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Plant Species Identification
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README.md

README.md

Automated Plant Species Identification

This repository presents results of our work to predict plant species based on the image of leaf using deep learning. The results are available in the Notebook

Implementation

We implemented transfer learning using Alexnet with PyTorch. The following hyperparameters provided best accuracy of 91.7%.

Arch = 'alexnet'; Batch = 32; Hidden_units = 4096; Epochs = 200; Dropout = 0.5; Learning Rate = 0.01, Optmizer = SGD, Momentum = 0.9

Results

Following Graphs show Model Accuracy for Training and Testing Phase; Model Loss for Training and Testing Phase; and Computation Time for Training the model and Training plus Tresting the model.

Model

Sanity Check

Output

Dataset

Our experimental dataset has following 11 types of plants.

Folder ID Plant Name
1 Acalypha hispida [EUPHORBIACAE]
2 Bauhinia coccinea [FABACEAE]
3 Calotropis gigantea [APOCYNACEAE]
4 Clitoria ternatea [FABACEAE]
5 Dillenia suffruticosa [DILLENIACEAE]
6 Ficus deltoidea [MORACEAE]
7 Melastoma beccarianum [MELASTOMATACEAE]
8 Melastoma malabathricum [MELASTOMATACEAE]
9 Melastoma malabathricum var alba [MELASTOMATACEAE]
10 Passiflora foetida [PASSIFLORACEAE]
11 Petrea volubilis [VERBENACEAE]

Dataset Size

We created dataset of 740 images from 11 plants and the dataset was divided into 596 images for training the model and 144 images used for testing the model.

Feedback

Please submit your feedback to Nagender Aneja. Please write an email (nagender.aneja@ubd.edu.bn) if you are interested to impement the model in a mobile app or web app. We welcome people and organization who can provide more data on plants from different countries to join this project.

Project Members

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