The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence
Open accessAbstract
Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.
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Cites
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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
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Machine Learning for High-Throughput Stress Phenotyping in Plants
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Cited by
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Cognition in UAV-Aided 5G and Beyond Communications: A Survey
Ullah, Z., Al-Turjman, F., & Mostarda, L. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2968311 · Cited by 226
Metadata
- Authors
- Ampatzidis, Y., & Partel, V.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2019-02-17
- Volume
- 11
- Issue
- 4
- Pages
- 410
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2917901091
- Cited by
- 266
- References
- 24
- FWCI
- 20.47
- Type
- journal article
- Language
- en
- Open access
- gold
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