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Special Issue "Precision Agriculture Technologies for a Sustainable Future: Current Trends and Perspectives"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture, Food and Wildlife".

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Konstantinos G. Arvanitis

Director of the Section of Farm Structures & Agricultural Machinery, Laboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Agricultural Production, Engineering and Environment, Agricultural University of Athens, 75 Iera Odos Str., Botanikos 11855, Athens, Greece
Website | E-Mail
Phone: +30-210-5294034
Interests: automation and ICT in agriculture; wireless sensor networks; precision farming; management and control of autonomous smart grids; remote sensing
Guest Editor
Dr. Spyros Fountas

Laboratory of Agricultural Machinery, Department of Natural Resources Management and Agricultural Engineering, School of Agricultural Production, Engineering and Environment, Agricultural University of Athens, 75 Iera Odos Str., Botanikos 11855, Athens, Greece
Website | E-Mail
Phone: +30-210-5294035
Interests: precision agriculture; farm machinery; information systems

Special Issue Information

Dear Colleagues,

Precision Agriculture is a management concept focusing on monitoring, measurement and responses to inter- and intra-variability in crops, fields and animals. Precision Agriculture has been made possible by the rapid development of sensing technologies, management information systems, advances in farm machinery and appropriate agronomic and economic models. The benefits of using Precision Agriculture practices include increasing crop yields and animal performance, cost reduction and optimization of process inputs. Thus, Precision Agriculture aims to reduce the environmental impacts of agriculture and farming practices, contributing to the sustainability of agricultural production.

This Special Issue aims to discuss various impacts of precision agriculture technologies and management methods from a sustainability viewpoint. This will include the impact of precision agriculture technologies and applications to the three pillars of sustainability: economic, environmental and social perspectives. We invite you to contribute to this issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods, technological tools and innovatively statistical analyses, in order to provide an opportunity for learning the state-of-the-art and for discussion on future directions in Precision and Sustainable Agriculture. Papers selected for this Special Issue are subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Prof. Dr. Konstantinos G. Arvanitis
Dr. Spyros Fountas
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. Sustainability 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 1400 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

  • Spatio-Temporal Variability in Crop, Soil and Natural Resources
  • Big Data Mining and Statistical Issues in Precision Agriculture
  • Information Systems in Precision Agriculture and Decision Support
  • Engineering Technologies and Advances in Precision Agriculture
  • Computational Intelligence in Precision Agriculture
  • Proximal Sensing in Precision Agriculture
  • Remote Sensing Applications in Precision Agriculture
  • Variable-Rate Application Equipment in Precision Agriculture
  • Food Security and Precision Agriculture
  • Precision Agriculture and Climate Change
  • Precision Conservation Management
  • Precision Crop Protection
  • Precision Horticulture
  • Precision Dairy and Livestock Management
  • Precision Nutrient and Water Management
  • Autonomous Vehicles (Ground and Aerial) for Sustainable Agriculture
  • Profitability, Sustainability and Adoption Trends
  • Education and Training in Precision Agriculture
  • Emerging Issues in Precision Agriculture (Energy, Life Cycle Analysis, Carbon and Water Footprints, etc.)

Published Papers (7 papers)

