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Identification of Plant Diseases Using Multi-Level Classification Deep Model

Identification of Plant Diseases Using Multi-Level Classification Deep Model

Jitendra Vikram Tembhurne, Tarun Saxena, Tausif Diwan
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 21
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.309408
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MLA

Tembhurne, Jitendra Vikram, et al. "Identification of Plant Diseases Using Multi-Level Classification Deep Model." IJACI vol.13, no.1 2022: pp.1-21. http://doi.org/10.4018/IJACI.309408

APA

Tembhurne, J. V., Saxena, T., & Diwan, T. (2022). Identification of Plant Diseases Using Multi-Level Classification Deep Model. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-21. http://doi.org/10.4018/IJACI.309408

Chicago

Tembhurne, Jitendra Vikram, Tarun Saxena, and Tausif Diwan. "Identification of Plant Diseases Using Multi-Level Classification Deep Model," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-21. http://doi.org/10.4018/IJACI.309408

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Abstract

As plants are exposed to the various environmental elements, they are highly prone to many diseases which affect the crop yield and result in low productivity. Due to the lack of proper care and regular checking for any diseases in plants, severe consequences may be seen in a long-term basis on plants and environments. Agricultural productivity is one of the important factors on which the economy highly depends. Plant pathologists require a reliable and effective method to diagnose the disease effectively. Several physical methods and techniques have been applied to better predict and classify the plant disease. However, we need an automated method to identify and produce as accurate result as possible with minimum time. Previously, many machine learning models were developed, producing limited accuracy. But, using deep learning, improved performance can be achieved for classification of plant diseases. The authors propose the multi-level classification model for plant diseases detection. The accuracy achieved by the proposed model is 96.70%, which is higher than the other models.

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