with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article-en


1 Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

2 Research Department of Plant Pathology, Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

3 Department of Physics, Shahid Beheshti University, Tehran, Iran

4 MSc. in Remote Sensing and Geographic Information System, Vanda Atlas Technologists Company, Karaj, Iran


Early diagnosis of plant diseases before the occurrence of symptoms can reduce the loss of the yield and increase the quality of agricultural crops. It also reduces the consumption of pesticides, environmental risks, and the cost of production. For this reason, the objectives of the present study were non-destructive diagnosis of early blight of tomato plant and discrimination of the most important agents of early blight (A. solani and A. alternate) in the primary stages of incidence of the disease before appearing visual symptoms using Vis-NIR spectroscopy (400-900 nm). The spectral data were acquired from the leaves of the plants infected with A. solani and A. alternate, 48 hours, 72 hours, 96 hours, and 120 hours after inoculation. To develop the recognition model based on the spectral data, principal components analysis (PCA) coupled with artificial neural network (ANN) was used. The results showed that the PCA-ANN model could diagnose the infected plants and pathogen species with accuracy of 93-100% for test set samples. In 96 hours after inoculation, in addition to the simpler model (8 PCs and 3 neurons in hidden layer), accuracy of 100% was obtained. At all times after inoculation, there was no error in diagnosis of the plants infected with A. solani that is more pathogenic and aggressive than other species, from healthy plants. Early blight in tomato plant and the type of pathogen before visual symptoms, without any plant sample preparation, could be diagnosed non-destructively (with accuracy of 93-100%) using Vis-NIR (400-900 nm) spectroscopy coupled with PCA-ANN. It was concluded that this technology could be used for rapid, low-cost, and early diagnosis of this disease in tomato plant instead of time-consuming, expensive, and destructive laboratory methods.


