Special Issue "Remote Sensing in Agricultural System"

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

Deadline for manuscript submissions: closed (31 August 2018).

Special Issue Editor

Dr. Raphael Linker
Website
Guest Editor
Technion-Israel Inst Technol, Techn Fac Civil & Environm Engn, Haifa, Israel
Interests: sensing; mid-infrared spectroscopy and hyperspectral imaging; modeling, control and optimization of agricultural and environmental systems

Special Issue Information

Dear Colleagues,

The last decade has witnessed tremendous technological developments in the field of remote sensing, in general, and its application to agriculture, in particular. Agricultural remote sensing can be broadly divided into three categories according to the type of platform used to collect the information: Satellites, airplanes or small unmanned aircraft vehicles. Each kind of platform has its own challenges: For satellites, the main issues are related to spatial and/or spectral resolution and re-visitation time, all of which are beyond the control of the end-user. Long delays between data acquisition and publication of corrected and validated final products by the data provider may also be an issue. Most of these issues can be avoided by using sensing platforms mounted on airplanes, but these are typically costly to operate and appear to be appropriate only under very specific conditions. Small, low flying, unmanned aircraft vehicles (UAVs) have recently attracted tremendous interest due to their flexibility, relative low-cost and perceived ease of operation. However, really low-cost systems have very limited payload and flight-time, while more "heavy-duty" systems are significantly more expensive and, in some areas, their use may be severely regulated. The goal of this Special Issue is to present an up-to-date overview of the recent achievements in the field of agricultural remote sensing, as well as to identify the obstacles still ahead, not only in terms of scientific and technological developments, but also in terms of the barriers that still prevent wider adoption of remote sensing as a key component of farm management.

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

·    Existing and planned satellite-based sensors dedicated to agriculture

·    Technological developments in the field of multispectral, hyperspectral and thermal imaging

·    Sun-induced fluorescence

·    Agricultural UAVs

·    Use of remote sensing data for defining Management Zones

·    Use of remote sensing data for calibrating or improving crop models (data assimilation)

·    Use of remote sensing data for disease/pest detection and management

·    Fusion of remote sensing data with other data streams

·    Generic (not crop specific) models and indices for estimating LAI, biomass and other key crop characteristics from remote sensing data

Dr. Raphael Linker
Guest Editor

Manuscript Submission Information

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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 1000 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

  • Data assimilation
  • Data-driven management of agricultural systems
  • Hyperspectral/multispectral imaging
  • Management zones
  • Precision agriculture
  • Sun-induced fluorescence
  • Thermal imaging
  • Unmanned aerial vehicles

Published Papers (5 papers)

