127. Automatic diagnosis of a multi-symptom grapevine disease by decision trees and Graph Neural Networks-Reference-Cited by-同舟云学术

127. Automatic diagnosis of a multi-symptom grapevine disease by decision trees and Graph Neural Networks

Published:2023-07-02 Issue: Volume: Page:
ISSN:
Container-title:Precision agriculture ’23
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Author:

Tardif M.¹,Keresztes B.¹,Deshayes A.¹,Martin D.²,Greven M.³⁴,Da Costa J.P.¹³

Affiliation:

1. Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, 33400 Talence, France.

2. Plant & Food Research, 85 Budge Street, 7201 Blenheim, New Zealand.

3. Bordeaux Sciences Agro, 1 Cr du Général de Gaulle, 33175 Gradignan, France.

4. EGFV, Univ. Bordeaux, BSA, ISVV, 1 Cr du Général de Gaulle, 33170 Gradignan, France.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-947-3_127

Reference17 articles.

1. In Field Detection of Downy Mildew Symptoms with Proximal Colour Imaging

2. Ali, Jehad, Khan, R.U., Ahmad, N. and Maqsood, M. (2012). Random Forests and Decision Trees. International Journal of Computer Science Issues (IJCSI) 9 272-278.

3. Bianchi, F.M., Grattarola, D. and Alippi, C. (2020). Spectral Clustering with Graph Neural Networks for Graph Pooling. Non-peer reviewed preprint at arXiv. https://doi.org/10.48550/arXiv.1907.00481.

4. Bochkovskiy, A., Wang, C.Y. and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. Non-peer reviewed preprint at ArXiv:2004.10934 [Cs, Eess], April. http://arxiv.org/abs/2004.10934.

5. Boulent, J. (2020). Identification des problèmes phytosanitaires de la vigne au sein de la parcelle : association de l’imagerie à ultra-haute résolution spatiale et de l’apprentissage profond (Identification of vineyard pest problems within the block: combining ultra-high spatial resolution imaging and deep learning). Doctoral thesis, http://hdl.handle.net/11143/17855.