International Journal of Research and Development in Engineering Sciences (IJRDES ) | AI driven Mango Plant Disease Preduction and Management System

AI driven Mango Plant Disease Preduction and Management System

The Mango leaf diseases significantly restrain mango output as they affect yield and tree health. Mango leaf sooty mould disease is one of the diseases which affects the tree’s photosynthesis and general vigor quite severely. This research study looks into the use of deep learning models like the Residual Network with 50 stacks and ResNext50 for assessing that severity classification of mango leaf sooty mould disease. The model evaluates severity based on the dataset of 25,000 images obtained from different mango fields, as per the study. The overall accuracy achieved is 94.61% for the ResNext50 architecture through layer-wise parameter analysis, performance metrics, and confusion matrices. Model comparisons in the field reveal advantages across and between models. This research not only proves the use of DL in disease management but also paves the way for more applications in farming use. Automated mango leaf disease assessment is always bright white.

Research Type: Field Research
Paper Type: Field Research Paper
Vol.7 , Issue 1 , Pages: 9 - 14, Jan 2025
Published on: 08 Jan, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

N Divya

India

Viswam Engineering College

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*Corresponding Author: N Divya, divyareddy7990@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.