Abstract:
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[EN] Very often, the root of problems found to produce food sustainably, as well as the origin of
many environmental issues, derive from making decisions with unreliable or inexistent data. Datadriven
agriculture has emerged ...[+]
[EN] Very often, the root of problems found to produce food sustainably, as well as the origin of
many environmental issues, derive from making decisions with unreliable or inexistent data. Datadriven
agriculture has emerged as a way to palliate the lack of meaningful information when taking
critical steps in the field. However, many decisive parameters still require manual measurements
and proximity to the target, which results in the typical undersampling that impedes statistical
significance and the application of AI techniques that rely on massive data. To invert this trend, and
simultaneously combine crop proximity with massive sampling, a sensing architecture for automating
crop scouting from ground vehicles is proposed. At present, there are no clear guidelines of how
monitoring vehicles must be configured for optimally tracking crop parameters at high resolution.
This paper structures the architecture for such vehicles in four subsystems, examines the most
common components for each subsystem, and delves into their interactions for an efficient delivery
of high-density field data from initial acquisition to final recommendation. Its main advantages
rest on the real time generation of crop maps that blend the global positioning of canopy location,
some of their agronomical traits, and the precise monitoring of the ambient conditions surrounding
such canopies. As a use case, the envisioned architecture was embodied in an autonomous robot to
automatically sort two harvesting zones of a commercial vineyard to produce two wines of dissimilar
characteristics. The information contained in the maps delivered by the robot may help growers
systematically apply differential harvesting, evidencing the suitability of the proposed architecture
for massive monitoring and subsequent data-driven actuation. While many crop parameters still
cannot be measured non-invasively, the availability of novel sensors is continually growing; to benefit
from them, an efficient and trustable sensing architecture becomes indispensable.
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