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Research

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Open AccessArticle Can Precision Agriculture Increase the Profitability and Sustainability of the Production of Potatoes and Olives?
Sustainability 2017, 9(10), 1863; doi:10.3390/su9101863
Received: 24 September 2017 / Revised: 10 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
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Abstract
For farmers, the application of Precision Agriculture (PA) technology is expected to lead to an increase in profitability. For society, PA is expected to lead to increased sustainability. The objective of this paper is to determine for a number of common PA practices
[...] Read more.
For farmers, the application of Precision Agriculture (PA) technology is expected to lead to an increase in profitability. For society, PA is expected to lead to increased sustainability. The objective of this paper is to determine for a number of common PA practices how much they increase profitability and sustainability. For potato production in The Netherlands, we considered variable rate application (VRA) of soil herbicide, fungicide for late blight control, sidedress N, and haulm killing herbicide. For olive production in Greece, we considered spatially variable application of P and K fertilizer and lime. For each of the above scenarios, we quantified the value of outputs, the cost of inputs, and the environmental costs. This allowed us to calculate profit as well as social profit, where the latter is defined as revenues minus conventional costs minus the external costs of production. Social profit can be considered an overall measure of sustainability. Our calculations show that PA in potatoes increases profit by 21% (420 € ha−1) and social profit by 26%. In olives, VRA application of P, K, and lime leads to a strong reduction in nutrient use and although this leads to an increase in sustainability, it has only a small effect on profit and on social profit. In conclusion, PA increases sustainability in olives and both profitability and sustainability in potatoes. Full article
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Open AccessArticle Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features
Sustainability 2017, 9(8), 1335; doi:10.3390/su9081335
Received: 12 June 2017 / Revised: 26 July 2017 / Accepted: 26 July 2017 / Published: 4 August 2017
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Abstract
Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of
[...] Read more.
Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection. Full article
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Open AccessArticle Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps
Sustainability 2017, 9(7), 1241; doi:10.3390/su9071241
Received: 25 April 2017 / Revised: 26 June 2017 / Accepted: 11 July 2017 / Published: 15 July 2017
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Abstract
One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a
[...] Read more.
One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%. Full article
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Review

Jump to: Research

Open AccessReview Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics
Sustainability 2017, 9(8), 1339; doi:10.3390/su9081339
Received: 22 June 2017 / Accepted: 16 July 2017 / Published: 31 July 2017
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Abstract
Agriculture is one of the economic sectors that affect climate change contributing to greenhouse gas emissions directly and indirectly. There is a trend of agricultural greenhouse gas emissions reduction, but any practice in this direction should not affect negatively farm productivity and economics
[...] Read more.
Agriculture is one of the economic sectors that affect climate change contributing to greenhouse gas emissions directly and indirectly. There is a trend of agricultural greenhouse gas emissions reduction, but any practice in this direction should not affect negatively farm productivity and economics because this would limit its implementation, due to the high global food and feed demand and the competitive environment in this sector. Precision agriculture practices using high-tech equipment has the ability to reduce agricultural inputs by site-specific applications, as it better target inputs to spatial and temporal needs of the fields, which can result in lower greenhouse gas emissions. Precision agriculture can also have a positive impact on farm productivity and economics, as it provides higher or equal yields with lower production cost than conventional practices. In this work, precision agriculture technologies that have the potential to mitigate greenhouse gas emissions are presented providing a short description of the technology and the impacts that have been reported in literature on greenhouse gases reduction and the associated impacts on farm productivity and economics. The technologies presented span all agricultural practices, including variable rate sowing/planting, fertilizing, spraying, weeding and irrigation. Full article
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Open AccessReview The Potential of Animal By-Products in Food Systems: Production, Prospects and Challenges
Sustainability 2017, 9(7), 1089; doi:10.3390/su9071089
Received: 9 May 2017 / Revised: 7 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
PDF Full-text (940 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The consumption of animal by-products has continued to witness tremendous growth over the last decade. This is due to its potential to combat protein malnutrition and food insecurity in many countries. Shortly after slaughter, animal by-products are separated into edible or inedible parts.
[...] Read more.
The consumption of animal by-products has continued to witness tremendous growth over the last decade. This is due to its potential to combat protein malnutrition and food insecurity in many countries. Shortly after slaughter, animal by-products are separated into edible or inedible parts. The edible part accounts for 55% of the production while the remaining part is regarded as inedible by-products (IEBPs). These IEBPs can be re-processed into sustainable products for agricultural and industrial uses. The efficient utilization of animal by-products can alleviate the prevailing cost and scarcity of feed materials, which have high competition between animals and humans. This will also aid in reducing environmental pollution in the society. In this regard, proper utilization of animal by-products such as rumen digesta can result in cheaper feed, reduction in competition and lower cost of production. Over the years, the utilization of animal by-products such as rumen digesta as feed in livestock feed has been successfully carried out without any adverse effect on the animals. However, there are emerging gaps that need to be further addressed regarding the food security and sustainability of the products. Therefore, the objective of this review highlights the efficacy and effectiveness of using animal by-products as alternative sources of feed ingredients, and the constraints associated with their production to boost livestock performance in the industry at large. Full article
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Open AccessReview iPathology: Robotic Applications and Management of Plants and Plant Diseases
Sustainability 2017, 9(6), 1010; doi:10.3390/su9061010
Received: 13 March 2017 / Revised: 8 June 2017 / Accepted: 9 June 2017 / Published: 12 June 2017
Cited by 1 | PDF Full-text (249 KB) | HTML Full-text | XML Full-text
Abstract
The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in
[...] Read more.
The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management. Full article
Open AccessReview Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation
Sustainability 2017, 9(3), 353; doi:10.3390/su9030353
Received: 21 November 2016 / Revised: 2 February 2017 / Accepted: 15 February 2017 / Published: 28 February 2017
PDF Full-text (735 KB) | HTML Full-text | XML Full-text
Abstract
Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water
[...] Read more.
Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Life Cycle Assessment of Two Vineyards after the Application of Precision Viticulture Techniques
Author: A.T.Balafoutis1, S. Koundouras2, E. Anastasiou1, S. Fountas1, K.G. Arvanitis1
Affiliations: 1Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, IeraOdos 75, 11855, Athens, Greece
2Laboratory of Viticulture, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Abstract: Precision viticulture is the application of site-specific techniques to vineyard production to improve grape quality and yield and minimize the negative effects on the environment. While there are various studies on the inherent spatial and temporal variability of vineyards, and techniques and methods to optimize resource utilization, the assessment of the environmental impact of variable rate applications has attracted limited attention. In this study, two vineyards planted with different grapevine varieties (Sauvignon blanc and Syrah) were managed with precision viticulture techniques during two growing seasons (2013 and 2014) and the environmental impact in terms of energy and greenhouse gas emissions was quantified. The first year, the vineyards were studied in terms of soil properties and crop characteristics, which provided the delineation of two distinct management zones for each vineyard.The second year, variable rate fertilization was applied to each management zone based on leaf nutrient analysis and leaf canopy reflectance, while variable rate irrigation was based on soil moisture sensors, meteorological data and evapotranspiration calculation. Life Cycle Assessment (LCA) was carried out to identify the effect of the application of variable rate applications on vineyard environment. The results indicated that nutrient status management offers the greatest potential for reducing environmental impact in both vineyards.Variable rate irrigation also showed differences in comparison to conventional irrigation treatment, but to a lesser degree than variable rate fertilization, mostly due to the fact that 2014 was a wet season and a limited number of water treatments was carried out.With respects to different vineyard varieties, precision viticulture could have a high environmental impact on Sauvignon blanc, while the situation was less significant, but not negligible, in the Syrah vineyard. This difference between the two varieties wasmainly attributed to the significant fertilizer quantity reduction in Sauvignon blanc in comparison to Syrah.

