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Open AccessArticle

A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information

1
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2
Jiangsu Province Engineering Lab for Modern Intelligent Facilities of Agriculture Technology & Equipment, Nanjing 210031, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2985; https://doi.org/10.3390/s20102985
Received: 3 May 2020 / Revised: 22 May 2020 / Accepted: 22 May 2020 / Published: 25 May 2020
(This article belongs to the Special Issue Sensors in Agriculture 2020)
To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards. View Full-Text
Keywords: orchard; apple tree; canopy information; unmanned aerial vehicle (UAV); multispectral; multimodal orchard; apple tree; canopy information; unmanned aerial vehicle (UAV); multispectral; multimodal
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MDPI and ACS Style

Sun, G.; Wang, X.; Yang, H.; Zhang, X. A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information. Sensors 2020, 20, 2985.

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