Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system

Abstract

Food security is at an increased threat as plant diseases caused by pathogens continue to increase their global range, overcome plant tolerance, or develop resistance to fungicides. Leaf wetness is a critical component of disease development through the facilitation of microbial growth. The use of precision agriculture and IoT (Internet of Things) sensors can improve disease modeling and disease management by tracking a leaf's wetness duration. Weather variables including humidity, solar radiation, and precipitation can alter leaf wetness duration and vary among crop heights and canopy densities. The placement of humidity and leaf wetness sensors is in question based on canopy density which can alter these parameters. By using IoT in-field sensors at differing placements, the threshold that humidity must reach to initiate leaf wetness and their relation to a leaf wetness sensor were tracked. IoT sensors placed low in the corn canopy consistently showed lower wetness durations compared to a higher positioning, while the between or in-row placement in soybeans had no observable difference. High relative humidity and low temperature periods induced leaf wetness more often than other environmental factors. A humidity threshold of 85 % for all heights within the corn canopy and between or within soybean rows demonstrated strong correlations to sensor-observed wetness. 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.