 

Title: Precision Farming and Advanced ICT Solutions are Required to Make Agricultural Buildings more Sustainable
Authors: T. Norton1, T. Bartzanas2, D. Berckmans1
Affiliations: 1 Katholieke Universiteit Leuven, Division Animal & Human Health Engineering, M3-BIORES, Kasteelpark Arenberg 30, Leuven, Belgium
2 Center for Research and Technology – Hellas, Institute for Research and Technology of Thessaly, Dimitriados 95, 38333, Volos, Greece
Abstract: As the demands from a growing global population increase, modern ICT-based solutions will become mainstream in the support sustainable building-based horticultural and livestock farming practices. In livestock farming, the evolution of Precision Livestock Farming (PLF) has seen new systems for the automatic monitoring and management of production practices being investigated and brought to the market. These solutions need to be implemented within livestock buildings, and they replace the eyes and ears of the producers and can potentially optimise the process while farmers are not present. Recent research has shown how PLF solutions can improve the sustainability of livestock buildings with respect to precision ventilation, precision health and welfare monitoring, as well as precision waste management. In protected horticulture, Precision Farming (PF) is now becoming a greater focus for research and technical implementation. Advanced climate, water and nutrient control systems are being developed to tune the microclimate to the continuous needs of the plants, taking plant signals as the input to control systems. Recent ICT-based solutions are also being used to develop technologies to integrate the production of algae and fish, with this shown great potential in this sector. The aim of this paper is to review the recent PF and PLF solutions that contribute to sustainable agricultural buildings. The final message will be that PF/PLF and making advanced ICT is key to agricultural buildings sustainable into the future, but that these solutions must integrate the response of animals and crops to their environment to achieve this objective.

