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Remote and Proximal Sensing for Precision Agriculture and Viticulture
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Remote and Proximal Sensing for Precision Agriculture and Viticulture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 67656

Special Issue Editor

Dr. Frédéric Cointault

E-Mail Website
Guest Editor
L’Institut Agro Dijon, UMR Agroecology, 26 Bd Dr Petitjean, BP87999, 21079 Dijon, CEDEX, France
Interests: crop phenotyping; image acquisition and processing; viticulture; plant disease; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote and proximal sensing are the two most common techniques concerning the acquisition of information about an object or any phenomenon without physical contact with the object. Remote sensing is widely tied to the use of satellite, airborne or UAV platforms using multi- or hyperspectral imagery. In terms of proximal sensing, the sensor is close to the object and is installed on platforms ranging from handheld, fixed installations, or robotics and tractor-embedded sensors. The types of sensors range from simple RGB or grey-level-cameras to multispectral and hyperspectral high resoluted imaging systems or even thermographic camera.

Associated with plant growth conditions and phenotyping techniques, remote and proximal sensing are able to provide information on nutrient deficiency, biotic stress such as pests and diseases as well as abiotic stresses, allowing Precision Agriculture and Viticulture practices.

We invite thus papers on both fundamental and applied research relating on Remote and Proximal Sensing for Precision Agriculture and Viticulture, combining spectral, spatial and temporal information based on multi- and hyperspectral imagery with the capabilities of management-oriented crop simulation models. We also invite papers dedicated to new sensors able to be used in Agriculture; aiming at a better management of the crops, and methods for better crop management and more respectful of the environment.

Dr. Frédéric Cointault
Guest Editor

Keywords

  • Remote sensing
  • Proximal Sensing
  • Precision Agriculture and Viticulture
  • Image Acquisition
  • Image Processing
  • Multi- and Hyperspectral data and sensors

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Published Papers (15 papers)

