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# A collaborative and low cost acquisition chain for collecting spatialized observations in agriculture

S. Moinard* , B. Oger, G. Brunel, A. Ducanche, T. Crestey, P. Faure, L. Pichon, B. Tisseyre

ITAP, Univ Montpellier, INRAE, Institut Agro, 2 Place Pierre Viala 34060 Montpellier

ARTICLE INFO

Keywords:

Centipede
GNSS RTK
Open source
Smartphone
Spatial data

A B S T R A C T

This technical note describes a new data acquisition chain for real-time collection and sharing of georeferenced observations made manually and recorded on a form using a smartphone for agriculture projects. This acquisition chain relies on a low-cost Real-Time Kinematics (RTK) Global Navigation Satellite System (GNSS) rover and the open source Centipede network to obtain corrected geolocation with centimeter accuracy on any Android smartphone device. The Mergin Maps application, which completes this acquisition chain, provides versatility making it possible to design custom projects to meet a large diversity of applications in agriculture (collection of quantitative or qualitative data, images, polygons etc.). The precision provided by these technologies can also be easily combined with existing tools for collaborative data collection. The observations made through this application can be shared in real time with other users involved in the project, even remotely. The simplicity of use of this acquisition chain paves the way for ambitious data acquisition campaigns in agriculture which could involve a large number of participants with different profiles and/or take place over a wide variety of scales (field, domain, cooperative, region, etc.). Two case-studies, which led to the collection of several thousand observations, are presented to demonstrate the potential of this acquisition chain. The first case study deals with mapping missing and dead vines on a 35 ha vineyard, it highlights the flexibility of the acquisition chain and the convenience for sharing data. The second case study highlights the collaborative potential of the acquisition chain with more than thirty participants simultaneously involved in a one day project to make observations of vineyard yields performed at the field level.

# 1. Introduction

Many projects or applications require the collection of spatialized observations by operators in agriculture. Collecting and sharing accurate spatial observations is critical in agriculture, especially in high value crops, to improve management practices, to better understand crop production and to maximizing yields among other applications. However, depending on the expected spatial precision needed to locate observations, data collection systems can be expensive and complex. This can make it difficult for many stakeholders in the agricultural sector to access high quality geo-located data. In recent years, new data acquisition tools have been developed to support the collection of specific types of observations at different scales (within-field zones, fields, farms, watersheds, regional, etc.). These include, for example among many others, agricultural applications (agri-apps) to monitor nitrogen content (Delgado et al., 2013) or water stress in vineyards (Pichon et al., 2021) with mobile devices, particularly with smartphones. These agri-

apps have quickly become invaluable for on-farm monitoring and experimentation.

However, these data collections with mobile devices depend on the quality of the Global Navigation Satellite System (GNSS) technology embedded in the mobile device to geo-locate observations. The accuracy of these systems has greatly increased since their creation but is still not good enough in mobile devices for some agriculture situations. This is particularly the case for high-value perennial crops, when it is necessary, for experiment purposes, to locate a specific plant or a specific part of a plant in connection with any study of the spatial development of a specific disease (Thomas et al., 2002), or for example, to compare remote sensing information at high spatial resolution with precise ground observations (Gómez-Candón et al., 2017). Regarding growers, many observations need to be accurately positioned (missing vines, trellising issues, drippers problems, etc.) in order to be registered for subsequent operations. For these applications, precision in the order of the decimeter or even the centimetre is required. This precision is not

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: simon.moinard@supagro.fr (S. Moinard).</footnote>
available with current GNSS chips embedded in smartphones, which
limits their interest for these applications.

