Performance Evaluation of ProThinNet23 and State-of-the-Art Deep Learning Models in Plant Leaf Disease Identification

For sustainable development in agriculture , there is a need to develop automated systems that can identify plant leaf diseases by capturing their images directly from the field. Such systems can enhance agricultural productivity and minimize crop losses. Recent advancements in the domain of machine learning and deep learning have focused on creating powerful hybrid models for the automatic detection of plant diseases, as these approaches offer higher accuracy.

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Publication Details

Journal
Journal of Informatics Education and Research
Published
2025-03-08
DOI
https://doi.org/10.52783/jier.v5i1.2312
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Performance Evaluation of ProThinNet23 and State-of-the-Art Deep Learning Models in Plant Leaf Disease Identification

Sathish Rao Bala
Journal of Informatics Education and Research
Smart Agriculture and AI
article

Performance Evaluation of ProThinNet23 and State-of-the-Art Deep Learning Models in Plant Leaf Disease Identification

Sathish Rao Bala
article en

Abstract

For sustainable development in agriculture , there is a need to develop automated systems that can identify plant leaf diseases by capturing their images directly from the field. Such systems can enhance agricultural productivity and minimize crop losses. Recent advancements in the domain of machine learning and deep learning have focused on creating powerful hybrid models for the automatic detection of plant diseases, as these approaches offer higher accuracy.

Journal of Informatics Education and ResearchVol. 5(1)
Openalex Percentile: Top 97%
Smart Agriculture and AI
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Performance Evaluation of ProThinNet23 and State-of-the-Art Deep Learning Models in Plant Leaf Disease Identification — Sathish Rao Bala · Journal of Informatics Education and Research (2025) | TGRS Research Map | TGRS