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.