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This paper introduces a specific approach for leaf classification based on Machine Learning (ML), Transfer Learning (TL), and Convolutional Neural Network (CNN). The proposed method consists of three stages, pre-processing, feature extraction, and classification.

LeadingIndiaAI/Swedish-Leaf-Dataset-Classification

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Plant-Leaf-Classification-using-Swedish-Leaf-Dataset

This is a Research Project worked on building the Swedish Leaf Classifier by using Transfer_Learning, ML Algorithms and DeepConv-Nets

About Project

Plant classification is one of the most foremost tasks for scientists, field guides, and others because plants have a significant role to play in the natural circle of life. Our problem statement revolves around three objectives:

  • Showing the usage of Transfer Learning(TL) in classification models.
  • Comparing the working of that model with different Machine Learning(ML) Algorithms
  • Designing a dedicated CNN model for this leaf classification problem.

Dataset

The dataset used for this experiment is the Swedish Leaf Dataset,available at https://www.cvl.isy.liu.se/en/research/datasets/swedish-leaf, which is a database of 15 different plant species with a total of 1125 leaf images.

Final Results

Experimental results showed that Random Forest (RF) achieved a classification accuracy of 98.83% against other ML algorithms with a combination of Grayscale images, HSV color moments, hu moments, and haralick features. The ResNet50 model gave us the best accuracy of 99.85% compared to other models. A CNN convolves learned features with input data and uses 2D convolutional layers. We have built our own CNN model from scratch and managed to reach an accuracy of 98.04%.

About

This paper introduces a specific approach for leaf classification based on Machine Learning (ML), Transfer Learning (TL), and Convolutional Neural Network (CNN). The proposed method consists of three stages, pre-processing, feature extraction, and classification.

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