Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine
Abstract
:1. Introduction
2. Materials and Methods
2.1. Image Acquisition under Laboratory Conditions
2.2. Processing of RGB and Hyperspectral Images
2.3. Machine Learning Modelling
2.4. Implementation
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ali, M.M.; Bachik, N.A.; Muhadi, N.A.; Yusof, T.N.T.; Gomes, C. Non-destructive techniques of detecting plant diseases: A review. Physiol. Mol. Plant Pathol. 2019, 108, 101426. [Google Scholar] [CrossRef]
- Sankaran, S.; Mishra, A.; Ehsani, R.; Davis, C. A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 2010, 72, 1–13. [Google Scholar] [CrossRef]
- Oerke, E.-C.; Herzog, K.; Toepfer, R. Hyperspectral phenotyping of the reaction of grapevine genotypes toPlasmopara viticola. J. Exp. Bot. 2016, 67, 5529–5543. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gao, Z.; Khot, L.R.; Naidu, R.A.; Zhang, Q. Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging. Comput. Electron. Agric. 2020, 179, 105807. [Google Scholar] [CrossRef]
- Barbedo, J.G.A. Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 2019, 180, 96–107. [Google Scholar] [CrossRef]
- Mahlein, A.-K.; Kuska, M.T.; Thomas, S.; Wahabzada, M.; Behmann, J.; Rascher, U.; Kersting, K. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: Seamless interlocking of phytopathology, sensors, and machine learning is needed! Curr. Opin. Plant Biol. 2019, 50, 156–162. [Google Scholar] [CrossRef] [PubMed]
- Gonçalves, J.D.; Pinto, F.D.D.; de Queiroz, D.M.; Villar, F.M.D.; Barbedo, J.; del Ponte, E.M. Deep Learning Models for Semantic Segmentation and Automatic Estimation of Severity of Foliar Symptoms Caused by Diseases or Pests. 2020. Available online: https://osf.io/wdb79 (accessed on 9 July 2020). [CrossRef]
- Bock, C.H.; Barbedo, J.G.A.; Del Ponte, E.M.; Bohnenkamp, D.; Mahlein, A.-K. From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy. Phytopathol. Res. 2020, 2, 1–30. [Google Scholar] [CrossRef] [Green Version]
- Toffolatti, S.L.; Maddalena, G.; Salomoni, D.; Maghradze, D.; Bianco, P.A.; Failla, O. Evidence of resistance to the downy mildew agent Plasmopara viticola in the Georgian Vitis vinifera germplasm. Vitis -J. Grapevine Res. 2016, 55, 121–128. [Google Scholar] [CrossRef]
- Peressotti, E.; Duchêne, E.; Merdinoglu, D.; Mestre, P. A semi-automatic non-destructive method to quantify grapevine downy mildew sporulation. J. Microbiol. Methods 2011, 84, 265–271. [Google Scholar] [CrossRef] [PubMed]
- Nagi, R.; Tripathy, S.S. Infected Area Segmentation and Severity Estimation of Grapevine Using Fuzzy Logic. In Advances in Intelligent Systems and Computing; Springer: Berlin/Heidelberg, Germany, 2020; Volume 988, pp. 57–67. [Google Scholar]
- Bock, C.H.; Parker, P.E.; Cook, A.Z.; Gottwald, T.R. Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves. Plant Dis. 2008, 92, 530–541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pizer, S.M.; Amburn, E.P.; Austin, J.D.; Cromartie, R.; Geselowitz, A.; Greer, T.; Romeny, B.T.H.; Zimmerman, J.B.; Zuiderveld, K. Adaptive histogram equalization and its variations. Comput. Vision Graph. Image Process. 1987, 39, 355–368. [Google Scholar] [CrossRef]
- Yuen, H.; Princen, J.; Illingworth, J.; Kittler, J. Comparative study of Hough Transform methods for circle finding. Image Vis. Comput. 1990, 8, 71–77. [Google Scholar] [CrossRef] [Green Version]
- Liao, P.S.; Chen, T.S.; Chung, P.C. A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 2001, 17, 713–727. [Google Scholar] [CrossRef]
- Savitzky, A.; Golay, M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 1627–1639. [Google Scholar] [CrossRef]
- Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13, 583–598. [Google Scholar] [CrossRef] [Green Version]
- Wang, G.; Sun, Y.; Wang, J. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Comput. Intell. Neurosci. 2017, 2017, 2917536. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hernandez, I.; Gutierrez, S.; Barrio, I.; Toffolatti, S.; Taradaguila, J. Computer Vision and Fuzzy Logic for Evaluating Downy Mildew Severity in Grapevine. 2021; Submitted. [Google Scholar]
Aim | Image Type | Techniques | Method Information | ||||
---|---|---|---|---|---|---|---|
Training Time | Testing Time | Results | Detection Model | ||||
Severity estimation | RGB | Computer vision | None | Middle-Low | R2 | RMSE | - |
0.76 ** | 20.53% | - | |||||
Early detection | Hyperspectral | Hyperspectral preprocessing, computer vision and machine learning | Middle | Low | Accuracy (%) | F1-score | Model |
High | Low | 82 | 0.81 | CNN | |||
Low | Low | 66 | 0.62 | KNN | |||
Middle | Low | 81 | 0.80 | MLP | |||
Low | Low | 53 | 0.35 | PLS-DA |
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Hernández, I.; Gutiérrez, S.; Ceballos, S.; Iñíguez, R.; Barrio, I.; Tardaguila, J. Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine. Horticulturae 2021, 7, 103. https://doi.org/10.3390/horticulturae7050103
Hernández I, Gutiérrez S, Ceballos S, Iñíguez R, Barrio I, Tardaguila J. Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine. Horticulturae. 2021; 7(5):103. https://doi.org/10.3390/horticulturae7050103
Chicago/Turabian StyleHernández, Inés, Salvador Gutiérrez, Sara Ceballos, Rubén Iñíguez, Ignacio Barrio, and Javier Tardaguila. 2021. "Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine" Horticulturae 7, no. 5: 103. https://doi.org/10.3390/horticulturae7050103