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Applying Machine Learning Clustering and Classification to Predict Banana Ripeness States and Shelf Life


 
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1. Title Title of document Applying Machine Learning Clustering and Classification to Predict Banana Ripeness States and Shelf Life
 
2. Creator Author's name, affiliation, country Nandan Thor; Master of Science, Industrial Engineering Candidate, California Polytechnic State University, San Luis Obispo; United States
 
3. Subject Discipline(s) Industrial Engineering: Computer Vision; Machine Learning
 
3. Subject Keyword(s) agriculture; food waste; machine learning classifier; machine learning clustering
 
3. Subject Subject classification Shelf-life classification; determining ripeness states
 
4. Description Abstract

Food waste accounts for over $15 billion annually. Not only is food waste a financial problem, it is also an ethical problem. The use of machine learning algorithms for clustering and classification provide an opportunity to help reduce food waste. K-Means clustering is proposed to determine banana ripeness states and the Decision Tree Classifier algorithm is proposed to classify banana shelf-life. An experiment is undertaken to provide data by imaging bananas and extracting color features using computer vision. The resultant data is then clustered to determine banana ripeness states. The states are used to determine the end-point of data collection. Seven different machine learning classification algorithms are tested to classify fruit shelf-life. The most accurate classifier is the Decision Tree Classifier which has an accuracy around 52%. The combination of machine learning algorithms and big data analysis becomes a powerful tool in working to reduce food waste.

 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) Jose Macedo, California State Polytechnic Universty: San Luis Obispo
 
7. Date (YYYY-MM-DD) 2017-02-20
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type Machine Learning
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://scientific.cloud-journals.com/index.php/IJAFST/article/view/Sci-533
11. Source Journal/conference title; vol., no. (year) International Journal of Advanced Food Science and Technology; Published Papers
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.) Bananas
 
15. Rights Copyright and permissions

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