International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Detailed Review on Recognition of Plant Disease Using Intelligent Image Retrieval Techniques

  • Gulbir Singh (Department of Computer Science & Engineering, Banasthali Vidyapith, MMICT&BM, M.M.(Deemed to be University)) ;
  • Kuldeep Kumar Yogi (Department of Computer Science & Engineering, Banasthali Vidyapith)
  • Received : 2023.09.05
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

Keywords

References

  1. Abu-Naser, S., S., Kashkash, K., A,. and Fayyad, M. (2008). Developing an Expert System for Plant Disease Diagnosis. Journal of Artificial Intelligence 1(2):78-85.
  2. Aji, A., F., Munajat, Q., Pratama, A., P., Kalamullah, H., Aprinaldi, J., Setiyawan, and Arymurthy, A., M.(2013). Detection of Palm Oil Leaf Disease with Image Processing and Neural Network Classification on Mobile Device. International Journal of Computer Theory and Engineering Vol. 5(3), pp. 528-532. https://doi.org/10.7763/IJCTE.2013.V5.743
  3. Albregtsen, F. (2008). Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, department of informatics, university of oslo, vol. 5.
  4. Anand.H. Kulkarni, Ashwin Patil R. K.(2012). Applying image processing technique to detect plantdiseases. International Journal of Modern Engineering Research (IJMER), Vol.2 (5), ISSN: 2249- 6645, pp-3661-3664.
  5. Babu, M., S., P. and Rao, B., S.(2007). Leaves Recognition Using Back Propagation Neural Network-Advice For Pest and Disease Control On Crops.IndiaKisan.Net: Expert Advissory System.
  6. Camargo and Smith, J. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering.
  7. Glorot, X. , Bordes, A. . and Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315-323.
  8. Gulhane, V., A. and Gurjar, A., A.(2001). Detection of diseases on cotton leaves and its possible diagnosis. International Journal of Image Processing, vol. 5, no. 5, pp. 590-598, 2011.
  9. Gupta, D., Singh, R. (2019). Disease Detection in Plant using Artificial Neural Network. International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 7 Issue VI, June 2019. Page No: 2560-2565.
  10. He, K. Zhang, ., X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, Las Vegas, NV, USA.
  11. Huang, J., Kumar, S. R., Mitra, M., Zhu, W. J., and Zabih, R. (1997). Image indexing using Color correlograms. IEEE Int. Conf. Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp.762-768.
  12. Jin, X., Jie, L., Wang, S., Qi, H., Li, S.(2018). Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens, 10, 395.
  13. Kaundal, R., Kapoor, A., S., and PS Raghava, G.(2006). Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics.
  14. Kho, S. J., S. Manickam, S. Malek, M. Mosleh, S.K. Dhillon, (2017). Automated plant identification using artificial neural network and support vector machine. Frontiers in Life Science, 10(1), 98-107. https://doi.org/10.1080/21553769.2017.1412361
  15. Krizhevsky, Sutskever, I. and Hinton, G., H., E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
  16. Kulkarni, N. (2012). Color thresholding method for image segmentation of natural images. International Journal of Image, Graphics and Signal Processing, vol. 4, p. 28.
  17. Kumari, C. U., Prasad, S. J., and Mounika, G.(2019). Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN. 3rd International Conference on Computing Methodologies and Communication (ICCMC).
  18. LeCun, Y. ., Bengio, Y. , and Hinton, G. (2015). Deep learning. Nature, vol. 521, no. 7553, pp. 436-444. https://doi.org/10.1038/nature14539
  19. Maheswary, P., Srivastav N., (2008). Retrieving similar image using Color moment feature detector and Kmeans clustering of remote sensing images. International conference on computer and electrical engineering, pp-821-824.
  20. Maliappis, M., T., Ferentinos, K., P., Passam, K., P., And. Sideridis, A., B. (2008) Gims: A Web based Greenhouse Intelligent Management System. World Journal of AGRICLTURAL Sciences 4(5):640-647.
