Missouri drones and machine learning bring precision nitrogen decisions
Researchers in Missouri are showing how drones and artificial intelligence can make farming smarter and more efficient. Instead of slow, handheld measurements, the team surveyed mid-Missouri corn fields using drones equipped with special cameras. These cameras capture wavelengths such as near-infrared and red-edge light—signals the human eye cannot see but that closely track plant health.
By analyzing these images with machine learning and combining them with soil information, the scientists accurately estimated chlorophyll in corn leaves across entire fields. Chlorophyll is a key indicator of crop health and nitrogen status. Knowing chlorophyll levels helps farmers choose when to apply fertilizer, where to apply it, and how much to use.
Nitrogen is essential for corn growth, yet it is expensive and can harm the environment if applied in excess. Too little nitrogen limits yield; too much raises costs and increases the risk of nutrient losses to air and water. With field-wide chlorophyll maps generated quickly from drone data, farmers can apply nitrogen at the right time, in the right amount, and in the right location.
The project was led by doctoral student Fengkai Tian in the lab of Associate Professor Jianfeng Zhou at the University of Missouri’s College of Agriculture, Food and Natural Resources. Zhou, who co-directs research for Mizzou’s Digital Agriculture Research and Extension Center, said the goal is to improve agricultural efficiency with innovative tools. The study demonstrates how precision agriculture can deliver practical, timely decisions that raise yields while reducing chemical use.
The research, conducted with the USDA Agricultural Research Service, appears in Smart Agricultural Technology under the title “Estimating corn leaf chlorophyll content using airborne multispectral imagery and machine learning.” While the study focused on corn, the same approach can help assess crops such as soybean and wheat. Farmers may eventually work with ag-technology providers to fly drones and process data, making advanced analytics accessible without requiring farmers to become experts in imaging or AI.
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