Volume 13, Issue 1 ((Autumn & Winter) 2024)                   Plant Pathol. Sci. 2024, 13(1): 135-148 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hosseini S, Anvari Z. (2024). Application of new information technologies in plant pathology. Plant Pathol. Sci.. 13(1), 135-148. doi:10.61186/pps.13.1.135
URL: http://yujs.yu.ac.ir/pps/article-1-440-en.html
University of Birjand , ahosseini@birjand.ac.ir
Abstract:   (297 Views)
Hosseini, S. A., & Anvari, Z. (2024). Application of new information technologies in plant pathology. Plant Pathology Science, 13(1),135-148.
 Population growth has put significant pressure on the food supply chain, making it even more challenging to ensure that everyone has access to adequate, healthy, and nutritious food. The use of new information technologies based on artificial intelligence in agriculture can play a significant role in increasing the production of healthy plant products and ensuring food security for humans. All plant crops are highly vulnerable to diseases and timely and correct management of diseases is essential to optimize their production. New information technologies such as remote sensing, analysis of plant absorption light spectra, and the use of specialized Internet software for the diagnosis of plant diseases on mobile phones can help in the rapid and accurate diagnosis of diseases, the implementation of a forecasting program and their monitoring to prevent their spread, and the timely implementation of their management methods. The unique applications of these new information technologies in the identification, monitoring and management of plant diseases are described in this article.
Full-Text [PDF 1096 kb]   (291 Downloads)    
Type of Study: Extentional | Subject: Plants Diseases Management Methods
Received: 2024/05/15 | Accepted: 2024/10/27

References
1. Akbar S. (2020). Handbook of 200 Medicinal Plants: A Com prehensive Review of Their Traditional Medical Uses and Scientific Justifications. Springer: Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-030-16807-0 [DOI:10.1007/978-3-030-16807-0.]
2. Alam F., Mehmood R., Katib I., Albogami N.N., Albeshri A. , Ardagna D., Cappiello C., Samá W., & Vitali M. (2018). Context aware data quality assessment for big data. Future Generation Computer Systems 89, 548-562. [DOI:10.1016/j.future.2018.07.014]
3. DOI: https://doi.org/10.1016/j.future.2018.07.014 [DOI:10.1016/j.future.2018.07.014.]
4. Asghari P., Rahmani A.M., & Seyyed Javadi H.H. (2019). Internet of things applications: a systematic review. Computer Net works 148: 241-261. [DOI:10.1016/j.comnet.2018.12.008]
5. Buja I., Sabella E., Monteduro A.G., Chiriacò M.S., de Bel lis L., Luvisi A., Maruccio G. (2021). Advances in plant dis ease detection and monitoring: from traditional assays to in-field diagnostics. Senso 21 (6): 2129. DOI: https://doi org/10.3390/s21062129. [DOI:10.3390/s21062129] [PMID] []
6. Chen C.J., Huang Y.Y., Li Y.S., Chang C.Y., Huang Y.M. (2020). An AIoT based smart agricultural system for pests detec tion. IEEE Access 8: 180750-180761. DOI: http://dx.doi. org/10.1109/ACCESS.2020.302489. [DOI:10.1109/ACCESS.2020.3024891]
7. Chouhan S.S., Uday., Singh P., Jain S. (2020). Applications of computer vision in plant pathology: a survey. Archives of Computational Methods in Engineering 27: 611-632. DOI: https://doi.org/10.1007/s11831-019-09324-0 [DOI:10.1007/s11831-019-09324-0.]
8. Clarke, W.S. (2000) Problems of communication and technology transfer in crop protection': A practitioner's pepective. Proceedings of the BCPC Conference - Pests & Diseases 2000, 1185-1192, BCPC Publications, Farnham, UK.
9. Eberlein J., Davenport B., Nguyen T.T., Victorino F., Jhun K., van der Heide V., Kuleshov M., Ma'ayan A., Kedl R., Ho mann D. (2020). Chemokine signatures of pathogen-specific T cells I: effector T cells. Journal of Immunology 205: 2169-2187. DOI: https://doi.org/10.4049/jimmunol.2000253 [DOI:10.4049/jimmunol.2000253.] [PMID] []
10. Furlanetto R.H., Nanni M.R., Mizuno M.S., Crusiol L.G.T., da Silva C.R. (2021). Identification and classification of Asian soybean rust using leaf-based hype pectral reflectance. In ternational Journal of Remote Sensing 42: 4177-4198. DOI: https://doi.org/10.1080/01431161.2021.1890855 [DOI:10.1080/01431161.2021.1890855.]
