Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8096
Keywords:
Crop leaf disease detection, tomato leaves dataset, convolution neural network, Inception network

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Punith Kumar
Department of computer science and engineering, University visvesvaraya college of engineering, Bangalore, India
H. N. Champa
Department of computer science and engineering University visvesvaraya college of engineering Bangalore, India

Abstract

Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore.


This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.

Article Details

How to Cite
Kumar, P. ., & Champa, H. N. . (2023). Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 238–247. https://doi.org/10.17762/ijritcc.v11i11s.8096
Issue
Vol. 11 No. 11s (2023)
Section
Articles

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