Study: Drones prove effective way to monitor maize re-growth
Maize lodging – when it falls due to rain, wind, and other weather events – can lead to yield reductions, decreased grain quality, and increased harvest costs.
Management practices like variety selection, plant population control, and proper fertilisation can help mitigate lodging risk.
But researchers in China now claim that a potential solution could be a rapid, non-destructive method of remote monitoring, called Unmanned Aerial Vehicle (UAV)-based hyperspectral imaging.
Conventional lodging prevention and mitigation, they say, takes many agricultural technicians significant time to investigate the crop fields. Their drone method on the other hand, revealed in the Journal of Remote Sensing, can accurately evaluate maize recovery without the time or expense of individuals physically inspecting the crops.
“UAV-based hyperspectral imaging technology revolutionises the way we monitor and assess the recovery of lodging crops,” said first author Qian Sun, a doctor 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.”
A more detailed understanding of canopy height and coverage
UAV-based hyperspectral imaging involves using drone-like vehicles that can fly with limited human input and examine the field. For every pixel in an image, the method determines the multiple spectral bands. This gives a much more detailed understanding than human eyesight, which only sees across three bands of visible light.
The researchers used UAV-based hyperspectral imaging to assess canopy height and coverage, as well as physiological activity of maize, such as chlorophyll production — evidence of photosynthesis, an energy-producing process that may reduce if stalks are shorter or leaves are obscured by other plants after lodging. This two-prong approach is necessary for accurate assessment, the researchers say, as measuring just one variable provides an incomplete picture of the maize’s regrowth progress.
“This technique allows for more precise monitoring and assessment of lodging crop conditions compared to traditional methods,” said co-corresponding author Xiaohe Gu, a professor with at the 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.”
Increases in yield and productivity
They determined that their imaging approach could accurately assess both the canopy stature and the physiological activity, providing information to farmers who could then make adjustments to the crops to assist in their recovery.
“The ultimate goal is to revolutionize agricultural practices through the widespread adoption of UAV-based hyperspectral technology,” said co-author author Liping Chen, a professor with the Research Center of Information Technology, Beijing Academy of Agriculture and Forestry.
“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, optimise interventions, and ultimately increase yield and productivity.”
