Multi-Class Image Classification Model for Robusta Coffee Leaf Images Using TensorFlow Take 4

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Robusta Coffee Leaf Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains 1560 Robusta coffee leaf images with visible mites and spots for infection cases and images without such appearance for healthy cases. Also, the dataset includes labels regarding the health state (healthy and unhealthy) and the severity of the disease (leaf area with spots).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet121 architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take3, we will construct a CNN model using the InceptionV3 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take4 iteration, we will construct a CNN model using the ResNet50V2 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.16% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 51.28% on the validation dataset.

In iteration Take2, the DenseNet121 model’s performance achieved an accuracy score of 97.61% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 73.08% on the validation dataset.

In iteration Take3, the InceptionV3 model’s performance achieved an accuracy score of 97.84% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 71.15% on the validation dataset.

In this Take4 iteration, the ResNet50V2 model’s performance achieved an accuracy score of 97.82% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 73.72% on the validation dataset.

CONCLUSION: In this iteration, the ResNet50V2 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Robusta Coffee Leaf Images

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2 http://dx.doi.org/10.17632/c5yvn32dzg.2

A potential source of performance benchmarks: https://data.mendeley.com/datasets/c5yvn32dzg/2

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Robusta Coffee Leaf Images Using TensorFlow Take 3

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Robusta Coffee Leaf Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains 1560 Robusta coffee leaf images with visible mites and spots for infection cases and images without such appearance for healthy cases. Also, the dataset includes labels regarding the health state (healthy and unhealthy) and the severity of the disease (leaf area with spots).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet121 architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take3 iteration, we will construct a CNN model using the InceptionV3 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.16% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 51.28% on the validation dataset.

In iteration Take2, the DenseNet121 model’s performance achieved an accuracy score of 97.61% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 73.08% on the validation dataset.

In this Take3 iteration, the InceptionV3 model’s performance achieved an accuracy score of 97.84% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 71.15% on the validation dataset.

CONCLUSION: In this iteration, the InceptionV3 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Robusta Coffee Leaf Images

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2 http://dx.doi.org/10.17632/c5yvn32dzg.2

A potential source of performance benchmarks: https://data.mendeley.com/datasets/c5yvn32dzg/2

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Robusta Coffee Leaf Images Using TensorFlow Take 2

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Robusta Coffee Leaf Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains 1560 Robusta coffee leaf images with visible mites and spots for infection cases and images without such appearance for healthy cases. Also, the dataset includes labels regarding the health state (healthy and unhealthy) and the severity of the disease (leaf area with spots).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take2 iteration, we will construct a CNN model using the DenseNet121 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.16% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 51.28% on the validation dataset.

In this Take2 iteration, the DenseNet121 model’s performance achieved an accuracy score of 97.61% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 73.08% on the validation dataset.

CONCLUSION: In this iteration, the DenseNet121 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Robusta Coffee Leaf Images

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2 http://dx.doi.org/10.17632/c5yvn32dzg.2

A potential source of performance benchmarks: https://data.mendeley.com/datasets/c5yvn32dzg/2

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Robusta Coffee Leaf Images Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Robusta Coffee Leaf Images dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains 1560 Robusta coffee leaf images with visible mites and spots for infection cases and images without such appearance for healthy cases. Also, the dataset includes labels regarding the health state (healthy and unhealthy) and the severity of the disease (leaf area with spots).

In this Take1 iteration, we will construct a CNN model using a simple three-block VGG architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In this Take1 iteration, the baseline model’s performance achieved an accuracy score of 98.16% on the training dataset after 15 epochs. Furthermore, the final model achieved an accuracy score of 51.28% on the validation dataset.

CONCLUSION: In this iteration, the simple three-block VGG CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Robusta Coffee Leaf Images

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2 http://dx.doi.org/10.17632/c5yvn32dzg.2

A potential source of performance benchmarks: https://data.mendeley.com/datasets/c5yvn32dzg/2

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Land Use and Land Cover with Sentinel-2 Using TensorFlow Take 6

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Land Use and Land Cover with Sentinel-2 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset is part of a research study that addresses the challenge of land use and land cover classification using Sentinel-2 satellite images. The research project presented a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within a total of 27,000 labeled and geo-referenced images. The study project also provided benchmarks for this novel dataset with its spectral bands using deep Convolutional Neural Network (CNNs).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet121 architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take3, we constructed a CNN model using the EfficientNetB0 architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take4, we will construct a CNN model using the ResNet101 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take4, we constructed a CNN model using the ResNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take5, we constructed a CNN model using the VGG19 architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take6 iteration, we will construct a CNN model using the MobileNetV3Large architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.83% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 86.06% on the validation dataset.