GNSS technology based on Real-Time Kinematic (RTK) are now able to provide accuracies in the centimetre range through different solutions. In particular, low-cost GNSS RTK systems have become popular due to their affordability and accessibility, especially with the development of low-cost GNSS receivers, like those based on the u-blox chipsets (Liu and Li, 2017). However, compared to traditional high-end RTK GNSS systems, there are several limitations associated with low-cost alternatives (Hill et al., 2019). The main problem is that many low-cost GNSS receivers are single-frequency (e.g., L1) that can affect its performance in point positioning (Tsakiri et al., 2017), whereas traditional systems often use multi-frequency (e.g., L1, L2, L5) that provide faster and more accurate solutions. In addition, these low-cost GNSS receivers mainly based on single frequency have greater sensitivity to some effects, such as ionospheric delays, signal obstruction, multipath and atmospheric effects (Elsheikh et al., 2023). Hence, RTK coupled with a complex algorithm is often implemented in order to cope with these problems and to improve position accuracy (Xiang et al., 2016). This RTK correction applied on a single frequency receiver provides an accuracy of about 2 cm (Odolinski and Teunissen, 2017). However, when the dual or multi-frequency is integrated on this same receiver, centimetric accuracy (~1 cm) can be achieved in real time (Nguyen et al., 2021).

Today, the accuracy of these different technologies seems promising in agriculture (Pini et al., 2020; Kowalczyk and Hadas, 2024) and recent works underline its potential to collect data with high spatial accuracy (Madugundu et al., 2022). However, GNSS-RTK remains expensive and its adoption by farmers or advisors is still very limited. In the last few years, several low-cost alternatives provided by companies (Valente et al., 2020) or open-source projects (Ancelin et al., 2022a) have begun to be developed. However, these options are quite new and remain underexploited in agriculture.

In addition to geo-localisation issues, the adoption of many agri-apps is often limited by the apps being overly specific (i.e. focussed only on a specific type of data collection) and this lack of versatility makes them difficult to use in different or alternative use cases. The inability to easily observe spatial agricultural data in real time is a real barrier to the use of mobile devices for recording data, especially in situations that require collaborative acquisition efforts across multiple operators. These issues of specificity and data management/sharing are generic issues and many of them can be solved by externalising the acquisition chain using open-source software solutions for data-sharing. However, while the components exist for a collaborative acquisition chain solution for collecting and sharing spatialized observations in agriculture, a system to provide an affordable solution for reliable, traceable and accurate data collection by operators in agriculture at different spatial scales has not previously been proposed. This work echoes recent efforts to integrate precision agriculture tools into education, such as the initiative presented by Ploteau et al. (2023), which focuses on teaching digital technologies through field-based data acquisition using low-cost tools.

Therefore, the objective of this technical note is to propose a collaborative and low-cost acquisition chain for collecting and sharing spatialized observations in agriculture. This acquisition chain was implemented and tested with a GNSS-RTK receiver in a commercial vineyard in the South of France. The first use case was the mapping of dead and missing vines at farm level by a single operator. The second use case was the mapping of bunch weight at within-the field level simultaneously by several operators.

## 2. Description of the global acquisition chain

The acquisition chain has been designed for geo-locating spatial observations with centimetre precision. It relies mainly on open source projects/software and low cost devices/sensors to minimise cost and maximise the openness (flexibility) of the chain. Its originality is its

ability to allow several operators to work on the same project and to share collected, precisely geo-located data in quasi real time while still in the field. Fig. 1 presents the different functional modules of the proposed acquisition chain that are described hereafter.

### 2.1. Rover RTK.

The external RTK rover (Fig. 1.1) was built using an F9P RTK module and a DA910 dual-frequency antenna (both from Drotek, France). The correction signal, received by the smartphone, is transmitted via Bluetooth using a BT-05 module (AZ-Delivery, Germany), and the corrected position is then sent back to the smartphone through the same Bluetooth link. The entire system is powered by a 5000 mAh external battery (Adafruit, USA) and enclosed in a 3D-printed case (Ancelin et al., 2022b). The total cost of the system was under €400. The assembled rover is shown in Fig. 2.

### 2.2. Correction signal.

The correction signal (Fig. 1.2) is provided by the nearest base station of the Centipede network (Ancelin et al., 2022a), an open-source and collaborative system of over 300 home-made RTK bases across France (as of 2022). The correction stream is transmitted via the internet from the Centipede server to the smartphone and then to the RTK rover. This free network enables centimetre-level positioning across most of France wherever mobile internet is available, and is now expanding internationally (Ancelin et al., 2022b).

