E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

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

Laboratory of Farm 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 1 | Website 2 | E-Mail
Phone: +302015294034
Interests: instrumentation and automation in agriculture; advanced process control; optimization techniques and computational intelligence; wireless sensor networks; ICT applications in agriculture; energy management and control of autonomous smart grids; Internet of Things and cloud computing
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 (12 papers)

View options order results:
result details:
Displaying articles 1-12
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle Life Cycle Assessment of Two Vineyards after the Application of Precision Viticulture Techniques: A Case Study
Sustainability 2017, 9(11), 1997; https://doi.org/10.3390/su9111997
Received: 12 August 2017 / Revised: 28 August 2017 / Accepted: 6 October 2017 / Published: 1 November 2017
PDF Full-text (5641 KB) | HTML Full-text | XML Full-text
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, the assessment of the
[...] Read more.
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, the assessment of the environmental impact of variable rate applications has attracted limited attention. In this study, two vineyards planted with different grapevine cultivars (Sauvignon Blanc and Syrah) were examined for four consecutive growing seasons (2013–2016). The first year, the two vineyards were only studied in terms of soil properties and crop characteristics, which resulted in the delineation of two distinct management zones for each field. For the following three years, variable rate nutrient application was applied to each management zone based on leaf canopy reflectance, where variable rate irrigation was based on soil moisture sensors, meteorological data, evapotranspiration calculation, and leaf canopy reflectance. Life cycle assessment was carried out to identify the effect of variable rate applications on vineyard agro-ecosystems. The results of variable rate nutrients and water application in the selected management zones as an average value of three growing seasons were compared to the conventional practice. It was found that the reduction of product carbon footprint (PCF) of grapes in Sauvignon Blanc between the two periods was 25% in total. Fertilizer production and distribution (direct) and application (indirect) was the most important sector of greenhouse gas (GHG) emissions reduction, accounting for 17.2%, and the within-farm energy use was the second ranked sector with 8.8% (crop residue management increase GHG emissions by 1.1%, while 0.1% GHG reduction is obtained by pesticide use). For the Syrah vineyard, where the production was less intensive, precision viticulture led to a PCF reduction of 28.3% compared to conventional production. Fertilizers contributed to this decrease by 27.6%, while within-farm energy use had an impact of 2.2% that was positive even though irrigation was increased, due to yield rise. Our results suggest that nutrient status management offers the greatest potential for reducing GHG emissions in both vineyard types. Variable rate irrigation also showed differences in comparison to conventional treatment, but to a lesser degree than variable rate fertilization. This difference between conventional practices and precision viticulture is noteworthy, and shows the potential of precision techniques to reduce the effect of viticulture on GHG emissions. Full article
Figures

Figure 1

Open AccessFeature PaperArticle Reference Evapotranspiration Retrievals from a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation
Sustainability 2017, 9(11), 1971; https://doi.org/10.3390/su9111971
Received: 18 July 2017 / Revised: 19 October 2017 / Accepted: 20 October 2017 / Published: 28 October 2017
PDF Full-text (4303 KB) | HTML Full-text | XML Full-text
Abstract
Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling
[...] Read more.
Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281) than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244) for SMD estimation. Full article
Figures

Figure 1

Open AccessArticle Energy Savings from Optimised In-Field Route Planning for Agricultural Machinery
Sustainability 2017, 9(11), 1956; https://doi.org/10.3390/su9111956
Received: 31 July 2017 / Revised: 12 October 2017 / Accepted: 22 October 2017 / Published: 27 October 2017
Cited by 1 | PDF Full-text (3022 KB) | HTML Full-text | XML Full-text
Abstract
Various types of sensors technologies, such as machine vision and global positioning system (GPS) have been implemented in navigation of agricultural vehicles. Automated navigation systems have proved the potential for the execution of optimised route plans for field area coverage. This paper presents
[...] Read more.
Various types of sensors technologies, such as machine vision and global positioning system (GPS) have been implemented in navigation of agricultural vehicles. Automated navigation systems have proved the potential for the execution of optimised route plans for field area coverage. This paper presents an assessment of the reduction of the energy requirements derived from the implementation of optimised field area coverage planning. The assessment regards the analysis of the energy requirements and the comparison between the non-optimised and optimised plans for field area coverage in the whole sequence of operations required in two different cropping systems: Miscanthus and Switchgrass production. An algorithmic approach for the simulation of the executed field operations by following both non-optimised and optimised field-work patterns was developed. As a result, the corresponding time requirements were estimated as the basis of the subsequent energy cost analysis. Based on the results, the optimised routes reduce the fuel energy consumption up to 8%, the embodied energy consumption up to 7%, and the total energy consumption from 3% up to 8%. Full article
Figures

