Drone-Based Identification of Flood-Tolerant Maize via Multispectral Imaging: A Real-World Case Study

Abstract

Excess moisture (flooding, water logging, etc.) is a major source of crop damage causing catastrophic monetary losses to farmers around the world. Losses from excess water are often more common and costly than those from too little water (i.e., drought). Extreme weather patterns are predicted to increase, increasing the expected frequency of excess moisture events to farmers across the Midwest. Despite its importance, studying the impacts of flooding in the field is challenging due to unpredictability of flooding and fields being rendered inaccessible during flooding. Here, we took advantage of a natural flood experiment to examine the responses, damage, and recovery of diverse maize hybrids. Using drones, we monitored the hybrids before, during, and after flooding and examined the spatial and genetic components associated with post-flood survival.