 

Title: Exploring Precision Farming Scenarios using Dynamic Fuzzy Cognitive Maps for Decision Support
Authors: Asmaa Mourhir1, Elpiniki I. Papageorgiou2,*, K. Aggelopoulou3, K. Kokkinos4, Tajjeeddine Rachidi1 and G.D. Nanos3
Affiliations: 1 Computer Science Department, School of Science and Engineering, Al Akhawayn University in Ifrane, Morocco, A.Mourhir@aui.ma, T.Rachidi@aui.ma
2Computer Engineering Department, Technological Education Institute (TEI) of Central Greece, Greece, epapageorgiou@teiste.gr
3University of Thessaly, Department of Agriculture, Crop Production & Rural Environment, Fytoko Street, N. Ionia, Magnesia, 38446, Greece, aggelop@agr.uth.gr
4Computer Science Department, University of Thessaly, Lamia, Greece, email: kokkinos@uth.gr
Abstract: One of the most challenging problems confronted in precision farming is the exact knowledge of how the yield on a certain area responds to diminishing or marginal application of specific nutrients. Dynamic Rule-based Fuzzy Cognitive Maps (DRBFCMs) is a Fuzzy Cognitive Map variant that allows modeling of dynamic causal maps where, influence weights are determined dynamically at simulation time using fuzzy inference systems, in order to adapt to new conditions. In this work we propose the DRBFCM soft computing technique that: (i) implements yield prediction and hence enables managers to analyze yield variations across a field thus, they can determine how to vary the management options, and (ii) provide managers with a tool to perform simulations in precision farming that can be used to identify the effects of fertilizer or pesticide quantity variations on yield. Experts from the precision agriculture domain have been pooled to provide the necessary knowledge and design the proposed FCM model. An illustrative precision farming example is used to predict and experimentally verify the impact of certain management options on yield according to expert perceptions using proactive scenarios.
Keywords: fuzzy cognitive maps, fuzzy inference systems, Dynamic Rule-based Fuzzy Cognitive Maps, yield prediction, scenarios.

 

Title: Automated Navigation for Field Machinery: An Assessment on the Energy Savings
Authors: Dionysis Bochtis1; Remigio Berruto2; Patrizia Busato2; Efthimis Rodias2; Kun Zhou3
Affiliations: 1 Institute for Research and Technology - Thessaly IRETETH / Centre for Research & Technology Hellas –CERTH, Dimitriados Str. 95, GR 38333, Volos, Greece
2 University of Turin, Faculty of Agriculture, DISAFA Department Via Leonardo da Vinci 44, 10095, Grugliasco, Turin, Italy
3 Aarhus University, Faculty Science and Technology, Dept. of Engineering, Inge Lehmanss Gade 10, 8000 Aarhus C, Denmark
Abstract: Conventional filed area coverage is based on routes followed by agricultural vehicles which are formed as repetitions of standard motifs. This practice is not based on any kind of optimization necessities, in terms of any criterion, e.g. cost, energy savings, or soil compaction prevention, but rather on the human operator factor. A human operator lacks the ability to distinguish the field-work tracks on a morphologically uniform field area. The implementation of sensor systems, such as geographical positioning systems and machine vision systems, for navigation adding of agricultural vehicles, combined with on-board decision making systems and interfaces has removed the dependence of the field area coverage planning and execution from human capabilities. These automation technologies has enforced the potential for the generation of optimized field area coverage practices, such as the B-patterns based coverage where the sequence of the field-work tracks for a complete area coverage are algorithmically-computed under one or more selected criteria. It has been shown that the implementation of B-patterns result to savings in non-working distance up to and 58%, increases in area capacity up to 19%, and reduction of the soil compaction probability up to 60%. In this paper, a methodology for the generation of B-patterns under the criterion of energy consumption is presented. The energy consumption of agricultural vehicles during field operation is compared between the cases of the B-patterns generated routes and the conventional ones. Based on the results, the optimized routes reduces the energy consumption of agricultural vehicles up to 9%.