### 2.3. Smartphone.

The system currently works only on Android devices, as some key components are Android-specific and have no equivalents on other platforms. The smartphone plays three main roles in the acquisition chain: (i) it manages communication between modules via mobile network or Bluetooth, (ii) it runs dedicated applications for data exchange and formatting, and (iii) it serves as a user interface for entering and visualizing observations across the territory (Fig. 1.3). The Lefebure application (Lefebure Design, USA) plays a key role in GNSS-RTK positioning: (i) it handles the RTK correction signal and transmits it to the rover, (ii) it receives the centimetre-corrected positions and formats them for spatial data applications, and (iii) it overrides the smartphone's internal GNSS to use the corrected RTK position by default (Ancelin et al., 2022b).

### 2.4. Geospatial infrastructure.

The geospatial infrastructure was designed to support collaborative data collection and visualization across a wide range of agricultural projects. It manages two key types of data: (i) the location of observations (e.g. the coordinates of a vine plant), provided by the low-cost GNSS-RTK system, and (ii) the associated attributes (e.g. the weight of grape bunches). Its main features include customizable observation forms, real-time map visualization, and synchronization of all user data into a centralized database for live progress tracking. To meet these needs, the open-source Mergin Maps application (Lutra Consulting, UK) was selected for its simplicity and flexibility. Observation data are stored in a geopackage file hosted on Mergin Cloud (Fig. 1.4), enabling access from any internet-connected device. Each user can: (i) download the project, (ii) collect data, (iii) view their own and others' data, and (iv) sync with the cloud to share and update observations in near real time.

The main interface of a Mergin project (Fig. 3.1) shows the locations of previously recorded data, the current position of the RTK rover, and the estimated positioning accuracy (e.g., 0.01 m). To record new data, users configure an attribute form in the Mergin Maps app by selecting the desired fields (text, number, date, photos, etc.). Pressing the record button starts a new observation and opens the form (Fig. 3.2), which can
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** The acquisition chain consists of four functional parts: 1) an external RTK Rover that receives the signal from the GNSS satellites and computes the location from the correction stream sent by the smartphone (it replaces the internal GNSS chip signal), 2) an internet-enabled correction signal from the Centipede network of ground-based GNSS-RTK antennas (bases), 3) a smartphone with a specific application (Lefebure) that receives the correction signal and sends it to the external RTK Rover (via Bluetooth) and then receives the corrected RTK-GNSS position from the rover and Mergin Maps which then links with, 4) a cloud-based geospatial infrastructure (Mergin Cloud), to enable storage, sharing, visualization and analysis of the collected observations.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Assembled RTK Rover with 1) the dual frequency Antenna, 2) RTK Geolocalisation module, 3) Bluetooth module, 4) Power Bank, and 5) 3D-printed case. RTK Rover was connected via Bluetooth to 6) the Smartphone.</figcaption></figure>

then be filled in manually. Data are saved locally in a geopackage file (Fig. 1.4) and uploaded to the Mergin Cloud via the sync function. The cloud platform handles versioning and multi-user access. All geospatial data can also be downloaded and processed in GIS software such as QGIS (QGIS Association, Switzerland).

### 3. Example applications

This section presents two case studies conducted in a commercial vineyard in southern France (Villeneuve-lès-Maguelone, 43.5323,

3.8642, WGS84), using the GNSS-RTK acquisition chain. The correction signal was provided by a Centipede base station located about 1 km from the site, allowing for an expected accuracy of 1–5 cm, which decreases with distance to the base (Ancelin et al., 2022b). The case studies were designed to demonstrate the operational benefits of the system across varying contexts—data types, number of users, user expertise, duration, and data sharing. Both focus on high-value crops where precise vine-level positioning is essential. While illustrative, these examples show that the system can also be applied with other GNSS receivers, with or without RTK correction.

