measurements ◽  
Accurate Estimation ◽  
Image Capture ◽  
Area Index ◽  
Modis Lai ◽  
Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

scholarly journals Effect of row width on splash erosion and throughfall in silage maize crops

2017 ◽  
Vol 12 (No. 1) ◽  
pp. 39-50 ◽  
Author(s):  
V. Brant ◽  
P. Zábranský ◽  
M. Škeříková ◽  
J. Pivec ◽  
M. Kroulík ◽  
...  
Keyword(s):  
Stem Elongation ◽  
Field Measurements ◽  
Positive Influence ◽  
Row Spacing ◽  
Growth Stages ◽  
Area Index ◽  
Factors Affecting ◽  
Splash Erosion ◽  
Silage Maize ◽  
Plant Length

Line width is one of the major factors affecting arable soil erosion. The aim of the study was to assess the effects of different row spacing on splash erosion and throughfall in maize crops. Field measurements of the throughfall (P<sub>th</sub>, mm) and splash erosion (MSR, g/m<sup>2</sup>) were carried out in silage maize crops (row spacing 0.45 and 0.75 m) in 2012–2014. The BBCH growth stages for the crops, plant length (L, m), and leaf area index (LAI) were evaluated. Positive correlation was observed between the aerial precipitation (P, mm) and the P<sub>th</sub> values. With increasing P-values, higher levels of P<sub>th</sub> were identified in the 0.75 m compared to the 0.45 m row spacing. The value of this proportion was decreasing from the centre of the inter-row (0.75 m) to the row of the plants direction. Statistically significant lower values of splash erosion were observed in the 0.45 m compared with the 0.75 m wide rows, especially within the years 2012 and 2014. The experiments proved the positive influence of the length of plants and LAI on P/P<sub>th </sub>values. A decrease of P<sub>th </sub> in relation to precipitation values with height of plants and LAI values was observed. This dependency was then confirmed from the beginning of the stem elongation (BBCH 30) to the end of flowering (BBCH 70). Tighter dependency between the plant length (L) and the values of P/P<sub>th</sub> ratio in the 0.75 m wide crop rows was determined. Conversely, a more important influence of LAI on the values of P/P<sub>th</sub> ratio was estimated in the 0.45 m wide crop rows. The experiments proved the positive influence of the 0.45 m wide rows on the decrease of splash erosion as well as throughfall compared with the 0.75 m row spacing.  

scholarly journals RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

2020 ◽  
Vol 12 (18) ◽  
pp. 2982 ◽  
Author(s):  
Christelle Gée ◽  
Emmanuel Denimal
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Weed Management ◽  
Vegetation Index ◽  
Management Strategies ◽  
Weed Competition ◽  
Growth Stages ◽  
Area Index ◽  
Crop Weed Competition ◽  
The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

A simple approach to estimate coastal soil salinity using digital camera images

Soil Research ◽  
2020 ◽  
Vol 58 (8) ◽  
pp. 737
Author(s):  
Lu Xu ◽  
Raphael A. Viscarra Rossel ◽  
Juhwan Lee ◽  
Zhichun Wang ◽  
Hongyuan Ma
Keyword(s):  
Soil Salinity ◽  
Precision Agriculture ◽  
Mean Squared Error ◽  
Rapid Development ◽  
Salt Content ◽  
Digital Camera ◽  
Soil Surface ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
High Concentration

Soil salinisation is a global problem that hinders the sustainable development of ecosystems and agricultural production. Remote and proximal sensing technologies have been used to effectively evaluate soil salinity over large scales, but research on digital camera images is still lacking. In this study, we propose to relate the pixel brightness of soil surface digital images to the soil salinity information. We photographed the surface of 93 soils in the field at different times and weather conditions, and sampled the corresponding soils for laboratory analyses of soil salinity information. Results showed that the pixel digital numbers were related to soil salinity, especially at the intermediate and higher brightness levels. Based on this relationship, we employed random forest (RF) and partial least-squares regression (PLSR) to model soil salt content and ion concentrations, and applied root mean squared error, coefficient of determination and Lin’s concordance correlation coefficient to evaluate the accuracy of models. We found that ions with high concentration were estimated more accurately than ions with low concentrations, and RF models performed overall better than PLSR models. However, the method is only suitable for bare land of coastal soil, and verification is needed for other conditions. In conclusion, a new approach of using digital camera images has good potential to predict and manage soil salinity in the context of precision agriculture with the rapid development of unmanned aerial vehicles.

scholarly journals Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots

2020 ◽  
Vol 12 (21) ◽  
pp. 3478
Author(s):  
Ofer Beeri ◽  
Yishai Netzer ◽  
Sarel Munitz ◽  
Danielle Ferman Mintz ◽  
Ran Pelta ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Crop Coefficient ◽  
Landsat 8 ◽  
Area Index ◽  
Management Zone ◽  
Growing Seasons ◽  
Crop Parameters ◽  
Better Than ◽  
Sentinel 2

Daily or weekly irrigation monitoring conducted per sub-field or management zone is an important factor in vine irrigation decision-making. The objective is to determine the crop coefficient (Kc) and the leaf area index (LAI). Since the 1990s, optic satellite imagery has been utilized for this purpose, yet cloud-cover, as well as the desire to increase the temporal resolution, raise the need to integrate more imagery sources. The Sentinel-1 (a C-band synthetic aperture radar—SAR) can solve both issues, but its accuracy for LAI and Kc mapping needs to be determined. The goals of this study were as follows: (1) to test different methods for integrating SAR and optic sensors for increasing temporal resolution and creating seamless time-series of LAI and Kc estimations; and (2) to evaluate the ability of Sentinel-1 to estimate LAI and Kc in comparison to Sentinel-2 and Landsat-8. LAI values were collected at two vineyards, over three (north plot) and four (south plot) growing seasons. These values were converted to Kc, and both parameters were tested against optic and SAR indices. The results present the two Sentinel-1 indices that achieved the best accuracy in estimating the crop parameters and the best method for fusing the optic and the SAR data. Utilizing these achievements, the accuracy of the Kc and LAI estimations from Sentinel-1 were slightly better than the Sentinel-2′s and the Landsat-8′s accuracy. The integration of all three sensors into one seamless time-series not only increases the temporal resolution but also improves the overall accuracy.

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