Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. (January 2020)

• Record Type: Journal Article
• Authors: Zhang, Meina; Zhou, Jianfeng; Sudduth, Kenneth A.; Kitchen, Newell R.
• Is Part Of: Biosystems engineering. Volume 189(2020)
• Page Start: 24
• Page End: 35
• Publication Date: 2020-01
• Subjects: Maize -- UAV -- Yield prediction -- Colour feature -- Modelling -- Variable-rate application; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2019.11.001
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : 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 m 2 ). The model performance was evaluated using coefficient of determination ( R 2 ), 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 R 2 values were all <0.5. Prediction error was lower at the later growth stages, as the crop approached maturity, and at the largest sample level. The ExG image feature showed potential for evaluating the effect of variable-rate N application on crop growth. Overall, the low-cost UAV imaging system provided useful information for field management. Highlights: A low-cost UAV-based visual band imaging system was used in monitoring maize growth. Corn yield was estimated using visual band image features with an error of 6–15%. The spatial resolution and imaging time influenced the accuracy of yield estimation. The effect of variable-rate N application on crops might be evaluated by images.