Special Issue "Precision Agriculture"

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

Deadline for manuscript submissions: 30 December 2018

Special Issue Editors

Guest Editor
Prof. José Antonio Martínez-Casasnovas

Department of Environment and Soil Sciences Research Group on AgroICT and Precision Agriculture University of Lleida - Agrotecnio Center Rovira Roure 191, 25198 Lleida, Catalonia, Spain
Website | E-Mail
Interests: precision agriculture, digital soil mapping, spatial analysis of intrafield variability, remote sensing of crop vigour, zoning of crop varialbes
Guest Editor
Dr. Jaume Arnó Satorra

Research Group on Agro ICT & Precision Agriculture, University of Lleida, Catalonia (Spain)
Website | E-Mail
Phone: +34 973 702 859
Interests: sampling and geostatistical analysis in agriculture, use of sensors, variable rate technologies (VRT), software applied to Precision Agriculture

Special Issue Information

Dear Colleagues,

Precision Agriculture (PA), also referred to as Precision Farming or Smart Agriculture, is a relatively recent farming management concept based on the use of information technology with the aim of obtaining higher production efficiency, sustainable profitability and better quality products, while minimizing environmental impacts. PA uses a combination of technological advances, such as crop and soil sensors, satellite navigation and positioning technology, remote sensing, variable-rate application machinery and the Internet of Things, among other. On the other hand, in order to take site-specific crop management decisions, PA uses methods to process and analyse data to know the magnitude and spatiotemporal patterns of crop variability. Making the right decision at the right time to apply the right amount of inputs is an important issue because productivity, economic benefit and sustainability are dependent on proper farm management. PA has a big potential and can make a significant contribution to food production, security and safety but, although PA technologies are already widely available, their level of implementation is still low. Nevertheless, influential work practices and new farming business models are on the rise as application of precision farming concepts.

This Special Issue intends to cover the state-of-the-art and recent progress in different aspects related to the real implementation of Precision Agriculture in a wide range of cropping systems (grain crops, grassland, horticultural crops, fruit trees). All types of manuscripts (original research and reviews) providing new insights in the application and benefits of Precision Agriculture methods and technology are welcome. Articles may include, but are not limited to, the following topics:

  • Spatial analysis and zoning of within-field an on-farm variability
  • Proximal and remote sensing of soils and crops
  • Sampling and geostatistical analysis
  • Wireless sensor networks, Internet of Things, big data in PA
  • Variable-Rate Technologies
  • Precision irrigation
  • Ag-engineering and robotics
  • Precision crop protection
  • Nitrogen sensing and management
  • Crop models and decision support systems in PA
  • Real implementation and effectiveness of PA (economical and/or environmental)

Prof. José Antonio Martínez-Casasnovas
Dr. Jaume Arnó Satorra
Guest Editors

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
  • Precision farming
  • Spatial analysis
  • Remote sensing
  • Proximal sensing
  • Variable-Rate Technology

Published Papers (7 papers)

