Following the machines, people and ideas shaping agriculture. Selected developments, clear context and a link to the original source.
Robotics. Intelligence. Agriculture.
137 stories matching “Data”.
The CERBERUS project has started initial field trials of a variable-rate sprayer in Cyprus, testing whether a control system can translate five-dose prescription maps into orchard spray adjustments.
Two open-source models from NASA Harvest and Agmatix interpret vegetation-index time series to estimate current crop stages and separate successive growing cycles, supporting satellite-based agricultural monitoring across different crops.
The four-year TRITON programme will combine artificial intelligence, crop modelling and gene editing to accelerate breeding for climate resilience, with an emphasis on making research tools accessible internationally.
The EU-funded EVOLVE project will investigate whether signals released by wheat roots can influence beneficial microorganisms, potentially opening routes to improved nutrient use and drought resilience through future breeding.
The University of Tennessee Institute of Agriculture appointed José Peiretti to a statewide Extension role focused on evaluating the practical performance and economics of precision technologies for row-crop growers locally.
NC State’s AIRS project is developing repeatable drone data collection and analysis across agricultural research stations, with software already released and remotely initiated flights forming a further stage of development.
University of Georgia students are testing FieldLog, a Google-supported AI platform that turns spoken field notes into organised farm records, with farmer feedback guiding its development and deployment.
A University of Idaho-led research collaboration is developing harvest-integrated measurements and AI models to connect crop genetics with stalk strength, aiming to improve breeding for resistance to bending and breakage.
Texas Tech and Texas A&M AgriLife Research are developing a multi-scale framework combining crop measurements, machine learning and growth models to improve irrigation decisions in regions with constrained water supplies.
Arkansas researchers report that different sensing platforms can reveal similar crop patterns, while separate rice trials show why spray-drone flight speed must be assessed alongside deposition and actual disease control.
MATE and TERRA-COOP completed a cotton-focused project combining neural-network growth-stage recognition with fuzzy decision support, aiming to improve the timing and selection of crop-protection treatments under practical farming conditions.
ICICLE outlined connected agricultural AI demonstrations for Farm Science Review 2026, combining drone scouting, geospatial data, edge computing and tools that help users inspect labels and explore field information.