In iteration Take2, the DenseNet model’s performance achieved an accuracy score of 99.92% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.59% on the validation dataset.

In iteration Take3, the EfficientNet model’s performance achieved an accuracy score of 99.89% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.83% on the validation dataset.

In iteration Take4, the ResNet model’s performance achieved an accuracy score of 99.70% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.20% on the validation dataset.

In iteration Take5, the VGG19 model’s performance achieved an accuracy score of 99.96% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.74% on the validation dataset.

In this Take6 iteration, the MobileNetV3Large model’s performance achieved an accuracy score of 99.70% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 94.06% on the validation dataset.

CONCLUSION: In this iteration, the MobileNetV3Large CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Land Use and Land Cover with Sentinel-2

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://github.com/phelber/eurosat

A potential source of performance benchmarks: https://github.com/phelber/eurosat

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Land Use and Land Cover with Sentinel-2 Using TensorFlow Take 5

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Land Use and Land Cover with Sentinel-2 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset is part of a research study that addresses the challenge of land use and land cover classification using Sentinel-2 satellite images. The research project presented a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within a total of 27,000 labeled and geo-referenced images. The study project also provided benchmarks for this novel dataset with its spectral bands using deep Convolutional Neural Network (CNNs).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take3, we constructed a CNN model using the EfficientNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take4, we constructed a CNN model using the ResNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take5 iteration, we will construct a CNN model using the VGG19 architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.83% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 86.06% on the validation dataset.

In iteration Take2, the DenseNet model’s performance achieved an accuracy score of 99.92% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.59% on the validation dataset.

In iteration Take3, the EfficientNet model’s performance achieved an accuracy score of 99.89% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.83% on the validation dataset.

In iteration Take4, the ResNet model’s performance achieved an accuracy score of 99.70% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.20% on the validation dataset.

In this Take5 iteration, the VGG19 model’s performance achieved an accuracy score of 99.96% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.74% on the validation dataset.

CONCLUSION: In this iteration, the VGG19 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Land Use and Land Cover with Sentinel-2

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://github.com/phelber/eurosat

A potential source of performance benchmarks: https://github.com/phelber/eurosat

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Land Use and Land Cover with Sentinel-2 Using TensorFlow Take 4

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Land Use and Land Cover with Sentinel-2 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset is part of a research study that addresses the challenge of land use and land cover classification using Sentinel-2 satellite images. The research project presented a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within a total of 27,000 labeled and geo-referenced images. The study project also provided benchmarks for this novel dataset with its spectral bands using deep Convolutional Neural Network (CNNs).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take3, we constructed a CNN model using the EfficientNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take4 iteration, we will construct a CNN model using the ResNet architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.83% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 86.06% on the validation dataset.

In iteration Take2, the DenseNet model’s performance achieved an accuracy score of 99.92% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.59% on the validation dataset.

In iteration Take3, the EfficientNet model’s performance achieved an accuracy score of 99.89% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.83% on the validation dataset.

In this Take4 iteration, the ResNet model’s performance achieved an accuracy score of 99.70% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.20% on the validation dataset.

CONCLUSION: In this iteration, the ResNet CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Land Use and Land Cover with Sentinel-2

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://github.com/phelber/eurosat

A potential source of performance benchmarks: https://github.com/phelber/eurosat

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Land Use and Land Cover with Sentinel-2 Using TensorFlow Take 3

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Land Use and Land Cover with Sentinel-2 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset is part of a research study that addresses the challenge of land use and land cover classification using Sentinel-2 satellite images. The research project presented a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within a total of 27,000 labeled and geo-referenced images. The study project also provided benchmarks for this novel dataset with its spectral bands using deep Convolutional Neural Network (CNNs).

In iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In iteration Take2, we constructed a CNN model using the DenseNet architecture and tested the model’s performance using a validation dataset (20%) set aside from the training images.

In this Take3 iteration, we will construct a CNN model using the EfficientNet architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In iteration Take1, the baseline model’s performance achieved an accuracy score of 98.83% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 86.06% on the validation dataset.

In iteration Take2, the DenseNet model’s performance achieved an accuracy score of 99.92% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 97.59% on the validation dataset.

In this Take3 iteration, the EfficientNet model’s performance achieved an accuracy score of 99.89% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 96.83% on the validation dataset.

CONCLUSION: In this iteration, the EfficientNet CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Land Use and Land Cover with Sentinel-2

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://github.com/phelber/eurosat

A potential source of performance benchmarks: https://github.com/phelber/eurosat

The HTML formatted report can be found here on GitHub.