Abstract
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. Data-driven 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.
Highlights
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
Tributaries and streams throughout the Mississippi river basin, for instance, are suffering from an overload of nutrients due to excessive use of fertilizers [1], and, in Valencia (Spain), the water pumped from some irrigation wells has such a high concentration of nitrate that agronomists advise against the use of fertilizers containing nitrogen; by taking soil and water data before fertilizing, soil health deterioration can be avoided in the long run
Despite the multiple benefits brought by data-driven agriculture, its practical implementation has been undermined by the difficulty of getting consistent data at the right periodicity, with the necessary precision, and with a minimum spatial resolution to apply statistics, geostatistics, and artificial intelligence techniques
Summary
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. 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. The availability of practical tools allowing the methodical recording of field data sets the basis for reaching long-term profit and financial stability over maximum earnings in a single vintage This financial stability is key for assuring economical sustainability, whereas the systematic monitoring of water status allows the rational use of irrigation water, which leads to environmental sustainability, the two pillars upon which data-driven agriculture is founded. This fact, prevents the systematic use of these techniques at a large scale for cost-efficiency reasons
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