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Research

Open AccessFeature PaperArticle Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture
Agriculture 2018, 8(7), 116; https://doi.org/10.3390/agriculture8070116
Received: 30 May 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 23 July 2018
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Abstract
High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian
[...] Read more.
High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessFeature PaperArticle Management of Plant Growth Regulators in Cotton Using Active Crop Canopy Sensors
Agriculture 2018, 8(7), 101; https://doi.org/10.3390/agriculture8070101
Received: 1 June 2018 / Revised: 24 June 2018 / Accepted: 25 June 2018 / Published: 2 July 2018
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Abstract
Factors affecting cotton development present spatial and temporal variability. Plant growth regulators (PGR) are used to control vegetative growth, promote higher yields, better fiber quality, and facilitate mechanical harvest. The optimal rate of PGR application depends on crop height, biomass, and growth rate.
[...] Read more.
Factors affecting cotton development present spatial and temporal variability. Plant growth regulators (PGR) are used to control vegetative growth, promote higher yields, better fiber quality, and facilitate mechanical harvest. The optimal rate of PGR application depends on crop height, biomass, and growth rate. Thus, the objective of this study was to evaluate optical and ultrasonic crop canopy sensors to detect the crop spatial variability in cotton fields, and to develop strategies for using this information to perform variable rate application (VRA) of PGR in cotton. Field trials were conducted in Midwest Brazil during the 2013/2014 and 2014/2015 crop seasons. Two optical and two ultrasonic active crop canopy sensors were evaluated as tools to detect crop variability. On-farm trials were used to develop and validate algorithms for VRA based on within-field variations in crop response to PGR applications. The overall performance of the sensors to predict crop height and the accumulation of biomass in cotton was satisfactory. Short distance variability was predominant in some fields, reducing the performance of the sensors while making current technology for variable rate application of PGR inadequate. In areas with large scale variability, the VRA led to 17% savings in PGR products and no significant effect on yield was observed. Ultrasonic sensors present can be a low-cost alternative to implement variable rate application of PGR in real time. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes
Agriculture 2018, 8(7), 94; https://doi.org/10.3390/agriculture8070094
Received: 30 April 2018 / Revised: 21 June 2018 / Accepted: 22 June 2018 / Published: 26 June 2018
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Abstract
Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices
[...] Read more.
Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard (Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015–2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics (r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI (r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)
Agriculture 2018, 8(6), 84; https://doi.org/10.3390/agriculture8060084
Received: 28 May 2018 / Revised: 8 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis,
[...] Read more.
One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer’s expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers’ expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle Sampling Stratification Using Aerial Imagery to Estimate Fruit Load in Peach Tree Orchards
Agriculture 2018, 8(6), 78; https://doi.org/10.3390/agriculture8060078
Received: 30 April 2018 / Revised: 29 May 2018 / Accepted: 30 May 2018 / Published: 4 June 2018
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Abstract
A quick and accurate sampling method for determining yield in peach orchards could lead to better crop management decisions, more accurate insurance claim adjustment, and reduced expenses for the insurance industry. Given that sample size depends exclusively on the variability of the trees
[...] Read more.
A quick and accurate sampling method for determining yield in peach orchards could lead to better crop management decisions, more accurate insurance claim adjustment, and reduced expenses for the insurance industry. Given that sample size depends exclusively on the variability of the trees on the orchard, it is necessary to have a quick and objective way of assessing this variability. The aim of this study was to use remote sensing to detect the spatial variability within peach orchards and classify trees into homogeneous zones that constitute sampling strata to decrease sample size. Five mature peach orchards with different degrees of spatial variability were used. A regular grid of trees was established on each orchard, their trunk cross-sectional area (TCSA) was measured, and yield was measured as number of fruits/tree on the central tree of each one of them. Red Vegetation Index (RVI) was calculated from aerial images with 0.25 m·pixel−1 resolution, and used, either alone or in combination with TCSA, to delineate sampling strata using cluster fuzzy k-means. Completely randomized (CRS) and stratified samplings were compared through 10,000 iterations, and the Minimum Sample Size required to obtain estimates of actual production for three quality levels of sampling was calculated in each case. The images allowed accurate determination of the number of trees, allowing a proper application of completely randomized sampling designs. Tree size and the canopy density estimated by means of multispectral indices are complementary parameters suitable for orchard stratification, decreasing the sample size required to determine fruit count up to 20–35% compared to completely randomized samples. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle GeoFIS: An Open Source, Decision-Support Tool for Precision Agriculture Data
Agriculture 2018, 8(6), 73; https://doi.org/10.3390/agriculture8060073
Received: 25 April 2018 / Revised: 23 May 2018 / Accepted: 28 May 2018 / Published: 30 May 2018
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Abstract
The world we live in is an increasingly spatial and temporal data-rich environment, and agriculture is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of ‘Precision Agriculture’ and ‘Smart
[...] Read more.
The world we live in is an increasingly spatial and temporal data-rich environment, and agriculture is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of ‘Precision Agriculture’ and ‘Smart Agriculture’ are and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source software called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data, (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields, and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. These case studies were applied to three contrasting crop types, banana, wheat and vineyards. These were chosen to highlight the diversity of applications and data characteristics that might be handled with GeoFIS. For each case-study, up-to-date algorithms arising from research studies and implemented in GeoFIS were used to process these precision agriculture data. Areas for future development and possible relations with existing geographic information systems (GIS) software is also discussed. Full article
(This article belongs to the Special Issue Precision Agriculture)
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Open AccessArticle Precision for Smallholder Farmers: A Small-Scale-Tailored Variable Rate Fertilizer Application Kit
Agriculture 2018, 8(4), 48; https://doi.org/10.3390/agriculture8040048
Received: 9 February 2018 / Revised: 13 March 2018 / Accepted: 20 March 2018 / Published: 24 March 2018
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Abstract
Precision agriculture technology at the hands of smallholder farmers in the developing world is often deemed far-fetched. Low-resource farmers, however, are the most susceptible to negative changes in the environment. Providing these farmers with the right tools to mitigate adversity and to gain
[...] Read more.
Precision agriculture technology at the hands of smallholder farmers in the developing world is often deemed far-fetched. Low-resource farmers, however, are the most susceptible to negative changes in the environment. Providing these farmers with the right tools to mitigate adversity and to gain greater control of the production process could unlock their potential and support rural communities to meet the increasing global food demand. In this study, a real-time variable rate fertilizer application system was developed and tested as an add-on kit to conventional farm machinery. In the context of low investment costs for smallholder farmers, high user-friendliness and easy installment were the main concerns for the system to be viable. The system used nitrogen (N)-sensors to assess the plant nutrient status on the spot and subsequently adjust the amount of fertilizer deposited according to the plant’s needs. Test bench trials showed that the add-on kit performed well with basic operations, but more precision is required. Variability between N-sensors and metering systems, combined with power fluctuations, created inaccuracies in the resulting application rate. Nevertheless, this work is a stepping stone towards catalyzing the elaboration of more cutting-edge precision solutions to support small-scale farmers to become successful, high producing agro-entrepreneurs. Full article
(This article belongs to the Special Issue Precision Agriculture)
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