Special Issue "Remote sensing for crop production and management"

A special issue of Agriculture (ISSN 2077-0472).

Deadline for manuscript submissions: closed (31 October 2015)

Special Issue Editor

Guest Editor
Dr. Yanbo Huang

United States Department of Agriculture, Agricultural Research Service, Crop Production Systems Research Unit, Stoneville, Mississippi, USA
Website | E-Mail
Phone: 662-686-5354
Interests: Aerial application technology (manned aircraft and unmanned aerial vehicles); Remote sensing for precision application (space-borne, airborne, and ground truthing); Machine learning, soft computing and decision support for precision agriculture; Spatial statistics for remote sensing data analysis; and Image processing, and process modeling, optimization, control and automation

Special Issue Information

Dear Colleagues,

Global food security requires the modernization of agriculture, which depends on precision farming with agricultural mechanization, information technology, and biotechnical innovations, through the adoption of genetically-modified cropping systems. Precision agriculture considers the within-field variability of soil, crop growth, and yield, and is implemented in the manner of site-specific management (i.e., instead of managing the entire field as one uniform unit, as in traditional farming), so as to operate at the right time, and at the needed location, with the proper amount of treatments, so as to maximize productivity and minimize costs and environmental pollution.

Remote sensing is such a technology, as it provides data for the prescription of precision agricultural operations with geographic information and global positioning data. Also, remote sensing is a cost-effective measure for the sensing and analysis of all the issues concerned in and around crop fields. Remote sensing data is valuable, in combination with other sources, for improving the estimation of crop parameters. Agricultural remote sensing began in the late 1920s when aerial photography was first used to identify cotton root rot in College Station, TX (Neblette, 1927, Photo-Era Mag. 58:346). Since then, through the general development of remote sensing technology, remote sensing has been developed, inter alia, for crop growth monitoring, estimation of evapotranspiration for irrigation scheduling, nitrogen efficiency analysis, pest management, harvest, and yield estimation. Indeed, in the next decade or so, more specialized remote sensing and analysis techniques for crop production and management will likely be developed and applied toward the improvement of crop production and management.

Review and research papers are invited in, but not limited to, the following topics:

  • Remote sensing and precision agriculture
  • Remote sensing analysis of climate change for crop proudction
  • Remote sensing big data for crop production and management
  • New platforms, sensors, and methods for remote crop monitoring
  • Satellite remote sensing for crop land observation and classifcation
  • Low-altitude remote sensing platforms, including manned airplane and unmanned aerial vehicles (UAV), for crop monitoring, stress detection, and phenotyping
  • Ground-based remote sensing platforms, from handheld to on-the-go,  for crop monitoring, stress detection, and phenotyping
  • Multispectral vs hyperspectral remote sensing for crop monitoring and stress detection
  • Remote sensing for the prescription of variable-rate application
  • Assimilation of remote sensing data into crop growth models to improve parameter estimation
  • Crop biochemical parameter inversion through physially-based radiative transfer models
  • Solar-induced fluorescence signal extrsaction for characterization of crop stress

Yanbo Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Precision agriculture
  • Climate change
  • Big data
  • Satellite remote sensing
  • Low-altitude remote sensing
  • Unmanned aerial vehicles
  • Ground-based remote sensing
  • Multispectral
  • Hyperspectral
  • Remote sensing data assimilation
  • Variable-rate technology
  • Parameter inversion
  • Radiative transfer
  • Solar-induced fluorescence
  • Crop production
  • Crop management
  • Crop monitoring
  • Crop stress
  • Crop phenotyping

Published Papers (5 papers)

