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Spectral characterization and classification of two different crown root rot and vascular wilt diseases (fusarium oxysporum f.sp. radicis lycopersici and fusarium solani) in tomato plants using different machine learning algorithms

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Abstract

Fusarium oxysporum f.sp. radicis lycopersici (FORL) and Fusarium solani (F.S.) are common fungi responsible for crown root rot and vascular wilt diseases that highly impact the development of plants, causing significant yield losses. This study investigated changes in the hyperspectral reflectance of normal and Fusarium (FORL and F.S.)-infected tomato plants in a growth chamber at different disease stages (3, 10, 16, 23, 31 and 37 days after inoculation (DAI)) using a spectroradiometer as an alternative to traditional approaches for the early identification and classification of such diseases. Raw spectra, significant wavebands obtained with the RELIEF algorithm and various statistical features extracted from raw spectra were used to classify healthy and infected plants using three different classification algorithms (CAs): decision tree, cubic support vector machine and k-nearest neighbor models. At different stages of the disease, the spectral bands such as 508, 711, 540, 717, 536, 644 nm and 705, 1883, 525, 518, 444, 522 nm were the most effective in distinguishing FORL and F.S.-inoculated plants from healthy plants, respectively. While FORL caused general stress in the plants, F.S. also had a negative physiological effect. All CAs proved highly successful in distinguishing healthy and diseased plants, with maximum classification accuracy achieved as early as 3 DAI. CAs using statistical parameters as input had higher accuracies than other CAs. Healthy and diseased plant classification was significantly different between the different CAs (p < 0.05), while DAI, pathogen type and inputs of the classification did not exhibit significant differences in classification (p > 0.05) according to ANOVA.

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Data is available on request from the authors.

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Acknowledgments

The authors are grateful to Charlene Mottler and Robert Schindelbeck for help with the language editing of the article.

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Authors and Affiliations

  1. Department of Plant Health, GAP Agricultural Research Institute (GAPTAEM), 63100, Şanlıurfa, Turkey

    Ayşin Bilgili

  2. Department of Soil Science and Plant Nutrition, Agriculture Faculty, Harran University, Osmanbey Campus, 63300, Şanlıurfa, Turkey

    Ali Volkan Bilgili

  3. Department of Computer Engineering, Faculty of Engineering, Harran University, Osmanbey Campus, 63300, Şanlıurfa, Turkey

    Mehmet Emin Tenekeci

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering, Harran University, Osmanbey Campus, 63300, Şanlıurfa, Turkey

    Kerim Karadağ

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  1. Ayşin Bilgili
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Correspondence to Ayşin Bilgili.

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Bilgili, A., Bilgili, A.V., Tenekeci, M.E. et al. Spectral characterization and classification of two different crown root rot and vascular wilt diseases (fusarium oxysporum f.sp. radicis lycopersici and fusarium solani) in tomato plants using different machine learning algorithms. Eur J Plant Pathol 165, 271–286 (2023). https://doi.org/10.1007/s10658-022-02605-8

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