Figure 1

Open AccessArticle Can Precision Agriculture Increase the Profitability and Sustainability of the Production of Potatoes and Olives?
Sustainability 2017, 9(10), 1863; https://doi.org/10.3390/su9101863
Received: 24 September 2017 / Revised: 10 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
Cited by 1 | PDF Full-text (788 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
Figures

Figure 1

Open AccessArticle Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features
Sustainability 2017, 9(8), 1335; https://doi.org/10.3390/su9081335
Received: 12 June 2017 / Revised: 26 July 2017 / Accepted: 26 July 2017 / Published: 4 August 2017
Cited by 2 | PDF Full-text (1320 KB) | HTML Full-text | XML Full-text
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
Figures

Graphical abstract

Open AccessArticle Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps
Sustainability 2017, 9(7), 1241; https://doi.org/10.3390/su9071241
Received: 25 April 2017 / Revised: 26 June 2017 / Accepted: 11 July 2017 / Published: 15 July 2017
Cited by 2 | PDF Full-text (4258 KB) | HTML Full-text | XML Full-text
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
Figures

Figure 1

Review

Jump to: Research

Open AccessReview Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management
Sustainability 2018, 10(1), 181; https://doi.org/10.3390/su10010181
Received: 28 November 2017 / Revised: 22 December 2017 / Accepted: 28 December 2017 / Published: 12 January 2018
Cited by 1 | PDF Full-text (3693 KB) | HTML Full-text | XML Full-text
Abstract
Global information on the spatio-temporal variation of parameters driving the Earth’s terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need
[...] Read more.
Global information on the spatio-temporal variation of parameters driving the Earth’s terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen. Full article
Figures

Figure 1

Open AccessReview A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture
Sustainability 2018, 10(1), 51; https://doi.org/10.3390/su10010051
Received: 16 November 2017 / Revised: 21 December 2017 / Accepted: 23 December 2017 / Published: 26 December 2017
PDF Full-text (800 KB) | HTML Full-text | XML Full-text
Abstract
Management of nitrogen (N) is a challenging task and several methods individually and in combination are in use to manage its efficiency. However, nitrogen use efficiency (NUE) has not been improved to a level, only 33%, as predicted by the researchers while developing
[...] Read more.
Management of nitrogen (N) is a challenging task and several methods individually and in combination are in use to manage its efficiency. However, nitrogen use efficiency (NUE) has not been improved to a level, only 33%, as predicted by the researchers while developing nitrogen management tools and methods. The primary objective of this review article is to evaluate methods and tools available to manage nitrogen. Several methods, soil testing, plant tissue testing, spectral response, fertilizer placement and timing and vegetative indexes (leaf area index, and NDVI) through drones, handheld sensors, and satellite imagery were reviewed on the subject of user-friendly and effectiveness towards NUE. No single method was found sufficient to counter the nitrogen loss. Some methods were found time consuming and unsynchronized with N uptake behavior of particular crop, for example, plant tissue testing. Use of precision agriculture tools, such as GreenSeeker, Holland Crop Circle, drone, and satellite imagery, were found better compared to conventional methods such as soil testing, but these tools can only be used when the crop is up. Therefore, N management is possible only through inseason N application methods. When 70% of the applied nitrogen is used by the crops within 25–30 days after planting, for example, corn and potatoes, it is required to apply major N rates through inseason approach and some N at planting using soil test reports. In conclusion, this article strongly advocates using two or more methods in combination when managing N. Full article
Figures

Figure 1

Open AccessReview Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics
Sustainability 2017, 9(8), 1339; https://doi.org/10.3390/su9081339
Received: 22 June 2017 / Accepted: 16 July 2017 / Published: 31 July 2017
Cited by 6 | PDF Full-text (971 KB) | HTML Full-text | XML Full-text
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
Figures

Figure 1

Open AccessReview The Potential of Animal By-Products in Food Systems: Production, Prospects and Challenges
Sustainability 2017, 9(7), 1089; https://doi.org/10.3390/su9071089
Received: 9 May 2017 / Revised: 7 June 2017 / Accepted: 16 June 2017 / Published: 22 June 2017
Cited by 4 | 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
Figures

Figure 1

Open AccessReview iPathology: Robotic Applications and Management of Plants and Plant Diseases
Sustainability 2017, 9(6), 1010; https://doi.org/10.3390/su9061010
Received: 13 March 2017 / Revised: 8 June 2017 / Accepted: 9 June 2017 / Published: 12 June 2017
Cited by 2 | 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; https://doi.org/10.3390/su9030353
Received: 21 November 2016 / Revised: 2 February 2017 / Accepted: 15 February 2017 / Published: 28 February 2017
Cited by 3 | 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
Figures

Figure 1

Back to Top