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Research

Open AccessArticle
Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks
Agriculture 2018, 8(10), 147; https://doi.org/10.3390/agriculture8100147 - 20 Sep 2018
Cited by 6
Abstract
Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. [...] Read more.
Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessArticle
Does Solar Radiation Affect the Distribution of Dubas Bug (Ommatissus lybicus de Bergevin) Infestation
Agriculture 2018, 8(7), 107; https://doi.org/10.3390/agriculture8070107 - 05 Jul 2018
Cited by 5
Abstract
The Dubas bug Ommatissus lybicus is a serious pest of date palms. The infestation level of the Dubas bug varies from location to location, as well as from one season to the next. Climate factors are considered to be the main drivers for [...] Read more.
The Dubas bug Ommatissus lybicus is a serious pest of date palms. The infestation level of the Dubas bug varies from location to location, as well as from one season to the next. Climate factors are considered to be the main drivers for fluctuations in infestation levels. Few studies have examined the effects of solar radiation on O. lybicus infestation. This study was undertaken to examine the effect of solar radiation on O. lybicus infestation levels in Oman. Infestation data were collected during the spring infestation seasons of 2009 and 2016 from 49 and 69 locations, respectively, from seven governorates of North Oman. The monthly clear-sky potential solar radiation was calculated from a digital elevation model (DEM) with 20-m resolution in the ArcGIS environment, and the average daily solar radiation was calculated for each month. Ordinary least square regression (OLS) and geographic weight regression (GWR) models were run to find the relationship between infestation levels and solar radiation. The infestation level ranged from 0.02 insect/leaflet to 32.98 insects/leaflet, with an average of 7.50 insects/leaflet in 2009 and 0.17 insect/leaflet to 17.52 insects/leaflet, with an average of 4.38 insects/leaflet in 2016. The highest solar radiation was recorded in June, with an average of 27.7 MJ/m2/day, and the minimum was in December, with an average of 14.1 MJ/m2/day. The higher infestation rate showed a weak correlation with solar radiation. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessArticle
A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone
Agriculture 2018, 8(5), 70; https://doi.org/10.3390/agriculture8050070 - 17 May 2018
Cited by 25
Abstract
Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three [...] Read more.
Silage is the main feed in milk and ruminant meat production in Northern Europe. Novel drone-based remote sensing technology could be utilized in many phases of silage production, but advanced methods of utilizing these data are still developing. Grass swards are harvested three times in season, and fertilizer is applied similarly three times—once for each harvest when aiming at maximum yields. Timely information of the yield is thus necessary several times in a season for making decisions on harvesting time and rate of fertilizer application. Our objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data. Variation in the studied crop stand was generated using six different nitrogen fertilizer levels and four harvesting dates. The sward was a timothy-meadow fescue mixture dominated by timothy. We extracted various features from the remote sensing data by combining an ultra-high resolution photogrammetric canopy height model (CHM) with a pixel size of 1.0 cm and red, green, blue (RGB) and near-infrared range intensity values and different vegetation indices (VI) extracted from orthophoto mosaics. We compared the performance of multiple linear regression (MLR) and a Random Forest estimator (RF) with different combinations of the CHM, RGB and VI features. The best estimation results with both methods were obtained by combining CHM and VI features and all three feature classes (CHM, RGB and VI features). Both estimators provided equally accurate results. The Pearson correlation coefficients (PCC) and Root Mean Square Errors (RMSEs) of the estimations were at best 0.98 and 0.34 t/ha (12.70%), respectively, for the dry matter yield (DMY) and 0.98 and 1.22 t/ha (11.05%), respectively, for the fresh yield (FY) estimations. Our assessment of the sensitivity of the method with respect to different development stages and different amounts of biomass showed that the use of the machine learning technique that integrated multiple features improved the results in comparison to the simple linear regressions. These results were extremely promising, showing that the proposed multispectral photogrammetric approach can provide accurate biomass estimates of grass swards, and could be developed as a low-cost tool for practical farming applications. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessArticle
Relationship of Date Palm Tree Density to Dubas Bug Ommatissus lybicus Infestation in Omani Orchards
Agriculture 2018, 8(5), 64; https://doi.org/10.3390/agriculture8050064 - 29 Apr 2018
Cited by 8
Abstract
Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops [...] Read more.
Date palm trees, Phoenix dactylifera, are the primary crop in Oman. Most date palm cultivation is under the traditional agricultural system. The plants are usually under dense planting, which makes them prone to pest infestation. The main pest attacking date palm crops in Oman is the Dubas bug Ommatissus lybicus. This study integrated modern technology, remote sensing and geographic information systems to determine the number of date palm trees in traditional agriculture locations to find the relationship between date palm tree density and O. lybicus infestation. A local maxima method for tree identification was used to determine the number of date palm trees from high spatial resolution satellite imagery captured by WorldView-3 satellite. Window scale sizes of 3, 5 and 7 m were tested and the results showed that the best window size for date palm trees number detection was 7 m, with an overall estimation accuracy 88.2%. Global regression ordinary least square (OLS) and local geographic weighted regression (GWR) were used to test the relationship between infestation intensity and tree density. The GWR model showed a good positive significant relationship between infestation and tree density in the spring season with R2 = 0.59 and medium positive significant relationship in the autumn season with R2 = 0.30. In contrast, the OLS model results showed a weak positive significant relationship in the spring season with R2 = 0.02, p < 0.05 and insignificant relationship in the autumn season with R2 = 0.01, p > 0.05. The results indicated that there was a geographic effect on the infestation of O. lybicus, which had a greater impact on infestation severity, and that the impact of tree density was higher in the spring season than in autumn season. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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Open AccessArticle
Fluorescence and Reflectance Sensor Comparison in Winter Wheat
Agriculture 2017, 7(9), 78; https://doi.org/10.3390/agriculture7090078 - 20 Sep 2017
Cited by 4
Abstract
Nitrogen (N) is the most important macronutrient in plant production. For N application, legislation requirements have raised, and the purchasing costs have increased. Modern sensors can help farmers to save costs, to apply the right quantity, and to reduce their impact on the [...] Read more.
Nitrogen (N) is the most important macronutrient in plant production. For N application, legislation requirements have raised, and the purchasing costs have increased. Modern sensors can help farmers to save costs, to apply the right quantity, and to reduce their impact on the environment. Two spectrometers and one fluorescence sensor have been used on a vehicle sensor platform for N detection in wheat (Triticum aestivum L.) field trials over three years. The research fields were divided into plots, and the N input ranged from 60 to 180 kg N ha−1 in six levels. The OSAVI (optimized soil-adjusted vegetation index) showed a similar value pattern to the NDVI (normalized difference vegetation index) and the CropSpec index for the investigated factors. The red-edge inflection point (REIP) index showed high correlations to N (indicated by r2 between 0.6 and 0.8), especially in June and July. The developed models from the fluorescence indices FERARI, NBIR, FLAV, and the spectrometer indices CropSpec and HVI show high correlations (r2 = 0.5–0.8) to yield and may be used for future yield predictions. The Multiplex Research™ fluorescence sensor (Force-A, Orsay, France) was the most convenient sensor with a simple measurement method and a non-proprietary file output. The implementation into existing agricultural vehicle networks is still necessary, being able to use it on a farm for online N recommendations. Full article
(This article belongs to the Special Issue Remote Sensing in Agricultural System)
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