By Jamie Martin
A University of Missouri team has shown how drones and artificial intelligence can quickly assess corn's health on a scale. Handheld sampling is slow in large fields, so researchers flew drones equipped with special cameras over mid-Missouri corn. These sensors capture bands like near-infrared and red-edge light, which plants reflect in ways that closely relate to chlorophyll—an important indicator of nitrogen status.
The team processed the images with machine-learning models and combined them with soil information to estimate leaf chlorophyll across the entire field with strong accuracy. These chlorophyll maps transform decision-making. Farmers can identify where nitrogen is needed most, plan in-season applications, and avoid over-application that wastes money and risks environmental harm.
Corn requires significant nitrogen, yet the ideal amount changes with weather, soils, and plant growth. Too little nitrogen stunts plants; too much can leach or volatilize. By delivering rapid, field-wide chlorophyll estimates, drone analytics help achieve “right time, right place, right rate” fertilization—boosting yield potential while controlling costs.
The study was led by doctoral student Fengkai Tian in the lab of Associate Professor Jianfeng Zhou at Mizzou’s College of Agriculture, Food and Natural Resources. Zhou also serves as co-director of research for the Digital Agriculture Research and Extension Center. Their work, published in Smart Agricultural Technology, was completed by scientists from the USDA Agricultural Research Service.
Beyond corn, the same approach can inform management in crops such as soybean and wheat, which have different nitrogen needs. Looking ahead, farmers may contract with ag-tech firms to handle flights and data processing. This makes advanced analytics available to producers without new equipment or technical training, bringing precision agriculture within reach of more operations and supporting higher productivity with a smaller environmental footprint.
Photo Credit: istock-psisa
Categories: National