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Research

Open AccessArticle Using Optical Sensors to Identify Water Deprivation, Nitrogen Shortage, Weed Presence and Fungal Infection in Wheat
Agriculture 2016, 6(2), 24; doi:10.3390/agriculture6020024
Received: 28 October 2015 / Revised: 7 March 2016 / Accepted: 20 April 2016 / Published: 26 May 2016
Cited by 2 | PDF Full-text (4425 KB) | HTML Full-text | XML Full-text
Abstract
The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the
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The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11–20 DAS. Differentiation of more than one stress factors with the same index was difficult. Full article
(This article belongs to the Special Issue Remote sensing for crop production and management)
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Open AccessArticle Monitoring Soil Sealing in Guadarrama River Basin, Spain, and Its Potential Impact in Agricultural Areas
Agriculture 2016, 6(1), 7; doi:10.3390/agriculture6010007
Received: 15 September 2015 / Revised: 19 January 2016 / Accepted: 21 January 2016 / Published: 30 January 2016
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Abstract
This study analyzes soil sealing and its repercussions in the loss of fertile soils, which are more appropriate for agriculture use. Also, soil sealing increases flood risk. The main objective is to estimate soil loss by sealing in the Guadarrama River Basin (Madrid,
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This study analyzes soil sealing and its repercussions in the loss of fertile soils, which are more appropriate for agriculture use. Also, soil sealing increases flood risk. The main objective is to estimate soil loss by sealing in the Guadarrama River Basin (Madrid, Spain) between 1961 and 2011. The combination of digital processing (Normalized Difference Vegetation Index (NDVI), principal components and convolution filters) of satellite imagery with the digital terrain model helps to detect risk areas and allows quick updating of sealed soil mapping. The supervised classifications of the images were used to estimate the actual soil loss by sealing (9% in 2011) in the Guadarrama River Basin and the types and agrologic classes that have been lost. Soil loss occurs to a greater extent in highly permeable soils (sands) and in the most fertile soils. The main sealed soil associations are luvisols (alfisols), regosols (entisols) and cambisols (inceptisols). Full article
(This article belongs to the Special Issue Remote sensing for crop production and management)
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Open AccessFeature PaperArticle A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation
Agriculture 2016, 6(1), 4; doi:10.3390/agriculture6010004
Received: 29 October 2015 / Revised: 11 December 2015 / Accepted: 29 December 2015 / Published: 18 January 2016
Cited by 2 | PDF Full-text (4210 KB) | HTML Full-text | XML Full-text
Abstract
The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based
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The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based on an industrial multi-sensor camera and a customizable image processing routine. The system was tested in a split fertilized N field trial at different growth stages in between the end of stem elongation and the end of anthesis. The acquired multispectral images were processed to normalized difference vegetation index (NDVI) and red-edge inflection point (REIP) orthoimages for an analysis with simple linear regression models. The best results for the estimation of above-ground biomass were achieved with the NDVI (R 2 = 0.72–0.85, RMSE = 12.3%–17.6%), whereas N content was estimated best with the REIP (R 2 = 0.58–0.89, RMSE = 7.6%–11.7%). Moreover, NDVI and REIP predicted grain yield at a high level of accuracy (R 2 = 0.89–0.94, RMSE = 9.0%–12.1%). Grain protein content could be predicted best with the REIP (R 2 = 0.76–0.86, RMSE = 3.6%–4.7%), with the limitation of prediction inaccuracies for N-deficient canopies. Full article
(This article belongs to the Special Issue Remote sensing for crop production and management)
Open AccessArticle The Shortwave Infrared Bands’ Response to Stomatal Conductance in “Conference” Pear Trees (Pyrus communis L.)
Agriculture 2015, 5(4), 1003-1019; doi:10.3390/agriculture5041003
Received: 30 July 2015 / Revised: 14 September 2015 / Accepted: 8 October 2015 / Published: 12 October 2015
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Abstract
In situ measurements consisting of stomatal conductance, air temperature, vapor pressure deficit and the spectral reflectance in the shortwave infrared (SWIR) regions of thirty “Conference” pear trees (Pyrus communis L.) were repeatedly measured for eighty-six days. The SWIR was segmented into eight
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In situ measurements consisting of stomatal conductance, air temperature, vapor pressure deficit and the spectral reflectance in the shortwave infrared (SWIR) regions of thirty “Conference” pear trees (Pyrus communis L.) were repeatedly measured for eighty-six days. The SWIR was segmented into eight regions between 1550 and 2365 nm, where distances ranged from 40–200 nm. Each of the regions was used to describe the change in canopy water status over a period of approximately three months. Stomatal conductance of the water stress treatment was first determined to be significantly different from the control group nine days after stress initiation. The most suitable SWIR region for this study had wavelengths between 1550 and 1750 nm, where the first significant difference was also measured nine days after stress was initiated. After the period of water stress ended, forty-seven days after stress was initiated, all of the trees received full irrigation, where the SWIR region between 1550 and 1750 nm determined that stomatal conductance of the stress treatment lagged behind the control group for thirty days. Using a temporal sequence of SWIR measurements, we were able to successfully measure the beginning and the recovery of water stress in pear trees. Full article
(This article belongs to the Special Issue Remote sensing for crop production and management)
Open AccessArticle Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data
Agriculture 2015, 5(3), 538-560; doi:10.3390/agriculture5030538
Received: 6 May 2015 / Revised: 6 July 2015 / Accepted: 17 July 2015 / Published: 23 July 2015
Cited by 11 | PDF Full-text (1131 KB) | HTML Full-text | XML Full-text
Abstract
It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass
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It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass of paddy rice on the field scale. Beyond that, the spatial and temporal transferability of established biomass regression models were investigated to prove the robustness of the method and evaluate the suitability of linear and exponential functions. In each growing season of two years, three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. Crop surface models (CSMs) were generated from the TLS-derived point clouds for calculating plant height with a very high spatial resolution of 1 cm. High coefficients of determination between CSM-derived and manually measured plant heights (R2: 0.72 to 0.91) confirm the applicability of the approach. Yearly averaged differences between the measurements were ~7% and ~9%. Biomass regression models were established from the field experiment data sets, based on strong coefficients of determination between plant height and dry biomass (R2: 0.66 to 0.86 and 0.65 to 0.84 for linear and exponential models, respectively). The spatial and temporal transferability of the models to the farmer’s conventionally managed fields is supported by strong coefficients of determination between estimated and measured values (R2: 0.60 to 0.90 and 0.56 to 0.85 for linear and exponential models, respectively). Hence, the suitability of TLS-derived spatial plant height as a non-destructive estimator for biomass of paddy rice on the field scale was verified and the transferability demonstrated. Full article
(This article belongs to the Special Issue Remote sensing for crop production and management)
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