#### 3.1. *Case study 1: Mapping dead and missing vines over the entire vine estate.*

The first case study, conducted between May and October 2022, involved exhaustive mapping of dead and missing vines across the 35 ha vineyard. A single expert user carried out the data collection over five months using a GNSS-RTK-connected smartphone. Observations were manually entered into a single data layer, with an attribute field specifying whether each point corresponded to a “Dead vine” or “Missing vine.” Fig. 4 illustrates the data collected over a 7 ha section, where the user recorded each affected vine by walking along all vine rows.

Progress was shared with other project members in real time via Mergin Maps, which facilitated discussions and early feedback on the data. Within the first weeks, the form was updated to include a third category, “Anchors,” allowing the user to also map trellis anchor points. In total, over 16,000 observations were collected during approximately twenty days of fieldwork, carried out in full or half-day sessions depending on availability. A portable battery was used to power the smartphone on longer days, while the RTK rover’s external battery provided autonomy for over eight hours.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Screenshots of the Mergin Maps application interface. 1) Project overview displaying previously collected observations (orange points) and the current RTK position (blue point) provided by the Lefebure app; here, each point corresponds to a vine plant. 2) Attribute form used to enter a new observation; in this example, each observation includes six values representing the weight of six grape bunches per vine. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Example of observations acquired on one of the blocks during the first case study on the mapping of missing and dead vines. Coordinates are in Lambert93 (EPSG 2154). The zoom on the right allows to observe the alignment of the measurement points along the vine row.</figcaption></figure>

## 3.2. Case study 2: Bunch weight acquisition at within-field level during harvest.

The second case study was conducted on September 9, 2022, during the harvest of one of the estate's fields. The goal was to precisely map the weight of each grape bunch. This required recording both the position of each productive vine and the weight of each individual bunch. In total, over 21,000 bunches were weighed across 733 vines. The Mergin project used a single data layer to locate the vines, with each vine associated with 100 numerical fields (Fig. 3.2) for manually entering bunch weights during harvest.

Due to harvest constraints, the data acquisition campaign had to be completed in a single day. Thirty participants were divided into ten groups, each equipped with an RTK rover. Within each group, tasks were split between harvesting, weighing, and data entry on smartphones. A

1.5-hour training session was sufficient to teach participants how to connect their devices to the rover, use Mergin Maps, and join the project. System accuracy was assessed on a reference site with known coordinates, where each operator recorded three geolocations. Despite the variety of Android smartphones used, the system performed reliably, with all position errors under 10 cm (mean absolute error: 3 cm; standard deviation: 1.40 cm).

The synchronization system enabled real-time monitoring of data collection. At each sync, groups uploaded their own data and received updates from others, allowing all participants to view the collective progress on their smartphones. This facilitated identification of already sampled vines and helped plan the remaining work. A remote user, added to the project, could also manage the campaign via the web interface—assigning teams to specific rows and tracking their progress. Overall, very few errors occurred. Most were due to manual entry
mistakes when recording bunch weights. Only five vines were incor-
rectly geolocated, likely due to internet connection issues or challenging
acquisition conditions (e.g., canopy or trellis interference). These errors
were easily identified as they fell outside vine rows. The dataset from
this case study is publicly available in a companion Data in Brief article
(Oger et al., 2023).

4. Discussion

The case-studies presented in this technical note show how the GNSS-RTK acquisition chain can match with a wide variety of contexts to collect and share high-precision spatialized information in quasi real time and in a collaborative way. The rapid uptake (short training and low error rate in case study 2) by non-expert users shows that the skills required to use this acquisition chain are easy to master. The ability to have large groups of people trained quickly for agri-data collection is invaluable. The project can be scheduled without time constraints and edited at will thanks to the flexibility of the acquisition chain. The total cost of the centipede rover is ~€350 (excluding access to a 3D printer for the casing), which remains low-cost for this type of technology. For all these reasons, the GNSS-RTK acquisition chain is easily deployable and examples presented here only represent a simple overview of its potential. If in the case presented, this acquisition chain was used in exploitation for research purposes, it could find many applications for teams composed of a great diversity of field agents such as groups of farmers, technical institutes, cooperatives, disease observation networks, etc.

