AN EFFECTUAL MULTIVARIATE SVM INTEGRATED WITH CNN FOR IDENTIFICATION OF DISEASES IN BANANA TREE

Authors

  • P. SENTHILRAJ
  • P. PARAMESWARI

DOI:

https://doi.org/10.47750/pnr.2022.13.S09.207

Keywords:

Banana Xanthomonas wilt (BXW), Pests, black & yellow Sigatoka, Multivariate, SVM & Banana Fusarium wilt (BFW).

Abstract

Banana production is one of India's important agricultural aspects, but the crop is affected by numerous illnesses. It is the most common commercial fruit crop farmed worldwide, and it is a main staple in many developing nations. Numerous plant diseases and pests harm banana crop globally. Novel and quick technologies for detecting diseases and pests will lead to more efficient surveillance and development of counter measures. Hence, Pest indicators are required to determine the infection early to prevent financial loss to farmers. By keeping this in mind, the proposed research has been formulated. An deeply integrated convolution neural network enables banana disease diagnosis, as well as the taxonomy is offered to solve these constraints.CNN is merged with Multivariate Support Vector Machine (M-SVM) classification model for predicting the disease as four categories A (BXW), B (BFW), C (BBTV) and D (BBS). The suggested model demonstrates 99% results as compared to similar deep learning methods.

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Published

2022-11-18

How to Cite

P. SENTHILRAJ, & P. PARAMESWARI. (2022). AN EFFECTUAL MULTIVARIATE SVM INTEGRATED WITH CNN FOR IDENTIFICATION OF DISEASES IN BANANA TREE. Journal of Pharmaceutical Negative Results, 1707–1719. https://doi.org/10.47750/pnr.2022.13.S09.207

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Section

Articles