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Research

15 pages, 3864 KiB  
Article
Modeling Phenols, Anthocyanins and Color Intensity of Wine Using Pre-Harvest Sentinel-2 Images
by Sandra N. Fredes, Luis Á. Ruiz and Jorge A. Recio
Remote Sens. 2021, 13(23), 4951; https://doi.org/10.3390/rs13234951 - 6 Dec 2021
Cited by 6 | Viewed by 2406
Abstract
The inclusion of technological innovation and the development of remote sensing tools in wine production are an efficient and productive factor that supports the production and improves the quality of the wine produced. In this study we explored models based on Sentinel-2 image [...] Read more.
The inclusion of technological innovation and the development of remote sensing tools in wine production are an efficient and productive factor that supports the production and improves the quality of the wine produced. In this study we explored models based on Sentinel-2 image bands and spectral indices to estimate key wine quality variables, such as phenols (TP), anthocyanins (TA) and color intensity (CI), providing different sensory characteristics of wine. Two Cabernet Sauvignon wine harvest seasons were studied, 2017 and 2018, and models with coefficients of determination (R2) higher than 60% were obtained for color intensity and total anthocyanins during the first season, both in a period very close to harvest during the first days of April, so the high periodicity of Sentinel 2 becomes strategic. In addition, homogeneous sectors can be identified in the plots for selective harvesting and thus the winery space can be programmed appropriately. These results suggest further work on the number of samples in order to transform it into a useful tool with the potential to define a differentiated harvest and estimate the accumulation of phenolic compounds and the intensity of wine color, key elements in the final quality of the wine. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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21 pages, 5666 KiB  
Article
Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images
by Eve Laroche-Pinel, Sylvie Duthoit, Mohanad Albughdadi, Anne D. Costard, Jacques Rousseau, Véronique Chéret and Harold Clenet
Remote Sens. 2021, 13(9), 1837; https://doi.org/10.3390/rs13091837 - 9 May 2021
Cited by 17 | Viewed by 4120
Abstract
Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water [...] Read more.
Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26). Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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20 pages, 4883 KiB  
Article
Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light
by Vladimir Sukhov, Ekaterina Sukhova, Andrey Khlopkov, Lyubov Yudina, Anastasiia Ryabkova, Alexander Telnykh, Ekaterina Sergeeva, Vladimir Vodeneev and Ilya Turchin
Remote Sens. 2021, 13(9), 1762; https://doi.org/10.3390/rs13091762 - 1 May 2021
Cited by 16 | Viewed by 2861
Abstract
Plants are affected by numerous environmental factors that influence their physiological processes and productivity. Early revealing of their action based on measuring spectra of reflected light and calculating reflectance indices is an important stage in the protection of agricultural plants. Photochemical reflectance index [...] Read more.
Plants are affected by numerous environmental factors that influence their physiological processes and productivity. Early revealing of their action based on measuring spectra of reflected light and calculating reflectance indices is an important stage in the protection of agricultural plants. Photochemical reflectance index (PRI) is a widely used parameter related to photosynthetic changes in plants under action of stressors. We developed a new system for proximal imaging of PRI based on using short pulses of measuring light detected simultaneously in green (530 nm) and yellow (570 nm) spectral bands. The system has several advances compared to those reported in literature. Active light illumination and subtraction of the ambient light allow for PRI measurements without periodic calibrations. Short duration of measuring pulses (18 ms) minimizes their influence on plants. Measurements in two spectral bands operated by separate cameras with aligned fields of visualization allow one to exclude mechanically switchable parts like filter wheels thus minimizing acquisition time and increasing durability of the setup. Absolute values of PRI and light-induced changes in PRI (ΔPRI) in pea leaves and changes of these parameters under action of light with different intensities, water shortage, and heating have been investigated using the developed setup. Changes in ΔPRI are shown to be more robust than the changes in the absolute value of PRI which is in a good agreement with our previous studies. Values of PRI and, especially, ΔPRI are strongly linearly related to the energy-dependent component of the non-photochemical quenching and can be potentially used for estimation of this component. Additionally, we demonstrate that the developed system can also measure fast changes in PRI (hundreds of milliseconds and seconds) under leaf illumination by the pulsed green-yellow measuring light. Thus, the developed system of proximal PRI imaging can be used for PRI measurements (including fast changes in PRI) and estimation of stressors-induced photosynthetic changes. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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15 pages, 2237 KiB  
Article
Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar
by Kensuke Kawamura, Tomohiro Nishigaki, Andry Andriamananjara, Hobimiarantsoa Rakotonindrina, Yasuhiro Tsujimoto, Naoki Moritsuka, Michel Rabenarivo and Tantely Razafimbelo
Remote Sens. 2021, 13(8), 1519; https://doi.org/10.3390/rs13081519 - 15 Apr 2021
Cited by 39 | Viewed by 5918
Abstract
