Binary-Class Image Classification Model for Pistachio Identification Using TensorFlow Take 5

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. The industry needs new methods and technologies for separating and classifying pistachios to provide this efficiency. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 99.19% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 98.37%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Pistachio Identification Using TensorFlow Take 4

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. The industry needs new methods and technologies for separating and classifying pistachios to provide this efficiency. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The EfficientNetV2S model’s performance achieved an accuracy score of 95.93% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 97.44%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2S CNN model appeared suitable for modeling this dataset.

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Pistachio Identification Using TensorFlow Take 3

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. The industry needs new methods and technologies for separating and classifying pistachios to provide this efficiency. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The VGG19 model’s performance achieved an accuracy score of 92.20% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.34%.

CONCLUSION: In this iteration, the TensorFlow VGG19 CNN model appeared suitable for modeling this dataset.

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Pistachio Identification Using TensorFlow Take 2

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. The industry needs new methods and technologies for separating and classifying pistachios to provide this efficiency. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 97.96% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 94.87%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Pistachio Identification Using TensorFlow Take 1

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. The industry needs new methods and technologies for separating and classifying pistachios to provide this efficiency. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 97.50% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.97%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Pistachio Identification Using Python and AutoKeras

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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. To provide this efficiency, the industry needs new methods and technologies for separating and classifying pistachios. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

After 100 trials, the best AutoKeras model processed the training dataset with an accuracy score of 95.63%. When we processed the test dataset with the final model, the model achieved an accuracy score of 93.25%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Pistachio 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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. To provide this efficiency, the industry needs new methods and technologies for separating and classifying pistachios. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The average performance of the preliminary TensorFlow models achieved an accuracy benchmark of 90.80%. When we processed the test dataset with the final model, the model achieved an accuracy score of 90.23%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Model for Pistachio 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 Pistachio Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pistachio nut has an important place in the agricultural economy; the efficiency of post-harvest industrial processes is crucial to maintaining its economic value. To provide this efficiency, the industry needs new methods and technologies for separating and classifying pistachios. In this study, the research team aimed to develop a classification model different from traditional separation methods based on image processing and artificial intelligence techniques.

A computer vision system (CVS) has been developed to distinguish two species of pistachios with different characteristics that address additional market types. The research team took 2148 sample images for these two kinds of pistachios with a high-resolution camera. They applied image processing, segmentation, and feature extraction techniques to the images of the pistachio samples.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 91.49%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 92.78%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.16%.

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

Dataset Used: Pistachio Dataset

Dataset ML Model: Binary classification with numerical features

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

One source of potential performance benchmarks: https://doi.org/10.23751/pn.v23i2.9686

The HTML formatted report can be found here on GitHub.