  21. Miller, S., Beed, F. and Harmon, C.(2009). Plant disease diagnostic capabilities and networks. Annual Review of Phytopathology.
  22. Mohanty, S., P., Hughes, D. and Salathe, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. eprint arXiv:1604.03169.
  23. Parsons, N., Edmondson, R. and Song, Y.(2009). Image analysis and statistical modeling for measurement and quality assessment of ornamental horticulture crops in glasshouses. Biosystems Engineering.
  24. Pawar, P., Turkar, V. and Patil, P. (2016). Cucumber disease detection using artificial neural network. International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1-5.
  25. Phadikar, S. & Sil, J.(2008). Rice Disease Identification Using Pattern Recognition Techniques. Proceedings Of 11th International Conference On Computer And Information Technology, 25-27
  26. Polder, G., Blok, P.M., de Villiers, H.A.C., van der Wolf, J.M., Kamp, J.(2019). Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images. Front. Plant Sci., 10.
  27. Preethi G. and Sornagopal, V. (2014). MRI image classification using GLCM texture features," in Green Computing Communication and Electrical Engineering (ICGCCEE).
  28. Rathod, A., N., Tanawal B., Shah, V.(2013). Image processing techniques for detection of leaf disease. Int J Adv Res Comput Sci Softw Eng, Page-3.
  29. Revathi, P. and Hemalatha, H.(2012). Classification of cotton leaf spot diseases using image processing edge detection techniques. Emerging Trends in Science, Engineering and Technology (INCOSET), International Conference on, pp. 169-173.
  30. Rumpf, T., Mahlein, A., Steiner, U., Oerke, E. and Plumer H. D. L.(2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agr.
  31. Signoroni, A., Savardi, M., Baronio, A., Benini, S.(2019). Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review. J., 5, 52.
  32. Simonyan, K. and Zisserman, A.(2015). Very deep convolutional networks for large-scale image recognition,. In Proceedings of the International Conference on Learning Representations, San Diego, CA, USA.
  33. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, Volume 2016, 11 pages.
  34. Tellaeche, X., P., Burgos-Artizzu, G. ,Pajares, and Ribeiro, A. (2008). A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition, vol. 41, no. 2, pp. 521-530. https://doi.org/10.1016/j.patcog.2007.07.007
  35. Tzionas, P., Papadakis, P., E. and Manolakis, D. (2005). Plant leaves classification based on morphological features and fuzzy surface selection technique. 5th International Conference ON Technology and Automation ICTA'05, Thessaloniki, Greece, pp.365-370, 15-16.
  36. Wang, D., Vinson, R., Holmes, M., Seibel, G., Bechar, A., Nof, S.;,Tao, Y. (2019). Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN). Sci. Rep, 9, 4377.
  37. Wu, S., G., Bao, F., S., Xu, E., U., Chang., F., U.(2007). A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. IEEE 7th International Symposium on Signal Processing and Information Technology.
  38. Ying, G., Miao, L., Yuan, Y., & Zelin, H.(2008). A Study on the Method of Image Pre-Processing for Recognition of Crop Diseases. International Conference on Advanced Computer Control, 2008 IEEE.
  39. Yu, H., Li, M., Zhang, H., Feng, J.(2003). Color texture moments for content-based image retrieval, International Conference on Image Processing.
  40. Yue, J., Zhao, W., Mao, S., Liu, H.(2015). Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens. Lett. 2015, 6, 468-477. https://doi.org/10.1080/2150704X.2015.1047045
  41. Zeiler, M., D. and Fergus, R.(2014). Visualizing and Understanding Convolutional Networks. European Conference on Computer Vision, pp. 818-833.
  42. Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonzalez-Moreno, P., Ma, H., Ye, H., Sobeih, T. A (2019). Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sens., 11, 1554.
  43. Zitnick , C., L. and Dollar, P. (2014). Edge boxes: locating object proposals from edges. In European Conference on Computer Vision, pp. 391-405.