Multi-Class Image Classification Model for Mango Varieties Grading 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 Mango Varieties Classification dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 94.63% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 61.67%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties Grading 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 Mango Varieties Classification dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 94.44% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 85.00%.

CONCLUSION: In this iteration, the TensorFlow ResNet50V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties Classification 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 Mango Varieties Classification dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 99.79% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.88%.

CONCLUSION: In this iteration, the TensorFlow DenseNet201 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties Classification 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 Mango Varieties Classification dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 99.31% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 81.88%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties Classification 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 Mango Varieties Classification dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 99.10% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 88.13%.

CONCLUSION: In this iteration, the TensorFlow ResNet50V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Dry Bean Identification Using Python and TensorFlow

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Dry Bean Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: There is a wide range of genetic diversity in dry beans, the most produced one among the edible legume crops in the world. Seed classification is essential for the marketing and production of sustainable agricultural systems. The primary objective of this research study is to provide a method for obtaining uniform seed varieties from crop production. The research team developed a computer vision system to classify seven different types of dry beans with similar features. For the classification model, the researchers used a high-resolution camera to gather 13,611 images of dry beans.

ANALYSIS: The average performance of the preliminary TensorFlow models achieved an accuracy benchmark of 92.64%. When we processed the test dataset with the final model, the model achieved an accuracy score of 92.07%.

CONCLUSION: In this iteration, TensorFlow appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Dry Bean Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2020.105507

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Dry Bean Identification Using Python and XGBoost

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Dry Bean Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: There is a wide range of genetic diversity in dry beans, the most produced one among the edible legume crops in the world. Seed classification is essential for the marketing and production of sustainable agricultural systems. The primary objective of this research study is to provide a method for obtaining uniform seed varieties from crop production. The research team developed a computer vision system to classify seven different types of dry beans with similar features. For the classification model, the researchers used a high-resolution camera to gather 13,611 images of dry beans.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 92.61%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 92.97%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.77%.

CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Dry Bean Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2020.105507

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Dry Bean Identification Using Python and TensorFlow Decision Forests

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Dry Bean Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: There is a wide range of genetic diversity in dry beans, the most produced one among the edible legume crops in the world. Seed classification is essential for the marketing and production of sustainable agricultural systems. The primary objective of this research study is to provide a method for obtaining uniform seed varieties from crop production. The research team developed a computer vision system to classify seven different types of dry beans with similar features. For the classification model, the researchers used a high-resolution camera to gather 13,611 images of dry beans.

ANALYSIS: The performance of the preliminary Random Forest model achieved an accuracy benchmark of 97.26% on the training dataset. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 97.35%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Dry Bean Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.compag.2020.105507

The HTML formatted report can be found here on GitHub.