Following the machines, people and ideas shaping agriculture. Selected developments, clear context and a link to the original source.
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463 stories matching “AI”.
University of Florida, USDA and NASA researchers used hyperspectral imaging to identify early drought stress in lettuce, supporting development of non-destructive monitoring methods for greenhouses and other tightly controlled growing environments.
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.
UC Merced researchers are developing materials that absorb ultraviolet light and emit visible wavelengths, exploring whether molecular design can improve greenhouse light management under the Central Valley’s demanding growing conditions.
UC Merced has introduced mobile greenhouse and agricultural-technology trailers to bring practical demonstrations of plant production, sensing, drones and automation to schools, communities and agricultural workplaces across the Central Valley.
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 field trials tested a camera-guided push-cart sprayer for green weeds in dormant bermudagrass, reporting lower herbicide volume than broadcast treatment while exposing the importance of detection conditions and field variability.
A University of Maryland farm partnership is using buried moisture sensors and phone alerts to guide irrigation, with growers reporting postponed watering and energy savings while final harvest outcomes remained unmeasured.
Researchers from the University of Virginia, NASA and the University at Buffalo tested drone sensing at Morven, pairing aerial measurements with field observations to evaluate methods for monitoring plant stress and productivity.
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.
A Kennesaw State student project combines environmental sensors and local image analysis to monitor crops with limited connectivity. Early testing classified visible lettuce disease; earlier warning remains a development goal.
University of Texas at Austin researchers are developing FarmBrain, a system that keeps immediate robot reactions onboard while sharing more demanding AI processing through nearby computing and private wireless networks.
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.
