ItemArticle

A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture

Title
A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture
Creator
Pawaskar, Praveen; Yogish, H.K.; Pakruddin, B.; Deepa, Y.
Description
Background: Agriculture sustains human life by providing food, raw materials and employment opportunities. However, climate change and resource limitations pose significant challenges to crop production. AI-driven smart farming has emerged as a solution to enhance agricultural efficiency, with Explainable AI (XAI) improving transparency in decision-making. Innovations such as smart sensors and automated systems have benefited key agricultural sectors, including crops, forestry, livestock and aquaculture. Turmeric, valued for its medicinal and economic significance, requires careful monitoring to combat diseases like leaf spot and leaf blotch, which can impact yield and quality. Methods: This study introduces turmeric net, a Convolutional Neural Network (CNN)-based model leveraging transfer learning to detect and classify turmeric leaf diseases. The dataset used consists of 791 original images and 3,702 augmented images obtained from mendeley data, categorized into four classes: healthy leaf, dry leaf, leaf blotch and rhizome rot. The model development was carried out using TensorFlow, with ResNet50V2 as a baseline for comparison. The models were trained on processed image data, incorporating augmentation techniques to improve robustness and generalizability. Result: The accuracy of both models was evaluated. ResNet50V2 achieved an accuracy exceeding 99%, demonstrating high effectiveness in disease classification. Meanwhile, TurmericNet attained a competitive accuracy of 98%, making it a reliable alternative for turmeric disease identification. These results indicate that deep learning-based models can significantly aid in early disease detection, providing farmers with a valuable tool to enhance crop management and productivity. 2025 , Agricultural Research Communication Centre. All rigths reserved.
Source
Indian Journal of Agricultural Research;Volume;59;Issue;9;pp.1395-1403
Date
01-01-2025
Publisher
Agricultural Research Communication Centre
Subject
Agriculture; Classification; Disease detection; Turmeric
Coverage
Pawaskar P., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India, School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Yogish H.K., Department of Computer Science and Engineering, MS Ramaiah Institute of Technology, Bengaluru, Visvesvaraya Technological University, Karnataka, Belagavi, 590 018, India; Pakruddin B., School of Computer Science and Engineering and Information Science and Engineering, Presidency University, Karnataka, Bengaluru, 560 064, India; Deepa Y., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, 560 029, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 3678245;
Format
online
Language
English
Type
Article

Citation

Pawaskar, Praveen; Yogish, H.K.; Pakruddin, B.; Deepa, Y., “A Novel and Efficient Deep Learning Models for Assessing AIs Impact on Disease Diagnosis in Agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23367.

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