Smart Technology Holds Promise for Improving Food Security in the Face of Plant Diseases

Strategic Insights -

Researchers at Michigan State University have explored the potential of precision agriculture and IoT sensors in improving disease modeling and management in corn and soybean fields. According to their study, leaf wetness is a critical component of disease development, and tracking this factor can help farmers make more informed decisions. The study found that IoT sensors placed low in the corn canopy consistently showed lower wetness durations compared to a higher positioning, highlighting the importance of sensor placement in disease management.

Key Takeaways:

  • The use of precision agriculture and IoT sensors can improve disease modeling and management by tracking leaf wetness duration.
  • Weather variables such as humidity, solar radiation, and precipitation can alter leaf wetness duration and vary among crop heights and canopy densities.
  • IoT sensors placed low in the corn canopy consistently showed lower wetness durations compared to a higher positioning.
  • The between or in-row placement in soybeans had no observable difference in leaf wetness duration.
  • A humidity threshold of 85% was found to be strongly correlated to sensor-observed wetness for all heights within the corn canopy and between or within soybean rows.
  • Off-site weather stations underreported wetness events by 10% for low-canopy corn, 17% for upper-canopy corn, and 13% for soybean.
  • IoT in-field sensors accurately reported leaf wetness and weather factors, highlighting the potential of these technologies to provide accurate and easily culminated wetness information.

Statistics:

  • 10% underreporting of wetness events by off-site weather stations for low-canopy corn.
  • 17% underreporting of wetness events by off-site weather stations for upper-canopy corn.
  • 13% underreporting of wetness events by off-site weather stations for soybean.
  • 85% humidity threshold found to be strongly correlated to sensor-observed wetness for all heights within the corn canopy and between or within soybean rows.

Sources:

  • Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system. Smart Agricultural Technology, 2025, 11(): 100919. (Elsevier).
  • NewsRx. Studies in the Area of Smart Technology Reported from Michigan State University [Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system]. Agriculture Week. August 14, 2025; p 731.