Classification of Sweet Potato Variety using Convolutional Neural Network
| Autor: | Dexter I. Mercurio, Alexander A. Hernandez |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
biology business.industry Computer science Cash crop media_common.quotation_subject 04 agricultural and veterinary sciences Ipomoea biology.organism_classification Machine learning computer.software_genre 040401 food science 01 natural sciences Convolutional neural network Variety (cybernetics) 0404 agricultural biotechnology Production (economics) