Using Drones to Predict Degradation of Surface Drainage on Agricultural Fields: A Case Study of the Atlantic Dykelands

Excess water in agricultural fields can significantly limit crop productivity. Drone technology offers solutions for identifying and predicting drainage degradation. This study utilized drone-based photogrammetry to create high-resolution elevation models, multispectral imagery for vegetation indices, and flood simulations models to identify zones at risk of poor surface drainage. These models, collected from 2021 to 2023, were used to assess the relationship between poor drainage and corn productivity. The findings revealed a substantial decline in productivity in poorly maintained surface drainage areas, notably a decrease in mean plant height from 1.43 m in 2022 to 0.26 m in flood-prone areas in 2023. Flood-prone zones expanded significantly, from 37% to 61% of the field area between 2022 and 2023, emphasizing the negative cumulative impacts of pre-existing drainage issues. Conversely, fields receiving regular annual maintenance showed an increase in mean plant heights (from 2.23 m to 2.54 m) and NDVI values, reflecting improved drainage conditions. This research demonstrates the effectiveness of drone-derived elevation models for proactively identifying problematic drainage areas, allowing farmers to make informed decisions regarding field maintenance. Implementing these technologies can optimize drainage management practices, enhance agricultural productivity, and increase economic viability in regions that rely on surface drainage.