As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different [...] Read more.
As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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17 pages, 6877 KiB  
Article
Monitoring the Vertical Distribution of Maize Canopy Chlorophyll Content Based on Multi-Angular Spectral Data
by Bin Wu, Huichun Ye, Wenjiang Huang, Hongye Wang, Peilei Luo, Yu Ren and Weiping Kong
Remote Sens. 2021, 13(5), 987; https://doi.org/10.3390/rs13050987 - 5 Mar 2021
Cited by 9 | Viewed by 3189
Abstract
Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology [...] Read more.
Remote sensing approaches have several advantages over traditional methods in determining information on physical and chemical parameters, including timely data acquisition, low costs, and wide coverage. Thus, remote sensing is widely used in crop growth monitoring. Unlike vertical observations, multi-angular remote sensing technology can obtain the vertical distribution information of the central and lower leaves of a crop. Furthermore, applications of remote sensing on the vertical distribution of maize canopy components is complicated, and related research is limited. In the current paper, we employed multi-angular spectral data, measured by a self-designed multi-angular observation instrument at view zenith angles (VZAs) of 0°, 10°, 20°, 30°, 40°, 50°, and 60°, to explore the monitoring strategy and monitoring precision of the vertical distribution of chlorophyll content in the maize canopy. This was then used to determine the optimal monitoring method for the chlorophyll content (soil and plant analyzer development (SPAD) value) of each layer. The correlation between SPAD value and chlorophyll sensitivity indices at different growth stages was used as the basis for screening indices and VZAs. The correlation between the selected EPI (eucalyptus pigment index) and REIP (red edge inflection point) indices and chlorophyll content indicated view zenith angles (VZAs) of 0°, 30°, and 40° as optimal for the early growth stage monitoring of chlorophyll content in the 1st, 2nd, and 3rd layers, respectively. These values were associated with RMSEs of 4.14, 1.71, and 1.11 for EPI, respectively; and 4.61, 2.31, and 1.00 for REIP, respectively. In addition, a VZA of 50° was selected to monitor the chlorophyll content of the 1st, 2nd, 3rd, and 4th layers at the late growth stage, with RMSE values of 2.97, 3.50, 2.80, and 4.80 for EPI, respectively; and 3.16, 5.02, 4.55, and 7.85 for REIP, respectively. The results demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the maize canopy chlorophyll content vertical distribution, with the VZAs of different vertical layers varying between the early and late growth stages. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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14 pages, 13836 KiB  
Article
Effect of the Illumination Angle on NDVI Data Composed of Mixed Surface Values Obtained over Vertical-Shoot-Positioned Vineyards
by Pedro C. Towers and Carlos Poblete-Echeverría
Remote Sens. 2021, 13(5), 855; https://doi.org/10.3390/rs13050855 - 25 Feb 2021
Cited by 10 | Viewed by 2212
Abstract
Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized [...] Read more.
Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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18 pages, 11853 KiB  
Article
Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat
by Jiale Jiang, Jie Zhu, Xue Wang, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao and Xia Yao
Remote Sens. 2021, 13(4), 739; https://doi.org/10.3390/rs13040739 - 17 Feb 2021
Cited by 15 | Viewed by 3933
Abstract
Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification [...] Read more.
Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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24 pages, 5812 KiB  
Article
Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models
by Oveis Hassanijalilian, C. Igathinathane, Sreekala Bajwa and John Nowatzki
Remote Sens. 2020, 12(24), 4143; https://doi.org/10.3390/rs12244143 - 18 Dec 2020
Cited by 9 | Viewed by 3251
Abstract
The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual [...] Read more.
The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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18 pages, 3939 KiB  
Article
Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery
by Ramin Heidarian Dehkordi, Moussa El Jarroudi, Louis Kouadio, Jeroen Meersmans and Marco Beyer
Remote Sens. 2020, 12(22), 3696; https://doi.org/10.3390/rs12223696 - 11 Nov 2020
Cited by 37 | Viewed by 6449
Abstract
During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the [...] Read more.
During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) < 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients < 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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24 pages, 3502 KiB  
Article
Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat
by Jie Jiang, Zeyu Zhang, Qiang Cao, Yan Liang, Brian Krienke, Yongchao Tian, Yan Zhu, Weixing Cao and Xiaojun Liu
Remote Sens. 2020, 12(22), 3684; https://doi.org/10.3390/rs12223684 - 10 Nov 2020
Cited by 26 | Viewed by 3693
Abstract
Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV [...] Read more.
Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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20 pages, 6686 KiB  
Article
An Innovative Approach for Improving the Accuracy of Digital Elevation Models for Cultivated Land
by Yuyang Ma, Huanjun Liu, Baiwen Jiang, Linghua Meng, Haixiang Guan, Mengyuan Xu, Yang Cui, Fanchang Kong, Yue Yin and MengPei Wang
Remote Sens. 2020, 12(20), 3401; https://doi.org/10.3390/rs12203401 - 16 Oct 2020
Cited by 8 | Viewed by 2493
Abstract
