Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery

By Meina Zhang, Jianfeng Zhou, Kenneth A. Sudduth, Newell R. Kitchen. 2020

93 citations recorded by the source corpus.

Accurate crop yield estimation is important for agronomic and economic decision-making. This study evaluated the performance of imagery data acquired using a unmanned aerial vehicle (UAV)-based imaging system for estimating yield of maize (Zea mays L.) and the effects of variable-rate nitrogen (N) application on crops. Images of a 27-ha maize field were captured using a UAV with a consumer-grade RGB camera flying at ~100 m above ground level at three maize growth stages. The collected sequential images were stitched and the Excess Green (ExG) colour feature was extracted to develop prediction models for maize yield and to examine the effect of the variable-rate N application. Various linear regression models between ExG and maize yield were developed for three sample area sizes (21, 106, and 1058 m2). The model performance was evaluated using coefficient of determination (R2), F-test and the mean absolute percentage error (MAPE) between estimated and actual yield. All linear regression models between ExG and yield were significant (p ≤ 0.05). The MAPE ranged from 6.2 to 15.1% at the three sample sizes, although R2 values were all

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/09bbe924db98b09cb1713023edc27a3900f519fc

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In Ecology

In Environmental Engineering

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Citations in the corpus

High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, background): “Compared with other sensors, the advantage of the RGB camera relates to its affordable price and high-spatial-resolution imagery [237].”