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Agronomy
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Spatially-Based Services and Applications in Precision Farming: From Data to...
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Spatially-Based Services and Applications in Precision Farming: From Data to Field Information

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 6703

Special Issue Editor

Prof. Dr. Enrico Corrado Borgogno Mondino

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, Italy
Interests: remote sensing; spatial analysis and landscape planning; GIS; digital photogrammetry; precision farming; lidar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on “Spatially Based Services and Applications in Precision Farming: From Data to Field Information” is intended to assemble high-level contributions providing an exhaustive overview of the ongoing geomatics-related technology transfer into the agricultural sector. Contributions from authors should not report simple case studies but more properly highlight opportunities, limitations, and criticalities still persisting in this context, with special concern about actual and potential services to farmers.

The following themes are warmly encouraged:

  • Design and implementation of institutional services for agriculture (controls and management) based on satellite/aerial/UAV data with special concerns about image time series and monitoring instances (EO Browser, OneSoil, CropMoitoring-EoS, etc.);
  • Technical criticalities/limitations/potentialities and possible solutions related to the integration of new low-cost ground sensors (e.g., agro-meteorological sensors networks) with remotely sensed imagery to provide operational services for farmers;
  • Remote sensing data processing for agronomic information retrieval (both qualitative and quantitative). Works presenting procedures to validate prescription maps and criteria definition to derive reliable intensity rates of agronomic interventions from RS data are encouraged;
  • Economical evaluations concerning costs of RS, actual, and forecasted income improvements in the agriculture sector, potential market of web-based services;
  • Artificial-Intelligence-based platforms supporting spatial applications in agriculture; DIAS, HPC and IOT in agriculture;
  • Standardization of processes and outputs.

All other issues related to the adoption of RS in the agriculture sector will be evaluated, as well.

Prof. Dr. Enrico Corrado Borgogno Mondino
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 submissions that pass pre-check are 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 2600 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

  • services
  • satellite imagery
  • copernicus
  • crop monitoring
  • artificial intelligence
  • sensor networks
  • multispectral imagery
  • digital photogrammetry

Published Papers (3 papers)

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Research

17 pages, 7051 KiB  
Article
UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments
by Dimitrios Katikaridis, Vasileios Moysiadis, Naoum Tsolakis, Patrizia Busato, Dimitrios Kateris, Simon Pearson, Claus Grøn Sørensen and Dionysis Bochtis
Agronomy 2022, 12(8), 1937; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12081937 - 17 Aug 2022
Cited by 9 | Viewed by 1888
Abstract
Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for [...] Read more.
Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field’s tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts’ orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery. Full article
(This article belongs to the Special Issue Spatially-Based Services and Applications in Precision Farming: From Data to Field Information)
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18 pages, 22145 KiB  
Article
An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies
by Abhishesh Pal, Gautham Das, Marc Hanheide, Antonio Candea Leite and Pål Johan From
Agronomy 2022, 12(6), 1299; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12061299 - 29 May 2022
Cited by 2 | Viewed by 1810
Abstract
Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can [...] Read more.
Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions. Full article
(This article belongs to the Special Issue Spatially-Based Services and Applications in Precision Farming: From Data to Field Information)
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20 pages, 5234 KiB  
Article
The Importance of Agronomic Knowledge for Crop Detection by Sentinel-2 in the CAP Controls Framework: A Possible Rule-Based Classification Approach
by Filippo Sarvia, Samuele De Petris, Federica Ghilardi, Elena Xausa, Gianluca Cantamessa and Enrico Borgogno-Mondino
Agronomy 2022, 12(5), 1228; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12051228 - 20 May 2022
Cited by 5 | Viewed by 2121
Abstract
Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness [...] Read more.
Farmers are supported by European Union (EU) through contributions related to the common agricultural policy (CAP). To obtain grants, farmers have to apply every year according to the national/regional procedure that, presently, relies on the Geo-Spatial Aid Application (GSAA). To ensure the properness of applications, national/regional payment agencies (PA) operate random controls through in-field surveys. EU regulation n. 809/2014 has introduced a new approach to CAP controls based on Copernicus Sentinel-2 (S2) data. These are expected to better address PA checks on the field, suggesting eventual inconsistencies between satellite-based deductions and farmers’ declarations. Within this framework, this work proposed a hierarchical (HI) approach to the classification of crops (soya, corn, wheat, rice, and meadow) explicitly aimed at supporting CAP controls in agriculture, with special concerns about the Piemonte Region (NW Italy) agricultural situation. To demonstrate the effectiveness of the proposed approach, a comparison is made between HI and other, more ordinary approaches. In particular, two algorithms were considered as references: the minimum distance (MD) and the random forest (RF). Tests were operated in a study area located in the southern part of the Vercelli province (Piemonte), which is mainly devoted to agriculture. Training and validation steps were performed for all the classification approaches (HI, MD, RF) using the same ground data. MD and RF were based on S2-derived NDVI image time series (TS) for the 2020 year. Differently, HI was built according to a rule-based approach developing according to the following steps: (a) TS standard deviation analysis in the time domain for meadows mapping; (b) MD classification of winter part of TS in the time domain for wheat detection; (c) MD classification of summer part of TS in the time domain for corn classification; (d) selection of a proper summer multi-spectral image (SMSI) useful for separating rice from soya with MD operated in the spectral domain. To separate crops of interest from other classes, MD-based classifications belonging to HI were thresholded by Otsu’s method. Overall accuracy for MD, RF, and HI were found to be 63%, 80%, and 89%, respectively. It is worth remarking that thanks to the SMSI-based approach of HI, a significant improvement was obtained in soya and rice classification. Full article
(This article belongs to the Special Issue Spatially-Based Services and Applications in Precision Farming: From Data to Field Information)
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