11. Guo Y., Chen S., Wu Z., Wang S., Robin B.C., Senthilnath J., Cunha M., & Fu Y.H. (2021). Integrating spectral and textural information for monitoring the growth of pear trees using optical images from the UAV platform. Remote Sensing 13: 1795.DOI: https://doi.org/10.3390/rs13091795 [DOI:10.3390/ 13091795.]
12. Hahn F. (2009). Actual pathogen detection: senso and algo rithms - a review. Algorithms 2: 301-338. DOI: https://doi. org/10.3390/a2010301. [DOI:10.3390/a2010301]
13. Kuska M.T., Mahlein A.-K. (2018). Aiming at decision making in plant disease protection and phenotyping by the use of optical senso. European Journal of Plant Pathology152 (4): 987-992. DOI: https://doi.org/10.1007/s10658-018-1464-1 [DOI:10.1007/s10658-018-1464-1.]
14. Leeuw D.J., Vrieling A., Shee A., Atzberger C., Hadgu K.M., Biradar C.M., Keah H., & Turvey C. (2014). the potential and uptake of remote sensing in insurance: a review. Remote Sensing 6: 10888-10912. DOI: https://doi.org/10.3390/rs6099034 [DOI:10.3390/ 61110888.]
15. Li H., Zhao C., Yang G., & Feng H. (2015). Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf laye and spikes. Remote Sensing of Environment 169: 358-374. DOI: https://doi.org/10.1016/j.rse.2015.08.021 [DOI:10.1016/j. e.2015.08.021.]
16. Li L., Zhang S., Wang B. (2021). Plant disease detection and classification by deep learning - a review. IEEE Access 9: 56683-56698. DOI: https://doi.org/10.1109/ACCESS.2021.3069646 [DOI:10.1109/ACCESS. 2021.3069646]
17. Luvisi, A.; Panattoni, A.; Bandinelli, R.; Rinaldelli, E.; Pagano, M.; Triolo, E. (2012). Ultra-high frequency transponde in grapevine: A tool for traceability of plants and treatments in viticulture. Biosyst. Eng. 113, 129-139. [DOI:10.1016/j.biosystemseng.2012.06.015]
18. Mahlein A., Rumpf T., Welke P., Dehne H., Plümer L., Steiner U., Oerke E. (2013). Development of spectral indices for de tecting and identifying plant diseases. Remote Sensing of Environment 128: 21-30. DOI: https://doi.org/10.1016/j.rse.2012.09.019 [DOI:10.1016/j. e.2012.09.019.]
19. Mahlein A.K., Kuska M., Behmann J., Polder G., Walter A. (2018). Hyper-spectral senso and imaging technologies in phytopathology: state of the art. Annual Review of Phytpathology 56 (1): 535-558. DOI: https://doi.org/10.1146/annurev-phyto-080417-050100 [DOI:10.1146/ annurev-phyto-080417-050100.] [PMID]
20. Mandi S.S. (2016). Natural UV Radiation in Enhancing Survival Value and Quality of Plants. Springer, New Delhi, India. DOI: http://dx.doi.org/10.1007/978-81-322-2767-0. [DOI:10.1007/978-81-322-2767-0]
21. Mendes J., Pinho T.M., dos Santos F.N., Sousa J.J., Peres E., Boaventura-Cunha J., Cunha M., & Morais R. (2020). Smart phone applications targeting precision agriculture practices - a systematic review. Agronomy 10: 855. DOI: https://doi. org/10.3390/agronomy10060855. [DOI:10.3390/agronomy10060855]
22. Moshou D., Bravo C., Oberti R., West J.S., Ramon H., Vougiou kas S., Bochtis D. (2011). Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering 108: 311-321. DOI: https:// doi.org/10.1016/j.biosystemseng.2011.01.003. [DOI:10.1016/j.biosystemseng.2011.01.003]
23. Mrisho L.M., Mbilinyi N.A., Ndalahwa M., Ramcharan A.M., Kehs A.K., McCloskey P.C., Murithi H., Hughes D.P., & Legg J.P. (2020). Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symp toms of the viral diseases of cassava - CMD and CBSD. Frontie in Plant Science 11: 590889. DOI: https://doi. org/10.3389/fpls.2020.590889. [DOI:10.3389/fpls.2020.590889] [PMID] []
24. Ngie A., Abutaleb K., Ahmed F., Darwish A., & Ahmed M. (2014). Assessment of urban heat island using satellite remotely sensed imagery: a review. South African Geographical Journal 96 (2): 198-214. DOI: https://doi.org/10.1080/03736245.2014.924864 [DOI:10.1080/03736245.2014.924864.]
