PriBeL-Net: Extending betel leaf dataset with CNN-based image classification.

Gauri Mane, Raghav Bhise, Rutuja Kadam, Gagandeep Kaur, Gitanjali Shinde, Grishma Bobhate, Sonal Fatangare

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Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Gauri ManeDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.

Raghav BhiseDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.

Rutuja KadamDepartment of Computer Engineering, MIT, Academy of Engineering, Alandi, Pune, India.

Gagandeep KaurSymbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.

Gitanjali ShindeDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Technology, Pune, India.

Grishma BobhateDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Technology, Pune, India.

Sonal FatangareDepartment of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Technology, Pune, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning is core to precision agriculture. In this research, the authors compare four deep-learning frameworks: MobileNetV2, EfficientNetB0, ResNet50V2, and DenseNet121, on a custom dataset under controlled and on-field environments. Whereas MobileNetV2 and ResNet50V2 gave the best results on the controlled setup, robust to light variations, backgrounds, and different leaf orientations, DenseNet121 gave better results in field environments with good accuracy and F1-score. However, EfficientNetB0 was not up to the mark, implying the restrictions of light-weight models while working with noisy, real-world datasets. With such implications, DenseNet121 is reported to be the most dependable candidate for application in agriculture. In the next phase, adaptation and rearrangement of DenseNet121's architecture and parameters will be taken up to better its performance and its ability for adaptation under diverse agricultural conditions.

Indexed as

CNN modelsControlled EnvironmentDeep learningImage classificationOn-Field

Identifiers

PMID41778255

DOI10.1016/j.mex.2026.103828

PMCPMC12950366

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.