abhisheks008 · abhisheks008 · Jan 21, 2024 · Jan 19, 2024 · Jan 20, 2024 · Jan 20, 2024
diff --git a/Cassava Leaf Disease Classification using DL/README.md b/Cassava Leaf Disease Classification using DL/README.md
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+# <h1 align = "center"> Cassava Leaf Disease Classification using DL</h1>
+## Aim of the project: 
+### The project focuses on classification of different diseases of cassava leaves using various Deep Learning Algorithms.
+
+###  Libraries and Frameworks used:
+1. Pandas
+2. Numpy 
+3. Matplotlib
+4. Seaborn
+5. Tensorflow
+6. Keras
+7. sklearn
+8. glob
+9. OpenCV
+
+
+## Deep Learning Algorithms used:
+1. MobileNet
+2. ResNet
+3. DenseNet
+4. InceptionNet
+5. EfficientNet
+
+## Accuracy and training time comparison of all the Deep Learning Algorithms
+|                    |   Accuracy    |
+|--------------------|---------------|
+|     MobileNet      |     59%       |
+|      ResNet        |     60%       |  
+|     DenseNet       |     68%       |
+|    InceptionNet    |     61%       |
+|    EfficientNet    |     54%       |     
+
+# Representation of diseases of cassava leaves
+![EDA](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/diseases.png)
+
+# Counts of disease cases
+![values](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/bar%20plot.png)
+
+# Pie chart for the count of cases
+![ri](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/pie%20chart.png)
+
+# Orignal image vs grayscale image
+![ovsg](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/gray.png)
+
+# Original vs Resized image(224*224 pixels)
+![ovri](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/original%20vs%20res.png)
+
+
+# Accuracy and plots of all models
+
+## InceptionNetV2
+![inv2](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/inceptionNet_1.png)
+
+## DenseNet
+![densenet](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/denseNet.png)
+
+## ResNet50
+![resnet](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/ResNet50_1.png)
+
+## EfficientNet
+![effnet](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/efficientNet_1.png)
+
+## MobileNet
+![mnet](https://github.com/the-silent-geek/DL-Simplified/blob/a3ed67cd7eaae3e9c9c4e25c9316a484083387c3/Cassava%20Leaf%20Disease%20Classification%20using%20DL/images/mobileNetV2_1.png)
+
+# Conclusion
+DenseNet model performs better comparative to other models used on the above dataset.
diff --git a/Cassava Leaf Disease Classification using DL/dataset/README.md b/Cassava Leaf Disease Classification using DL/dataset/README.md
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+# Dataset
+
+The dataset used in this project is take from the Kaggle website.
+<br>
+<b>Dataset Link:- https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classification</b>
+<br>
+
+<br>
+This dataset consists of 21,367 labeled images collected during a regular survey in Uganda. Most images were crowdsourced from farmers taking photos of their gardens, and annotated by experts at the National Crops Resources Research Institute (NaCRRI) in collaboration with the AI lab at Makerere University, Kampala. This is in a format that most realistically represents what farmers would need to diagnose in real life.
+
+The task is to classify each cassava image into four disease categories or a fifth category indicating a healthy leaf.
+<br>
diff --git a/Cassava Leaf Disease Classification using DL/images/ResNet50_1.png b/Cassava Leaf Disease Classification using DL/images/ResNet50_1.png
diff --git a/Cassava Leaf Disease Classification using DL/images/bar plot.png b/Cassava Leaf Disease Classification using DL/images/bar plot.png
diff --git a/Cassava Leaf Disease Classification using DL/images/denseNet.png b/Cassava Leaf Disease Classification using DL/images/denseNet.png
diff --git a/Cassava Leaf Disease Classification using DL/images/diseases.png b/Cassava Leaf Disease Classification using DL/images/diseases.png
diff --git a/Cassava Leaf Disease Classification using DL/images/efficientNet_1.png b/Cassava Leaf Disease Classification using DL/images/efficientNet_1.png
diff --git a/Cassava Leaf Disease Classification using DL/images/gray.png b/Cassava Leaf Disease Classification using DL/images/gray.png
diff --git a/Cassava Leaf Disease Classification using DL/images/inceptionNet_1.png b/Cassava Leaf Disease Classification using DL/images/inceptionNet_1.png
diff --git a/Cassava Leaf Disease Classification using DL/images/mobileNetV2_1.png b/Cassava Leaf Disease Classification using DL/images/mobileNetV2_1.png
diff --git a/Cassava Leaf Disease Classification using DL/images/original vs res.png b/Cassava Leaf Disease Classification using DL/images/original vs res.png
diff --git a/Cassava Leaf Disease Classification using DL/images/pie chart.png b/Cassava Leaf Disease Classification using DL/images/pie chart.png
diff --git a/Cassava Leaf Disease Classification using DL/model/model.ipynb b/Cassava Leaf Disease Classification using DL/model/model.ipynb
diff --git a/Cassava Leaf Disease Classification using DL/requirements.txt b/Cassava Leaf Disease Classification using DL/requirements.txt
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+pandas
+numpy
+matplotlib
+glob
+tensorflow
+opencv
+scikit-learn
+seaborn
+glob