One limitation remains the construction of the Rover RTK which requires some skills to implement despite the tutorials available (Ancelin et al, 2022b). The components listed in this article are accessible via dedicated websites and specialized distributors worldwide. However, promoting the dissemination of such a device means supporting practitioners in the development of the skills required to build the Rover RTK as proposed. One solution we recommend is to offer self-construction workshops in the form of lifelong learning trainings for advisors and farmers. In this respect, two one-day training courses for 10 people each (advisors/farmers) have already been carried out by the authors of this publication (RTK training, 2023). Ideally, these training courses should be dedicated to people likely to accompany other farmers or other advisors in the construction of their own rover, which opens the way to new skills for technicians and advisers.

Some limitations can affect the use of the GNSS-RTK acquisition chain. First, communication between the RTK rover and RTK location correction stream (Centipede in our case) rely on the internet mobile network, disconnections may occur depending on the network coverage where the data are collected. It is therefore recommended to regularly check that the GNSS accuracy is sufficient (≈0.01 m) via the Mergin Maps interface (Fig. 3.1). Second, both the RTK rover and the smartphone autonomy are dependent on their battery. For the RTK rover, a 5000 mA battery is enough to run the rover for more than 8 h, the smartphone battery life being usually the most limiting factor. In the cases studied, the autonomy of smartphones was generally between 4 and 5 h because of the screen and the connection to bluetooth and internet networks. Third, the Mergin Maps application is quite intuitive and easy to use, while Lefebure can be more difficult to handle. The installation tutorial (Ancelin et al. 2022b) gives detailed methodology to customize the smartphone location but unreferenced problems can be difficult to debug for novice users. Finally, the Mergin Maps application is free for research, teaching or personal use but requires a subscription to be used for commercial purposes.

This technical note presents a first, stable version of the GNSS-RTK
acquisition chain for use in the context of authors for research experi-
mentations. It is possible to adapt this acquisition chain with different
variations depending on the objectives, available resources and local
conditions. The Mergin Maps application can for example be used
without GNSS-RTK technology when a location accuracy of a few meters

is sufficient. In this case, the data acquisition chain is simply based on
the GNSS chip of the smartphone (Fig. 1). It is also possible to manage
the Mergin project only using a smartphone. Indeed, the QGIS software
and the Mergin web interface were respectively used for project creation
and project access management but alternative options are available on
the mobile application. Finally, the high precision positioning of the
smartphone provided by the GNSS-RTK centipede and the Lefebure
application can be used in any other application requiring location co-
ordinates. Other data collection methods can thus be added to fit the
operational needs of the users. The Qfield application (QField, 2023), for
example, offers a completely open-source and free alternative to Mergin
Maps, but it is more difficult to set up. Also, as most of the method is
based on open source technologies, the GNSS-RTK acquisition chain
remains fully customizable by any user.

5. Conclusion

This paper demonstrates the effectiveness of a low-cost GNSS-RTK
acquisition chain for the collaborative collection and sharing of spa-
tialized observations in the agricultural sector. It shows that this
acquisition chain can provide reliable data at an affordable cost, which
can be beneficial to producers and researchers dealing with spatial ob-
servations acquisition at different scales. The case study examples pre-
sented in this technical note highlight the versatility and accessibility of
the method. It is thus possible to include a large number of participants
in a data acquisition project who can all follow the progress in almost
real time, even remotely. The GNSS-RTK acquisition chain thus opens up
new opportunities for large-scale spatial data collection in other sectors.

CRediT authorship contribution statement

**S. Moinard:** Formal analysis, Conceptualization, Writing – review & editing, Writing – original draft, Validation, Software, Methodology. **B. Oger:** Writing – original draft, Validation, Supervision, Methodology, Funding acquisition, Formal analysis, Data curation. **G. Brunel:** Writing – review & editing, Writing – original draft, Supervision, Project administration. **A. Ducanche:** Writing – review & editing, Investigation, Conceptualization. **T. Crestey:** Resources, Project administration, Software. **P. Faure:** Funding acquisition, Data curation, Project administration, Software, Resources. **L. Pichon:** Writing – review & editing, Project administration. **B. Tisseyre:** Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

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