Editing AI in Agriculture (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Precision agriculture''' — Using data, sensors, and AI to optimize farming decisions at fine spatial and temporal resolution.
* '''NDVI (Normalized Difference Vegetation Index)''' — A satellite/drone derived index measuring vegetation health from near-infrared and red reflectance.
* '''Remote sensing''' — Using satellite or aerial sensors to monitor crop health, soil moisture, and land use.
* '''Crop disease detection''' — Using computer vision to identify plant diseases from leaf images.
* '''Yield prediction''' — Forecasting the harvest volume of a crop before harvest using ML on weather, soil, and satellite data.
* '''Irrigation scheduling''' — Using AI to determine optimal irrigation timing and volume to maximize water efficiency.
* '''Soil moisture sensors''' — Ground sensors measuring soil water content; input to precision irrigation AI.
* '''Variable rate application''' — Applying different amounts of inputs (fertilizer, pesticide, water) to different zones based on AI recommendations.
* '''Digital twin (agriculture)''' — A computational model of a farm that simulates crop growth and response to interventions.
* '''Crop phenotyping''' — Measuring plant traits (height, leaf area, fruit size) at scale using computer vision.
* '''Livestock monitoring''' — Using computer vision and wearable sensors to monitor animal health, behavior, and productivity.
* '''AgriTech''' — Technology companies applying AI and data science to agricultural challenges.
* '''Food loss and waste''' — Post-harvest losses (spoilage, quality rejection) reduced by AI quality inspection systems.
* '''Climate-smart agriculture''' — Farming practices that reduce emissions, increase resilience to climate change, and improve food security.
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