  1. Adhikari, P., Y. Oh, and D. R. Panthee. 2017. Current status of early blight resistance in tomato: an update. International Journal of Molecular 18 (10): 2019.
  2. Atherton, D., D. G. Watson, M. Zhang, Z. Qin, and X. Liu. 2015. Hyperspectral spectroscopy for detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants at two different growth stages. In 2015 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  3. Atherton, D., R. Choudhary, and D. Watson. 2017. Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants prior to visual disease symptoms. In 2017 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  4. Azadshahraki, F., B. Jamshidi, and V. R. Sharabiani. 2018. Non-destructive determination of vitamin C and lycopene contents of intact cv. Newton tomatoes using NIR spectroscopy. Yuzuncu Yil University Journal of Agricultural Sciences 28 (4): 389-397.
  5. Babagoli, M. A., and E. Behdad. 2012. Effects of three essential oils on the growth of the fungus Alternaria solani. Journal of Research in Agricultural Science 8 (14): 45-57.
  6. Brown, D. J., R. S. Bricklemyer, and P. R. Miller. 2005. Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana. Geoderma 129 (3-4): 251-267.
  7. Castro, W., J. Oblitas, R. Santa-Cruz, and H. Avila-George. 2017. Multilayer perceptron architecture optimization using parallel computing techniques. PloS One 12 (12): 1-17.
  8. Cen, H., and Y. He. 2007. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 18 (2): 72-83.
  9. Chaerani, R., and R. E. Voorrips. 2006. Tomato early blight (Alternaria solani): the pathogen, genetics, and breeding for resistance. Journal of General Plant Pathology 72 (6): 335-347.
  10. Chaerani, R., R. Groenwold, P. Stam, and R. E. Voorrips. 2007. Assessment of early blight (Alternaria solani) resistance in tomato using a droplet inoculation method. Journal of General Plant Pathology 73 (2): 96-103.
  11. Dai, Q., J. H. Cheng, D. W. Sun, H. Pu, X. A. Zeng, and Z. Xiong. 2015. Potential of visible/near-infrared hyperspectral imaging for rapid detection of freshness in unfrozen and frozen prawns. Journal of Food Engineering 149: 97-104.
  12. Ding, S., K. Meinholz, K. Cleveland, S. A. Jordan, and A. J. Gevens. 2019. Diversity and virulence of Alternaria spp. causing potato early blight and Brown spot in Wisconsin. Phytopathology 109 (3): 436-445.
  13. Ershad, D. 2009. Fungi of Iran. 3rd edition, Iranian Research Institution of Plant Protection. 531 pp.
  14. Fulton, T. M., J. Chunwongse, and S. D. Tanksley. 1995. Microprep protocol for extraction of DNA from tomato and other herbaceous plants. Plant Molecular Biology Reporter 13 (3): 207-209.
  15. Gold, K. M., P. A. Townsend, A. Chlus, I. Herrmann, J. J. Couture, E. R. Larson, and A. J. Gevens. 2020. Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sensing 12 (2): 286.
  16. Ghanei Ghooshkhaneh, N., M. R. Golzarian, and M. Mamarabadi. 2018. Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging. Journal of the Science of Food and Agriculture 98 (9): 3542-3550.
  17. Jamshidi, B., Minaei, E. Mohajerani, and H. Ghassemian. 2015. Pattern recognition of near-infrared spectroscopy for non-destructive discrimination of oranges based on taste index. Journal of Agricultural Machinery 5 (1): 101-110. (In Persian).
  18. Jinendra, B., K. Tamaki, S. Kuroki, M. Vassileva, S. Yoshida, and R Tsenkova. 2010. Near infrared spectroscopy and aquaphotomics: Novel approach for rapid in vivo diagnosis of virus infected soybean. Biochemical and Biophysical Research Communications 397 (4): 685-690.Kia, M. 2010. Neural network using MATLAb. Tehran: Kiyan Rayane.408pp.
  19. Mireei, S. A., S. S. Mohtasebi, R. Massudi, S. Rafiee, and A. S. Arabanian. 2010. Feasibility of near infrared spectroscopy for analysis of date fruits. International Agrophysics 24 (4): 351-356.
  20. Minich, D. M. 2019. A review of the science of colorful, plant-based food and practical strategies for “eating the rainbow. Nutrition and Metabolism 2019.
  21. Mouazen, A. M., W. Saeys, J. Xing, J. De Baerdemaeker, and H. Ramon. 2005. Near infrared spectroscopy for agricultural materials: an instrument comparison. Journal of Near Infrared Spectroscopy 13 (2): 87-97.
  22. Nicolai, B. M., K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron, and J. Lammertyn. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology 46 (2): 99-118.
  23. Nicolai, B. M., T. Defraeye, B. De Ketelaere, E. Herremans, M. L. Hertog, W. Saeys, A. Torricelli, T. Vandendriessche, and P. Verboven. 2014. Nondestructive measurement of fruit and vegetable quality. Annual Review of Food Science and Technology 5: 285-312.
  24. Omid, M., A. Mahmoudi, and M. H. Omid. 2010. Development of pistachio sorting system using principal component analysis (PCA) assisted artificial neural network (ANN) of impact acoustics. Expert Systems with Applications 37 (10): 7205-7212.
  25. Pan, L., Q. Zhang, W. Zhang, Y. Sun, P. Hu, and K. Tu. 2016. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chemistry 192: 134-141.
  26. Purcell, D. E., M. G. O'Shea, R. A. Johnson, S. Kokot. 2009. Near-infrared spectroscopy for the prediction of disease ratings for Fiji leaf gall in sugarcane clones. Journal of Applied Spectroscopy 63 (4): 450-457.
  27. Rotem, J. 1994. The genus Alternaria: biology, epidemiology, and pathogenicity. American Phytopathological Society Press, St. Paul, Minnesota.
  28. Salchenberger, L. M., E. M. Cinar, and N. A. Lash. 1992. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 (4): 899-916.
  29. Sankaran, S., A. Mishra, J. M. Maja, and R. Ehsani. 2011. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Computers and Electronics in Agriculture 77 (2): 127-134.
  30. Sigmund, R., and E. Gustav. 1991. The cultivated plants of the tropics and subtropics. Institute of Agronomy in the Tropics, University of Gottingen, Germany, p. 552.
  31. Sankaran, S., R. Ehsani, S. A. Inch, and R. C. Ploetz. 2012. Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt. Plant Disease 96 (11): 1683-1689.
  32. Sankaran, S., and R. Ehsani. 2013. Comparison of visible-near infrared and mid-infrared spectroscopy for classification of Huanglongbing and citrus canker infected leaves. Agricultural Engineering International: CIGR Journal 15 (3): 75-79.
  33. Sherf, A. F., and A. A. MacNab. 1986. Vegetable diseases and their control. John Wiley and Sons, USA, 722 pp.
  34. Simmons, E. G. 2000. Alternaria themes and variations (244-286) species on Solanaceae. Mycotaxon 75: 1-115.
  35. Song, Y., Q. Diao, and H. Qi. 2015. Polyamine metabolism and biosynthetic genes expression in tomato (Lycopersicon esculentum Mill) seedlings during cold acclimation. Journal of Plant Growth Regulation 75: 21-32.
  36. Teye, E., X. Y. Huang, and N. Afoakwa. 2013. Review on the potential use of near infrared spectroscopy (NIRS) for the measurement of chemical residues in food. American Journal of Food Science and Technology 1: 1-8.
  37. Taiz, L., and E. Zeiger. 2002. Plant Physiology. 3rd ed Sinauer Associates Inc Publishers. Sunderland, MA, 690pp.
  38. Thomma, B. P. 2003. Alternaria: from general saprophyte to specific parasite. Molecular Plant Pathology 4 (4): 225-236.
  39. Wold, S., M. Sjöström, and L. Eriksson. 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58 (2): 109-130.
  40. Xie, C., Y. Shao, X. Li, and Y. He. 2015. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports 5: 16564.
  41. Xie, C., and Y. He. 2016. Spectrum and image texture features analysis for early blight disease detection on eggplant leaves. Sensors 16 (5): 676.
  42. Yin, X., and S. Zhao. 2013. Hyperspectral recognition of processing tomato early blight based on GA and SVM. In Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing (Vol. 8783, p. 87831D). International Society for Optics and Photonics.
  43. Yoplac, I., H. Avila-George, L. Vargas, P. Robert, and W. Castro. 2019. Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools. Heliyon 5 (7): e02122.
  44. Zhang, D., J. Y. He, P. Haddadi, J. H. Zhu, Z. H. Yang, and L. Ma. 2018. Genome sequence of the potato pathogenic fungus Alternaria solani HWC-168 reveals clues for its conidiation and virulence. BMC Microbiology 18 (1): 1-13.
  45. Zitter, T. A., J. L. Drennan, M. A. Mutschler, and M. J. Kim. 2004. Control of early blight of tomato with genetic resistance and conventional and biological sprays. In I International Symposium on Tomato Diseases 695 (pp. 181-190).