25. Ouhami M., Hafiane A., Es-Saady Y., El Hajji M., & Canals R. (2021). Computer vision, IoT and data fusion for crop dis ease detection using machine learning: a survey and ongo ing research. Remote Sensing 13: 2486. DOI: https://doi. org/10.3390/13132486. [DOI:10.3390/rs13132486]
26. Park Y., Jin S., Noda I., Jung Y.M. (2020). Emerging developments in two-dimensional correlation spectroscopy (2D-COS). Journal of Molecular Structure 1217: 128405. DOI: https:// doi.org/10.1016/j.molstruc.2020.128405. [DOI:10.1016/j.molstruc.2020.128405]
27. Rehman T.U., Mahmud M.S., Chang Y.K., Jin J., Shin J. (2019). Current and future applications of statistical machine learn ing algorithms for agricultural machine vision systems. Compute and Electronics in Agriculture 156: 585-605. DOI: https://doi.org/10.1016/j.compag.2018.12.006 [DOI:10.1016/j.compag.2018.12.006.]
28. Roldán J.J., del Cerro J., Garzón-Ramos D., Garcia-Aunon P., Garzón M., de León J., & Barrientos A. (2018). Robots in agri culture: state of art and practical experiences. p. 67-90. In: "Service Robots". IntechOpen. DOI: http://doi.org/10.5772/ intechopen.69874. [DOI:10.5772/intechopen.69874]
29. Sabrol H., & Kumar S. (2015). Recent studies of image and soft computing techniques for plant disease recognition and classification. International Journal of Computer Applica tions 126 (1): 44-55. DOI: http://dx.doi.org/10.5120/ijca2015905982. [DOI:10.5120/ijca2015905982]
30. Sankaran S., Mishra A., Ehsani R., & Davis C. (2010). A review of advanced techniques for detecting plant diseases. A review of advanced techniques for detecting plant diseases. Com pute and Electronics in Agriculture 72 (1): 1-13. DOI: https://doi.org/10.1016/j.compag.2010.02.007 [DOI:10.1016/j.compag.2010.02.007.]
31. Shibusawa S. (2001). Precision farming approaches to small farm agriculture. IFAC Proceedings Volumes 34 (2): 22-27. DOI: https://doi.org/10.1016/S1474-6670(17)34099-5 [DOI:10.1016/S1474-6670(17)34099-5.]
32. van Klink, R., August, T., Bas, Y., Bodesheim, P., Bonn, A., Fos søy, F., Høye, T.T., Jongejans, E., Menz, M.H.M., Miraldo, A., Roslin, T., Roy, H.E., Ruczyński, I., Schigel, D., Schäffler, L., Sheard, J.K., Svenningsen, C., Tschan, G.F., Wäldchen, J., Zizka, V.M.A., Åström, J., & Bowler, D.E. (2022). Emerging technologies revolutionise insect ecology and monitoring. Trends in Ecology & Evolution 37 (10): 872-885. DOI: https://doi.org/10.1016/j.tree.2022.06.001 [DOI:10.1016/j.tree.2022.06.001.] [PMID]
33. Waggoner, P.E. (1968) Weather and the rise and fall of fungi, in Biometeorology (ed. W P Lowry) Oregon State Univeity Press, Corvallis,Pp.45-60.
34. Waggoner, P.E., & Hofall, J.G. (1969) EPIDEM, a simulator of plant disease written for computer. Connecticut Agricultural Experimental Station Bulletin, 698, 80 pp.
35. Walter A., Finger R., Huber R., Buchmann N. (2017). Smart farming is key to developing sustainable agriculture. PNAS 114 (24): 6148-6150. DOI: https://doi.org/10.1073/pnas.1707462114 [DOI:10.1073/ pnas.1707462114] [PMID] []
36. Yuan L., Zhang J., Shi Y., Nie C., Wei L., Wang J. (2014). Damage mapping of powdery mildew in winter wheat with high--resolution satellite image. Remote Sensing 6: 3611-3623. DOI: https://doi.org/10.3390/rs6053611 [DOI:10.3390/ 6053611.]
37. Zhang J., Huang Y., Pu R., Gonzalez-Moreno P., Yuan L., Wu K., & Huang W. (2019). Monitoring plant diseases and pests through remote sensing technology: a review. Compute and Electronics in Agriculture 165: 104943. DOI: https:// doi.org/10.1016/j.compag.2019.104943. [DOI:10.1016/j.compag.2019.104943]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | University of Yasouj Plant Pathology Science

Designed & Developed by : Yektaweb