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

Gauri Mane¹, Raghav Bhise¹, Rutuja Kadam²

¹Department of Computer Science Engineering - Artificial Intelligence and Machine Learning, Vishwakarma Institute of Information Technology, Pune, India.

|March 4, 2026

Summary

Deep learning frameworks were evaluated for precision agriculture. DenseNet121 showed the best performance in real-world field conditions, making it a dependable choice for agricultural applications.

Keywords:

CNN models Controlled Environment Deep learning Image classification On-Field

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Deep learning is crucial for advancing precision agriculture.
Evaluating deep learning frameworks is essential for optimizing agricultural technologies.

Purpose of the Study:

To compare the performance of four deep learning frameworks (MobileNetV2, EfficientNetB0, ResNet50V2, DenseNet121).
To identify the most suitable framework for precision agriculture applications under controlled and field conditions.

Main Methods:

A custom dataset was used to evaluate MobileNetV2, EfficientNetB0, ResNet50V2, and DenseNet121.
Frameworks were tested in both controlled laboratory settings and real-world field environments.

Main Results:

MobileNetV2 and ResNet50V2 performed best in controlled environments, showing robustness to variations.
DenseNet121 achieved superior accuracy and F1-score in field environments.
EfficientNetB0 underperformed, highlighting limitations of lightweight models in noisy datasets.

Conclusions:

DenseNet121 is identified as the most reliable deep learning model for agricultural applications.
Future work will focus on adapting DenseNet121 for enhanced performance across diverse agricultural conditions.