Special Issue "Smart Decision-Making Systems for Precision Agriculture"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Jaume Arnó
E-Mail Website
Guest Editor
Research Group in AgroICT & Precision Agriculture, University of Lleida, 25198 Lleida, Spain
Interests: precision agriculture; decision support systems; data analysis; geostatistics; sensors and monitoring; sampling in agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José A. Martínez-Casasnovas
E-Mail Website
Guest Editor
Research Group in AgroICT & Precision Agriculture, University of Lleida, Agrotecnio-CERCA Center, 25198 Lleida, Spain
Interests: precision agriculture; remote sensing; digital soil mapping; spatial data analysis; site-specific crop management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision Agriculture (PA) is, conceptually, an agricultural management strategy that aims to improve resource efficiency, productivity, and quality based on a better understanding of the spatial and temporal variability that is usually inherent in many farms. Acquisition of spatial and temporal data on soils and crops is the starting point of the process. For that, farmers usually have a wide variety of proximal and remote sensors, in addition to global navigation satellite systems to georeference all these data. However, moving from sensor data to piece of information, intermediate stages must be addressed in order to make the best agronomic decision, which is still a bottleneck in the real implementation of PA. Thus, data processing, mapping, data analysis, delineation of management zones or prescription map creation are steps to be solved before using variable-rate devices installed on agricultural equipment. Making the right decision at the right time to apply the right amount of inputs is an important issue on which the economy and sustainability of agricultural production ultimately depends. In this respect, the digital revolution in agriculture will only be feasible through the development of smart, compact and easy-to-use systems that, adapted to the needs of farmers, help in decision-making and to run management tools despite the complexity of these new technologies.

This Special Issue aims to contribute to the dissemination of new research findings related to (i) data processing and data analysis in the spatial and temporal domains, (ii) decision support, (iii) delineation of potential management zones, (iv) agricultural internet of things (IoT), (v) Agro Big Data, and (vi) efficient sampling methods in forecasting tasks.

Dr. Jaume Arnó
Prof. José A. Martínez-Casasnovas
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. Agronomy 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 2000 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
  • post-process data analysis
  • real-time data processing
  • smart sampling
  • Internet of Things (IoT)
  • decision making
  • smart farming

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
An Agent-Based Crop Model Framework for Heterogeneous Soils
Agronomy 2021, 11(1), 85; https://doi.org/10.3390/agronomy11010085 - 04 Jan 2021
Viewed by 745
Abstract
Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these [...] Read more.
Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Article
Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management
Agronomy 2020, 10(11), 1762; https://doi.org/10.3390/agronomy10111762 - 12 Nov 2020
Cited by 2 | Viewed by 826
Abstract
Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show [...] Read more.
Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show different spectral and morphological signatures have not been investigated much. This study investigated and compared the performance of models trained using aerial imagery collected at two growth stages of winter wheat with different canopy phenotypes. Specifically, three CNN-based models were trained with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. Results show that the multi-stage model trained by images from both growth stages outperformed the models trained by images from individual growth stages on all testing data. The mean accuracy of the multi-stage model was 89.23% for both growth stages, while the mean of the other two models were 52.32% and 84.9%, respectively. This study demonstrates the importance of diversity of training data in big data analytics, and the feasibility of developing a universal decision support system for wheat lodging detection and mapping multi-growth stages with high-resolution remote sensing imagery. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Article
Assessing the Sensitivity of Site-Specific Lime and Gypsum Recommendations to Soil Sampling Techniques and Spatial Density of Data Collection in Australian Agriculture: A Pedometric Approach
Agronomy 2020, 10(11), 1676; https://doi.org/10.3390/agronomy10111676 - 29 Oct 2020
Cited by 6 | Viewed by 731
Abstract
There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has [...] Read more.
There is currently limited understanding surrounding the spatial accuracy of soil amelioration advice as a function of sampling density at the sub-field scale. Consequently, soil-based decisions are often made using a data limiting approach, as the value proposition of soil data collection has not been well described. The work presented here investigates the spatial errors of gypsum and lime recommendations based on industry-standard blanket-rate and zone-based variable rate application, as well as the more advanced pedometric approaches – ordinary kriging (OK) and regression kriging (RK). All methods were tested at sampling densities between 0.1–3 samples/ha for a 108 ha broadacre site in central NSW, Australia. Whilst previous work has tested the effect of sampling density on the spatial predictive performance of OK and RK, here we assess prediction accuracy as the error associated with soil management decisions based on their results (i.e., the over- and under-application error of gypsum and lime applications) in conjunction with the RMSE of prediction for soil pH and exchangeable sodium percentage (ESP). The uncertainty of each method is also tested to observe the effect of random initialisation on predictive performance. Results indicated that RK provided superior spatial predictions across all sampling densities for the application of gypsum and lime, with a blanket-rate application providing the worse results, with over- and under-application errors exceeding 200 t and 300 t respectively for 40–60 cm treatment for the entire field. Interestingly, the spatial accuracy of amendment application increased to a sampling density of 0.5 samples/ha for RK, with minimal improvement thereafter, suggesting that meaningful soil amelioration advice can be attained proximal to this density. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Article
A Joint Decision-Making Approach for Tomato Picking and Distribution Considering Postharvest Maturity
Agronomy 2020, 10(9), 1330; https://doi.org/10.3390/agronomy10091330 - 04 Sep 2020
Cited by 1 | Viewed by 904
Abstract
Fruit maturity is an essential factor for fresh retailers to make economical distribution scheduling and scientific market strategies. In the context of farm-to-door mode, the fresh retailers could incorporate the postharvest maturity time, picking time and distribution time to deliver high-quality fruits to [...] Read more.
Fruit maturity is an essential factor for fresh retailers to make economical distribution scheduling and scientific market strategies. In the context of farm-to-door mode, the fresh retailers could incorporate the postharvest maturity time, picking time and distribution time to deliver high-quality fruits to consumers. This study selects climacteric tomato fruits and formulates a postharvest maturity model by capturing the firmness and soluble solid content (SSC) data during maturing. A joint picking and distribution model is proposed to ensure tomatoes could arrive at consumers within expected maturity time windows. To improve the feasibility of proposed model, an improved genetic algorithm (IGA) is designed to obtain solutions. The results demonstrate that the joint model could optimize the distribution routing to improve consumer satisfaction and reduce the order fulfillment costs. The proposed method provides precise guidance for tomato online retailers by taking advantage of postharvest maturity data, which is conducive to sustainable development of fresh e-ecommerce. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Article
Fruit Morphological Measurement Based on Three-Dimensional Reconstruction
Agronomy 2020, 10(4), 455; https://doi.org/10.3390/agronomy10040455 - 25 Mar 2020
Cited by 6 | Viewed by 1285
Abstract
Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences [...] Read more.
Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Review

Jump to: Research

Review
Data Lifecycle Management in Precision Agriculture Supported by Information and Communication Technology
Agronomy 2020, 10(11), 1648; https://doi.org/10.3390/agronomy10111648 - 26 Oct 2020
Cited by 4 | Viewed by 1176
Abstract
The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need [...] Read more.
The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variability of crops. Information and communication technology (ICT) systems have influenced and shaped every part of modern life, and PA is no exception. The current paper conducts a literature review of prominent ICT solutions, focusing on their role in supporting different phases of the lifecycle of PA-related data. In addition to this, a data lifecycle model was developed as part of a novel categorization approach for the analyzed solutions. Full article
(This article belongs to the Special Issue Smart Decision-Making Systems for Precision Agriculture)
Show Figures

Figure 1

Back to TopTop