Drones offer efficient monitoring of maize re-growth

Drones offer efficient monitoring of maize re-growth
by Simon Mansfield
Sydney, Australia (SPX) Oct 21, 2024
Maize, also known as corn, is a vital cereal crop used globally for food production and trade. However, extreme weather events such as rain and wind can cause maize plants to fall, a phenomenon known as lodging. This leads to shorter plants and overlapping leaves, which hinder the crop's growth and development. Traditional methods for managing lodging require significant manual effort to inspect fields, but researchers in China have explored a more efficient alternative using UAV-based hyperspectral imaging.

"UAV-based hyperspectral imaging technology revolutionizes the way we monitor and assess the recovery of lodging crops," said Qian Sun, a researcher at Yangzhou University. "This advanced method allows for rapid, non-destructive evaluation of plant health and growth. This not only aids in better understanding the state of plants but also enhances overall crop management practices, potentially leading to more effective interventions and improved agricultural production."

Drones equipped with hyperspectral imaging capture detailed data on various spectral bands across each pixel in the field, far surpassing human eyesight, which is limited to just three visible light bands. The research team used this technology to assess canopy height, coverage, and chlorophyll production, a key indicator of photosynthesis. This comprehensive approach, they explained, provides a more accurate evaluation of maize regrowth after lodging.

"This technique allows for more precise monitoring and assessment of lodging crop conditions compared to traditional methods," explained Xiaohe Gu, a professor at the Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences. "In particular, this study proposed a comprehensive evaluation framework that combines the canopy structure and the physiological activity, delivering a precise and efficient means of assessing the recovery grades of lodging maize."

The study found that this imaging method effectively assesses both the physical structure of the crop and its physiological activity, giving farmers valuable insights into how to adjust their management strategies to support crop recovery.

"The ultimate goal is to revolutionize agricultural practices through the widespread adoption of UAV-based hyperspectral technology," added Liping Chen, also a professor at the Research Center of Information Technology. "By making this advanced tool a standard component in crop monitoring, we aim to significantly enhance the accuracy and efficiency of assessing plant health and recovery. This will enable farmers and agronomists to manage crops more effectively, optimize interventions, and ultimately increase yield and productivity."

Other contributors to the study included Baoyuan Zhang, Xuxhou Qu, and Yanglin Cui, from the Research Center of Information Technology, and co-corresponding author Meiyan Shu from Henan Agricultural University.

Research Report:Evaluation of Growth Recovery Grade in Lodging Maize via UAV-Based Hyperspectral Images