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Deep learning-based classification of alfalfa varieties: A comparative study using a custom leaf image dataset.

Yonis Gulzar¹, Zeynep Ünal², Tefide Kızıldeniz²

¹Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.

|December 9, 2024

Summary

This summary is machine-generated.

Deep learning models accurately classify alfalfa varieties using image data. Transfer learning significantly boosts accuracy, with DenseNet121 and EfficientNetB3 achieving near-perfect results for plant classification.

Keywords:

Alfalfa plant Artificial Intelligence Comparative Deep Learning Model Evaluation Image classification Model Evaluation Plant Classification Transfer Learning

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Area of Science:

Agricultural Science
Computer Science
Machine Learning

Background:

Deep learning models enhance accuracy and efficiency in plant classification.
Accurate plant variety identification is crucial for agricultural applications.

Purpose of the Study:

To classify alfalfa plant varieties using deep learning techniques.
To compare the performance of various state-of-the-art deep learning models for alfalfa classification.

Main Methods:

A custom dataset of 1,214 images of three alfalfa varieties (Bilensoy-80, Diana, Nimet) was created.
Several deep learning models (MobileNetV3, InceptionV3, Xception, VGG19, DenseNet121, ResNet101, EfficientNetB3) were evaluated.
Models were tested with various hyperparameters, including learning rates, batch sizes, and dropout configurations.

Main Results:

Transfer learning generally resulted in higher test accuracies for alfalfa classification.
DenseNet121 achieved 1.0000 accuracy with transfer learning.
EfficientNetB3 achieved 0.9945 accuracy with both from-scratch and transfer learning methods.

Conclusions:

Transfer learning significantly enhances model performance in plant classification tasks.
Deep learning models, particularly DenseNet121 and EfficientNetB3, show high potential for accurate alfalfa variety identification.
The developed dataset serves as a valuable resource for future plant classification research.