Special Issue "Agricultural Route Planning and Feasibility"

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

Deadline for manuscript submissions: closed (15 December 2019).

Special Issue Editor

Dr. Claus G. Sørensen
E-Mail Website
Guest Editor
Aarhus University, Faculty of Science and Technology, Department of Engineering, Finlandsgade 22, 8200 Århus N, Denmark
Interests: operations and production management; information and communication technology; smart farming, system analyses; sustainability of innovative technologies
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is moving into an area of smart farming, where advanced data management, intelligent machines, and autonomous vehicles are working together to enable system optimization, support decision-making in real-time, smart analyses and planning, etc. Currently, information and communication technologies provide the potential for these advancements, but traditional operations management must be supplemented with new planning features, such as advanced route planning, to take full advantage. Route planning systems must be able to cope with multi-objective criteria, such as operations efficiency, adverse effects on soil disturbance and compaction, dynamic optimization in real-time, fast algorithms, etc.

In this Special Issue, we are open to contributions (research papers and a limited number of reviews) exploring the development and advancement of route planning systems for smart farming including agri-food supply chains. It includes both deterministic and dynamic route planning systems, combined planning for co-operating machines and vehicles, planning with multiple criteria (operational efficiency, field readiness, workability, soil compaction, etc.), and planning systems for both manually operated machines and autonomous vehicles. Integrated route planning systems with digital farming concepts (connection with Internet of Things (IoT) technologies providing automatic input/output for the planning systems, integration with scheduling and mission planning, etc.), and the estimation of cost-benefit are also welcome.

Dr. Claus G. Sørensen
Guest Editor

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 1800 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

  • Vehicle routing
  • Path planning
  • Field efficiency
  • Optimization
  • Area coverage planning
  • Field logistics
  • Fleet management

Published Papers (7 papers)

