ISSN : 2583-2646

Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network

ESP Journal of Engineering & Technology Advancements
© 2023 by ESP JETA
Volume 3  Issue 1
Year of Publication : 2023
Authors : T. Vignesh, E.Srie Vidhya Janani
:10.56472/25832646/JETA-V3I1P102

Citation:

T. Vignesh, E.Srie Vidhya Janani, 2023. "Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network" ESP Journal of Engineering & Technology Advancements  3(1): 07-14.

Abstract:

Finding plant leaves is a crucial step in preventing a major outbreak. The automatic diagnosis of plant disease is an important research area. Similar to humans and other animals, plants also experience the negative effects of sickness. These diseases affect the entire plant, including the leaf, stem, fruit, root, and flower. More often than not, when a plant's sickness is left untreated, the plant bites the ground or can also cause the loss of leaves, blooms, natural products, and so forth. For accurate identification and treatment of plant diseases, these disorders must be properly dedicated. The study of plant infections, their causes, and methods for containing and managing them is known as plant pathology. However, the modern strategy emphasises human inclusion for order and differentiating disease evidence. This strategy is time-consuming and expensive. Programmable disease detection from plant leaf images using a sensitive registration technique may be more valuable than the existing one. In this research, we present a method for identifying symptoms and characterising plant leaf illnesses organically called Bacterial looking development based entirely Radial Basis Function Neural Network (BRBFNN). We employ bacterial looking streamlining (BFO), which also increases the speed and accuracy of the device, to give Radial Basis Function Neural Network (RBFNN) the best possible weight when understanding various illnesses at the plant Leaf's. The suggested method improves recognition of evidence and infection characterisation.

References:

[1] Neto, J.C., Meyer, G.E., Jones, D.D., Samal, A.K.: Plant species identification using elliptic Fourier leaf shape analysis. Comput. Electron. Agric. 50(2), 121–134 (2006).
[2] Agarwal, G., Belhumeur, P., Feiner, S., Jacobs, D., Kress, J.W.R., Ramamoorthi, N.B., Dixit, N., Ling, H., Russell, D., Mahajan, R., Shirdhonkar, S., Sunkavalli, K., White, S.: First steps toward an electronic field guide for plants. Taxon 55(3), 597– 610 (2006)
[3] Knight, D., Painter, J., Potter, M.: Automatic plant leaf classification for a mobile field guide (2010)
[4] Du, J.X.,Wang, X.-F., Zhang, G.-J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007)
[5] White, S.M., Marino, D., Feiner, S.: Designing a mobile user interface for automated species identification. In:Conference on Human Factors in Computing Systems, pp. 291–294, San Jose (2007)
[6] Park, J., Hwang, E., Nam, Y.: Utilizing venation features for efficient leaf image retrieval. J. Syst. Softw. 81(1), 71–82 (2008)
[7] Wang, X.-F., Huang, D.-S., Du, J.-X., Xu, H., Heutte, L.: Classification of plant leaf images with complicated background. Appl. Math. Comput. 205(2), 916–926 (2008)
[8] Teng, C.H., Kuo, Y.T., Chen, Y.S.: Leaf segmentation, its 3d position estimation and leaf classification from a few images with very close viewpoints. In: International Conference on Image Analysis and Recognition, pp. 937–946, Halifax (2009)
[9] Villena-Román, J., Lana-Serrano, S., González-Cristóbal, J.C.: In: Proceeding of CLEF 2011 Labs and Workshop, Notebook Papers. Amsterdam, The Netherlands (2011)
[10] Paris, S., Halkias, X., Glotin, H.: Participation of LSIS/DYNI to ImageCLEF 2012 plant images classification task. In: Proceeding of CLEF 2012 Labs andWorkshop, Notebook Papers. Rome, Italy (2012)
[11] Chen, S.Y., Lee, C.L.: Classification of leaf images. Int. J. Imaging Syst.Technol. 16(1), 15–23 (2006)
[12] Hossain, J., Amin,M.A.: Leaf shape identification based plant biometrics. In: InternationalConference on Computer and Information Technology, pp. 458–463, Dhaka (2010)
[13] Du, J.X.,Wang, X.-F., Zhang, G.-J.: Leaf shape based plant species recognition. Appl. Math. Comput. 185(2), 883–893 (2007) [14] Backes, A.R., Bruno, O.M.: Shape classification using complex network and multi-scale fractal dimension. Pattern Recogn.Lett. 31(1), 44–51 (2010)
[15] Casanova, D., Florindo, J.B., Gonçalves, W.N., Bruno, O.M.:IFSC/USP at ImageCLEF 2012: plant identification task. In: Proceeding of CLEF 2012 Labs and Workshop, Notebook Papers. Rome, Italy (2012)
[16] Zhang, S., Lei, Y., Dong, T., Zhang, X.-P.: Label propagation based supervised locality projection analysis for plant leaf classification. Pattern Recogn. 46(7), 1891– 1897 (2013).

Keywords:

Neural Network (BRBFNN), Plant Pathology, Infection Characterisation, Image Processing, Analog and digital image processing, Digital Processing Techniques.