The redistribution of solar radiation, temperature, soil moisture and heat by topography affects the physical and chemical properties of the soil and the spatial distribution characteristics of crop growth. Analyses of the relationship between topography and these variables may help to improve the [...] Read more.
The redistribution of solar radiation, temperature, soil moisture and heat by topography affects the physical and chemical properties of the soil and the spatial distribution characteristics of crop growth. Analyses of the relationship between topography and these variables may help to improve the accuracy of digital elevation models (DEMs). The purpose of correcting Shuttle Radar Topography Mission (SRTM) data is to obtain high-precision DEM data in cultivated land. A typical black soil area was studied. A high-precision reference DEM was generated from an unmanned aerial vehicle (UAV) and extensive measured ground elevation data. The normalized differential vegetation index (NDVI), perpendicular drought index (PDI) extracted from SPOT-6 remote sensing images and potential solar radiation (PSR) extracted from SRTM. The interactions between topography and NDVI, PDI, and PSR were analyzed. The NDVI, PDI and PSR in June, July, August and September of 2016 and the SRTM were used as independent variables, and the UAV DEM was used as the dependent variable. Linear stepwise regression (LSR) and a back-propagation neural network (BPNN) were used to establish an elevation prediction model. The results indicated that (1) The correlation between topography and NDVI, PSR, PDI was significant at 0.01 level. The PDI and PSR improved the spatial resolution of SRTM data and reduce the vertical error. (2) The BPNN (R21 = 0.98, root mean square error, RMSE1 = 0.54) yielded a higher SRTM accuracy than did the studied linear model (RMSE1 = 1.00, R21 = 0.90). (3) A series of significant improvements in the SRTM were observed when assessed with the reference DEMs for two different areas, with RMSE reductions of 91% (from 14.95 m to 1.23 m) and 93% (from 15.6 m to 0.94 m). The proposed method improved the accuracy of existing DEMs and could provide support for accurate farmland management. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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30 pages, 12201 KiB  
Article
Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology
by Matthew Maimaitiyiming, Vasit Sagan, Paheding Sidike, Maitiniyazi Maimaitijiang, Allison J. Miller and Misha Kwasniewski
Remote Sens. 2020, 12(19), 3216; https://doi.org/10.3390/rs12193216 - 2 Oct 2020
Cited by 28 | Viewed by 6955
Abstract
Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, [...] Read more.
Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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20 pages, 7420 KiB  
Article
Management Zone Delineation for Site-Specific Fertilization in Rice Crop Using Multi-Temporal RapidEye Imagery
by Christos Karydas, Miltiadis Iatrou, George Iatrou and Spiros Mourelatos
Remote Sens. 2020, 12(16), 2604; https://doi.org/10.3390/rs12162604 - 12 Aug 2020
Cited by 8 | Viewed by 4072
Abstract
The objective of this research is to assess the potential of satellite imagery in detecting soil heterogeneity, with a focus on site-specific fertilization in rice. The basic hypothesis is that spectral variation would express soil fertility variations analogously. A 100-ha rice crop, located [...] Read more.
The objective of this research is to assess the potential of satellite imagery in detecting soil heterogeneity, with a focus on site-specific fertilization in rice. The basic hypothesis is that spectral variation would express soil fertility variations analogously. A 100-ha rice crop, located in the Plain of Thessaloniki, Greece, was selected as the study area for the 2016 cropping season. Three RapidEye images were acquired during critical growth stages of rice cultivation from the previous year (2015). Management zones were delineated with image segmentation of a 15-band multi-temporal composite of the RapidEye images (three dates × five bands), using the Fractal Net Evolution Approach (FNEA) algorithm. Then, an equal number of soil samples were collected from the centroid of each management zone before seedbed preparation. The between-zone variation of the soil properties was found to be 33.7% on average, whereas the within-zone variation 18.2%. The basic hypothesis was confirmed, and moreover, it was proved that zonal applications reduced within-zone soil variation by 18.6% compared to conventional uniform applications. Finally, between-zone soil variation was significant enough to dictate differentiated fertilization recommendations per management zone by 24.5% for the usual inputs. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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22 pages, 8517 KiB  
Article
Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms
by Aijing Feng, Jianfeng Zhou, Earl Vories and Kenneth A. Sudduth
Remote Sens. 2020, 12(11), 1764; https://doi.org/10.3390/rs12111764 - 30 May 2020
Cited by 28 | Viewed by 4701
Abstract
Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. [...] Read more.
Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes efficiently and build panoramas for each narrow band. The normalized difference vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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21 pages, 8437 KiB  
Article
Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery
by Luís Pádua, Telmo Adão, António Sousa, Emanuel Peres and Joaquim J. Sousa
Remote Sens. 2020, 12(1), 139; https://doi.org/10.3390/rs12010139 - 1 Jan 2020
Cited by 40 | Viewed by 5366
Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of [...] Read more.
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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