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

Research

Open AccessArticle
Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study
Agronomy 2020, 10(10), 1608; https://doi.org/10.3390/agronomy10101608 - 20 Oct 2020
Viewed by 456
Abstract
Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the [...] Read more.
Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
An Arable Field for Benchmarking of Metaheuristic Algorithms for Capacitated Coverage Path Planning Problems
Agronomy 2020, 10(10), 1454; https://doi.org/10.3390/agronomy10101454 - 23 Sep 2020
Cited by 1 | Viewed by 566
Abstract
This study specifies an agricultural field (Latitude = 56°30′0.8″ N, Longitude = 9°35′27.88″ E) and provides the absolute optimal route for covering that field. The calculated absolute optimal solution for this field can be used as the basis for benchmarking of metaheuristic algorithms [...] Read more.
This study specifies an agricultural field (Latitude = 56°30′0.8″ N, Longitude = 9°35′27.88″ E) and provides the absolute optimal route for covering that field. The calculated absolute optimal solution for this field can be used as the basis for benchmarking of metaheuristic algorithms used for finding the most efficient route in the field. The problem of finding the most efficient route that covers a field can be formulated as a Traveling Salesman Problem (TSP), which is an NP-hard problem. This means that the optimal solution is infeasible to calculate, except for very small fields. Therefore, a range of metaheuristic methods has been developed that provide a near-optimal solution to a TSP in a “reasonable” time. The main challenge with metaheuristic methods is that the quality of the solutions can normally not be compared to the absolute optimal solution since this “ground truth” value is unknown. Even though the selected benchmarking field requires only eight tracks, the solution space consists of more than 1.3 billion solutions. In this study, the absolute optimal solution for the capacitated coverage path planning problem was determined by calculating the non-working distance of the entire solution space and determining the solution with the shortest non-working distance. This was done for four scenarios consisting of low/high bin capacity and short/long distance between field and storage depot. For each scenario, the absolute optimal solution and its associated cost value (minimum non-working distance) were compared to the solutions of two metaheuristic algorithms; Simulated Annealing Algorithm (SAA) and Ant Colony Optimization (ACO). The benchmarking showed that neither algorithm could find the optimal solution for all scenarios, but they found near-optimal solutions, with only up to 6 pct increasing non-working distance. SAA performed better than ACO, concerning quality, stability, and execution time. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
Decision Support Tool for Operational Planning of Field Operations
Agronomy 2020, 10(2), 229; https://doi.org/10.3390/agronomy10020229 - 04 Feb 2020
Cited by 2 | Viewed by 840
Abstract
Precision Farming (PF) and Controlled Traffic Farming (CTF) are well known concepts within agriculture, but the adoption rate of these practices by farmers is still very low, because farmers lack the needed skills or fail to see the benefits of using these practices. [...] Read more.
Precision Farming (PF) and Controlled Traffic Farming (CTF) are well known concepts within agriculture, but the adoption rate of these practices by farmers is still very low, because farmers lack the needed skills or fail to see the benefits of using these practices. If farmers want to reap the full benefits, operational planning must be carried out in advance for the entire crop cycle, before the crop season begins. However, operational planning across the entire crop cycle is a non-trivial task, since the efficiency of each operation is determined by a range of selected operational features (e.g., wayline direction, operational speed, vehicle capacity, wayline sequence, and turn type). To that end, we present, in this paper, an application that can support farmers with operational planning of field operations with CTF, by automating the process. It provides the farmer with an overview of all his field operations, and acts as a decision support tool during the operational planning process. The application allows farmers to store and manage field and equipment information, which is used as input, when setting up CTF and generating way lines and route plans for the individual fields. One of the key benefits of the application is the provided comparison feature, where farmers can compare alternative solutions, based on Key Performance Indicators (KPIs). Results from an example field, for operations with different machine setups, are presented to illustrate how KPIs and visualisations can support farmers during the decision process. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
Metric Map Generation for Autonomous Field Operations
Agronomy 2020, 10(1), 83; https://doi.org/10.3390/agronomy10010083 - 07 Jan 2020
Cited by 3 | Viewed by 990
Abstract
Advanced systems for manned and/or agricultural vehicles—such as systems for auto-steering, navigation-adding, and autonomous route planning—require new capabilities in terms of the internal representation for the autonomous system of the working space; that is, the generation of a metric map that provides by [...] Read more.
Advanced systems for manned and/or agricultural vehicles—such as systems for auto-steering, navigation-adding, and autonomous route planning—require new capabilities in terms of the internal representation for the autonomous system of the working space; that is, the generation of a metric map that provides by numerical parameters any operation-related entity of the working space. In this paper, a real-time approach was developed for the generation of the field metric map, based on a row generation method (polygons-based geometry). The approach can deal with fields with or without in-field obstacles, where the generated field-work tracks can be either straight or curved. The functionality of the approach was demonstrated on 12 fields with different number of obstacles ranging from one to six. The test results showed that the computational times were in the range of 0.26–24.51 s. The presented tool brings a number of advancements on the process of generating a metric map for arable farming field operations, including the real-time generation feature, the potential to deal with multiple-obstacle areas, and the reduction in the overlapped area. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
Price Forecasting and Span Commercialization Opportunities for Mexican Agricultural Products
Agronomy 2019, 9(12), 826; https://doi.org/10.3390/agronomy9120826 - 01 Dec 2019
Viewed by 1101
Abstract
Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology [...] Read more.
Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
Integrated Harvest and Farm-to-Door Distribution Scheduling with Postharvest Quality Deterioration for Vegetable Online Retailing
Agronomy 2019, 9(11), 724; https://doi.org/10.3390/agronomy9110724 - 07 Nov 2019
Viewed by 663
Abstract
With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture [...] Read more.
With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture the perishable property of vegetables, and model it as a quadratic postharvest quality deterioration function. Then, we incorporate the postharvest quality deterioration function into the integrated harvest and farm-to-door distribution scheduling and formulate it as a quadratic vehicle routing programming model with time windows. Next, we propose a genetic algorithm with adaptive operators (GAAO) to solve the model. Finally, we carry out numerical experiments to verify the performance of the proposed model and algorithm, and report the results of numerical experiments and sensitivity analyses. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Open AccessArticle
AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics
Agronomy 2019, 9(7), 403; https://doi.org/10.3390/agronomy9070403 - 20 Jul 2019
Cited by 8 | Viewed by 2720
Abstract
This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies [...] Read more.
This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies investigating agricultural robotic systems with regard to: (i) the analysis approach, i.e., simulation, emulation, real-world implementation; (ii) farming operations; and (iii) the farming type. Our analysis demonstrates that simulation and emulation modelling have been extensively applied to study advanced agricultural machinery while the majority of the extant research efforts focuses on harvesting/picking/mowing and fertilizing/spraying activities; most studies consider a generic agricultural layout. Thereafter, we developed AgROS, an emulation tool based on the Robot Operating System, which could be used for assessing the efficiency of real-world robot systems in customized fields. The AgROS allows farmers to select their actual field from a map layout, import the landscape of the field, add characteristics of the actual agricultural layout (e.g., trees, static objects), select an agricultural robot from a predefined list of commercial systems, import the selected UGV into the emulation environment, and test the robot’s performance in a quasi-real-world environment. AgROS supports farmers in the ex-ante analysis and performance evaluation of robotized precision farming operations while lays the foundations for realizing “digital twins” in agriculture. Full article
(This article belongs to the Special Issue Agricultural Route Planning and Feasibility)
Show Figures

Figure 1

Back to TopTop