Precision agriculture uses sensing, automation, and biological insight to tailor farm operations to field conditions. It can help farmers target inputs, monitor crops and soils, and manage labor and resource use.
Agricultural drones are uncrewed aerial vehicles used for farming tasks such as crop monitoring, mapping, and spraying.
AI crop yield prediction uses artificial intelligence methods to estimate or forecast how much a crop will produce.
This topic covers farm machines and robots that operate autonomously, including robotic systems coordinated in swarms.
Digital twins in agriculture are virtual representations of farms, crops, livestock, or agricultural systems that are updated with data to support monitoring, simulation, and decision-making.
Hydroponic automation uses automated monitoring and control to manage growing conditions and operations in hydroponic systems.
Wearable devices attached to livestock that track animals’ location or movement.
Precision biocontrol uses targeted biological methods to manage agricultural pests, while RNAi pesticides use RNA molecules to suppress essential genes in target pests or pathogens.
The use of satellite-based multispectral or hyperspectral imagery to assess crop condition, growth, or production.
Soil microbiome and carbon MRV uses soil microbial information to measure, report, and verify changes in soil carbon.
Soil moisture sensors are instruments that measure the amount or availability of water in soil.
Variable rate technology adjusts agricultural input application to local field conditions, while spot spraying targets selected plants or areas rather than spraying an entire field uniformly.
Vertical farming and controlled environment agriculture grow crops in managed indoor or greenhouse environments, often using stacked growing systems and controlled light, temperature, and humidity.
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