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Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants

Year 2023, Volume: 14 Issue: 4, 611 - 619, 31.12.2023
https://doi.org/10.24012/dumf.1340922

Abstract

Plant diseases can lead to significant yield losses and economic damages, but these losses can be mitigated through early disease diagnosis. In recent times, remote sensing techniques have been widely used for early disease detection even before visible symptoms appear. This study focused on the potential of early detection of Fusarium Root and Crown Rot in Tomato Plants, which causes substantial yield losses in tomato plants, under controlled conditions using thermal images. In this research, thermal images were obtained from both disease-inoculated and disease-free control plants throughout the plant growth period under controlled conditions. These images underwent preprocessing in a computer environment, and various feature parameters related to temperature changes in both groups (such as minimum, maximum, standard deviation, and skewness) were extracted. These extracted features were then used as inputs for different machine learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Naive Bayes (NB), to classify healthy and diseased plants. Overall, the disease-inoculated plants exhibited higher average temperatures compared to the healthy control plants. The performance of the compared machine learning techniques in distinguishing between healthy and diseased plants was found to be in the order of KNN, NB, and LR, with success rates of 72%, 68%, and 60%, respectively. This study demonstrated the potential of using combined thermal images with different machine learning techniques for early diagnosis of Fusarium Root and Crown Rot in Tomato Plants. The results show promising prospects for utilizing thermal imaging in the early detection of plant diseases, leading to better management and reduction of yield losses and economic impacts.

References

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  • [12] I.C. Hashim, A.R.M.Shariff, S.K. Bejo, F.M. Muharam, K. Ahmad and H. Hashim, “ Application of thermal imaging for plant disease detection, ” IOP Conf. Series: Earth and Environmental Science, 540, 012052.
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Year 2023, Volume: 14 Issue: 4, 611 - 619, 31.12.2023
https://doi.org/10.24012/dumf.1340922

Abstract

References

  • [1] El-Sayed Ewis Omran, “Early sensing of peanut leaf spot using spectroscopy and thermal imaging”, Archives of Agronomy and Soil Science, 63:7, 883-896, 2017.
  • [2] Carvajal-Yepes M, Cardwell K, Nelson A, Garrett KA, Giovani B, Saunders DGO, Kamoun S, Legg JP, Verdier V, Lessel J, Neher RA, Day R, Pardey P, Gullino ML, Records AR, Bextine B, Leach JE, Staiger S, Tohme J., “A global surveillance system for crop diseases”, Science, vol. 364, no. 6447, pp. 1237-1239, Jun 2019.
  • [3] Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019 Mar;3(3):430-439. doi: 10.1038/s41559-018-0793-y. Epub 2019 Feb 4. PMID: 30718852.
  • [4] J.C., Zhang, R.L., Pu, J.H., Wang, W.J., Huang, L., Yuan, J.H. Luo, “Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements”, Computers and Electronics in Agriculture, vol. 85, pp.13-23. 2012.
  • [5] E., Bauriegel, A., Giebel, M., Geyer, U., Schmidt, W.B. Herppich, “Early detection of Fusarium infection in wheat hyper-spectral imaging”, Computers in Agriculture, vol. 75, pp. 304-312.
  • [6] K., Karadag, M.E., Tenekeci, R., Taşaltın, A. Bilgili, “Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance”. Sustainable Computing: Informatics and Systems, vol. 28, no. 100299. 2017.
  • [7] A., Bilgili, A.V., Bilgili, M.E. Tenekeci, and K. Karadag, “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, vol. 165, pp. 271–286, 2023.
  • [8] R. Ishimwe, K. Abutaleb, and F. Ahmed, “Applications of Thermal Imaging in Agriculture—A Review”. Advances in Remote Sensing, 3, 128-140.
  • [9] H. Erdoğan, A.K. Butuner and Y.S. Sahin,“ Detection of Cucurbit Powdery Mildew, Sphaerotheca Fuliginea (Schlech.) Polacci by Thermal Imaging in Field Conditions” Scientific Paper Series Management, Economic Engineering in Agriculture and Rucal Development”, 23, 1, 2023.
  • [10] I., Bhakta, S., Phadikar, K. Majumder, H. Mukherjee and A. Sau, “A novel plant disease prediction model based on thermal images using modified deep convolutional neural network”, Precision Agric, no: 24,pp. 23–39, 2023.
  • [11] S.A. Raza, V. Sanchez, G. Prince, J. P. Clarkson, N. M. Rajpoot, “Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain,” Pattern Recognition, vol. 48, no. 7, pp. 2119-2128, 2015.
  • [12] I.C. Hashim, A.R.M.Shariff, S.K. Bejo, F.M. Muharam, K. Ahmad and H. Hashim, “ Application of thermal imaging for plant disease detection, ” IOP Conf. Series: Earth and Environmental Science, 540, 012052.
  • [13], FAOSTAT World Food and Agriculture- Statistical Yearbook 2022. https://www.fao.org/3/cc2211en/cc2211en.pdf.
  • [14] A. Bilgili, “Determination of Root Rot Factors in GAP Region Pepper Cultivation, Molecular Characterization of the Active Pathogen and Investigation of the Efficiency of Mycorrhiza in its Control”. Ph.D Thesis, Turkey, Harran University. 2017 (In Turkish).
  • [15] Upov. Tomato, guidelines for the conduct of tests for distinctness, uniformity and stability. 2013. Retrieved October 20, 2022, from https://www.upov.int/edocs/tgdocs/en/tg044.pdf.
  • [16] M., Çalışan, & İ. Türkoğlu, “Termal Kameralar Ve Uygulamaları, TMMOB Elektrik-Elektronik ve Bilgisayar Sempozyumu” 2011, Elazığ.
  • [17] R.N. Singh, P. Krishnan, C. Bharadwaj, and B. Das, “Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions”, Ecological Informatics, vol. 73, 101933, 2023.
  • [18] S. Zia-Khan,M. Kleb, N. Merkt, S. Schock, J. Müller, “Application of Infrared Imaging for Early Detection of Downy Mildew (Plasmopara viticola) in Grapevine”, Agriculture 2022, 12, 617.
  • [19] Y. M. Awad, A. A. Abdullah, T. Y. Bayoumi, K. Abd-Elsalam and A. E. Hassanien, "Early detection of powdery mildew disease in wheat (triticum aestivum l.) using thermal imaging technique", Intelligent Systems’ 2014, pp. 755-765, 2015.
There are 19 citations in total.

Details

Primary Language Turkish
Subjects Machine Learning (Other)
Journal Section Articles
Authors

Ayşin Bilgili 0000-0002-3374-5602

Early Pub Date December 31, 2023
Publication Date December 31, 2023
Submission Date August 10, 2023
Published in Issue Year 2023 Volume: 14 Issue: 4

Cite

IEEE A. Bilgili, “Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants”, DUJE, vol. 14, no. 4, pp. 611–619, 2023, doi: 10.24012/dumf.1340922.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456