 

Title: Operational estimation of soil moisture, evapotranspiration and related parameters for agricultural crops in support of sustainable water management: state of the art and future outlook
Authors: Maria Piles1, Prashant K. Srivastava2, David Chaparro3, Kiril Manevski4, George P. Petropoulos5, Hywel Griffiths6
Affiliations: 1Barcelona Expert Centre, Institute of Marine Sciences (ICM), CSIC, Pg. Marítim Barceloneta 37-49, E-08003 Barcelona, Spain
2 National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Noida, India
3Universitat Politècnica de Catalunya, IEEC/UPC, Jordi Girona 1-3, E-08034 Barcelona, Spain
4Aarhus University, Tjele, Denmark
5Aberystwyth University, Geography & Earth Sciences, UK

 

Title: Quantifying the Effect of Precision Agriculture Using an Integrated Measure of Sustainability
Authors: Frits K. Van Evert1, Daniel Gaitan-Cremaschi2, Spyros Fountas3, Peter M. Kyveryga4, Corné Kempenaar1
Affiliations: 1 Wageningen University and Research Center, Wageningen Plant Research, Agrosystems Research, PO Box 16, 6700AA, Wageningen, The Netherlands
2 Wageningen University and Research Center, Wageningen School of Social Sciences, Leeuwenborch, building number 201, 6700AA, Wageningen, The Netherlands
3 Agricultural University of Athens, Natural Resources Management and Agricultural Engineering, Iera Odos 75, 11855 Athens, Greece
4 Iowa Soybean Association, 1255 SW Prairie Trail Pkwy, Ankeny, Iowa 50023, USA
Abstract: Precision Agriculture (PA) is the scientific domain that deals with management of spatial and temporal variability to improve economic returns and reduce environmental impact. For farmers, PA is expected to lead to an increase in profitability. For society, PA is expected to lead to increased sustainability. The objective of this paper is to rank a number of common PA practices in terms of how much they increase profitability and sustainability. For potato production in The Netherlands, we considered variable rate application (VRA) of lime and sidedress application of N. For olive production in Greece, we considered spatially variable application of P and K fertilizer. For maize/soybean production in the mid-Western United States, we considered VRA of N, as well as skipping areas with consistently low yields (so-called potholes). For each of the above scenarios, we quantified the change in inputs, outputs, and the environmental impact resulting from the application of the PA practice. Production data were obtained from commercial growers where possible and otherwise from experiments. Environmental impacts considered were GHG (on-farm energy use and embedded energy of inputs), nitrogen losses, and toxicity effects of pesticides. Local prices were used to determine the value of inputs and outputs. We used various methods to determine shadow prices for environmental impacts. This allowed us to calculate social profit, which is defined as revenues minus conventional costs minus the external costs of production – social profit can be considered an overall measure of sustainability.

 

Title: Low-Power Wide-Range Wireless Communication Technologies for Sustainable Precision Agriculture at a Regional Level
Authors: Dimitrios D. Piromalis 1,2, *, Konstantinos G. Arvanitis 1 , Stelios Mitilineos, 3 Panagiotis G. Papageorgas3
Affiliations: 1 Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
2 Department of Automation Engineering, Piraeus University of Applied Sciences (TEI of Piraeus), P. Ralli and Thivon 250, 12244, Egaleo,Greece
3 Department of Electronics Engineering, Piraeus University of Applied Sciences (TEU of Piraeus), P. Ralli and Thivon 250, 12244, Egaleo, Greece
Abstract: In a fast changing and competitive technological environment, Wireless Sensors Networks systems and applications are seriously affected. This paper focuses on the revolutionary technologies for long range communications and presents how the post-mesh era of wireless networks can help sustainable precision agriculture. Specifically, ultra narrow-band and low-power Wide-Area-Networks, such as the LoRa and Sigfox, etc., can support the transition from local to regional systems and applications. Moreover, this paper presents the implementation of experiments in laboratory environment regarding the measurements of throughput and packet error rate versus signal strength. In particular, the experiment is conducted using 868 MHz LoRa and ZigBee based nodes. Several useful conclusions regarding to the potential strengths of new communication technologies for low-power Wide-Area-Networks are emphatically presented in the last part of the paper.
Keywords: Low-Power WAN, LoRa, Sigfox, ZigBee, Precision Agriculture, Long-Range, Narrow-Band Communications

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