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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Related Experiment Video

Updated: Jun 5, 2025

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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

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.