Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Abstract

Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

“…The most investigated areas in this topic are the determination of changes in the content of water, nitrogen (N) in plants, as well as of chlorophyll or carotenoids, using various SVIs, which can be used to detect plant diseases. These techniques can be used to determine the nitrogen content of plants [217][218][219] and to detect plant stresses and diseases [56,57,78,[220][221][222], including the early detection of plant diseases and pest infestations [147,154,156,157,223].…”

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

“…The Nitrogen Nutrition Index (NNI) was prominently featured [29], being referenced 26 times, showcasing a steady uptrend over time. Followed by the Partial Factor Productivity (PFP), which was mentioned in eight publications [30]. The agronomic Nitrogen Use Efficiency (aNUE) [31] and Nitrogen Utilization Efficiency (NutE) [18] were equally represented, each being mentioned in six studies.…”

Post‐harvest drone flights to measure weed growth and yield associations

“…An effective way of measuring LAI and other plant characteristics is through drone imagery, which has already been used to predict corn ( Zea mays L.) yield (Meresma et al., 2020), derive variable rate nitrogen (N) recommendations (Thompson & Puntel, 2020), and detect in‐season weed growth (Singh et al., 2020). While many studies have measured in‐season weed growth with drones, they may overlook post‐harvest data collection, even though weed biomass can be physically measured in harvested fields (Youngerman et al., 2018).…”