ArticleSensors (Basel, Switzerland)2020
A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A review: research progress on intelligent technologies for orchard yield monitoring.Frontiers in plant science · 2026Review
- Apple Yield Estimation Method Based on CBAM-ECA-Deeplabv3+ Image Segmentation and Multi-Source Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- UAV multispectral sensing and data-driven modeling for precision onion yield prediction.Frontiers in plant science · 2025Article
- Evaluation of macadamia felted coccid (Hemiptera: Eriococcidae) damage and cultivar susceptibility using imagery from a small unmanned aerial vehicle (sUAV), combined with ground truthing.Pest management science · 2022Article
- Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.).Plant methods · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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
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Registered trials
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