To what extent can current French mobile network support agricultural robots?

TL;DR

The paper develops a bottom-up mobile-network model to quantify how large-scale agricultural robots affect energy use and greenhouse gas emissions in metropolitan France. By modeling sites, sectors, cells, bandwidth, and robot workloads, it estimates how much agricultural area can be managed under network constraints and evaluates incremental energy and embodied footprints for existing and upgraded networks. The results show that higher bitrate requirements can cause disproportionate energy and carbon impacts and reduce the usable area, while upgrades raise total footprints due to equipment renewal; naive extrapolations fail to capture these effects. The work highlights critical infrastructure considerations for digital agriculture and suggests future directions, including multi-operator networks, site expansion, and integration with empirical measurements. Overall, the study provides a quantitative framework to assess sustainability trade-offs of deploying data-intensive agricultural robotics at scale.

Abstract

The large-scale integration of robots in agriculture offers many promises for enhancing sustainability and increasing food production. The numerous applications of agricultural robots rely on the transmission of data via mobile network, with the amount of data depending on the services offered by the robots and the level of on-board technology. Nevertheless, infrastructure required to deploy these robots, as well as the related energy and environmental consequences, appear overlooked in the digital agriculture literature. In this study, we propose a method for assessing the additional energy consumption and carbon footprint induced by a large-scale deployment of agricultural robots. Our method also estimates the share of agricultural area that can be managed by the deployed robots with respect to network infrastructure constraints. We have applied this method to metropolitan France mobile network and agricultural parcels for five different robotic scenarios. Our results show that increasing the robot's bitrate needs leads to significant additional impacts, which increase at a pace that is poorly captured by classical linear extrapolation methods. When constraining the network to the existing sites, increased bitrate needs also comes with a rapidly decreasing manageable agricultural area.

Figures (5)

• Figure 1: Graphical representation of a site $x$ with three sectors $s_1, s_2, s_3$ and their related cells. It illustrates possibly used cells for each sector among low, lower-mid (LM) and upper-mid (UM) frequency bands. The propagation radius $\gamma_{b}$ of a cell in the band $b$ corresponds to the diameter of the cell. The $\pmb{c}_{s,b}$ points denote the guessed centers of their respective cells (only three out of six are shown).
• Figure 2: General overview of our assessment process. The Network selection and Upgrade module simulates two robots deployments. 1: based on the initial network; 2: based on an upgraded network version. Three types of agricultural areas are distinguished. Manageable: managed by robots, as the area is covered by mobile network with a sufficient bandwidth; Non-manageable: not managed by robots, as it is covered by mobile network but remaining bandwidth is not sufficient; Non-covered: not covered by mobile network.
• Figure 3: Zoomed agricultural area covered by frequency bands of the mobile network given by the UAA cover model. Selected UAA encompasses cereals, protein crops, oil-seeds, vines, forage and vegetables. Agricultural area displayed in yellow is covered by low, area displayed in orange is covered by low and lower-mid, area displayed in red is covered by low, lower-mid and upper-mid. Agricultural area in grey is not covered.
• Figure 4: Additional carbon footprint for RTK, Stream and Edge scenarios when varying the number of passes annually done by robots, between 6, 12 and 18.
• Figure 5: Number of cells per hectare, additional carbon footprint per hectare and part of UAA managed and deployed robots for RTK, Stream and Edge scenarios when jointly varying by 20% the spectral efficiency and propagation radius of frequency bands. The pessimistic variation is denoted as ($-$), optimistic variation as ($+$) and the initial set of parameters as ($=$).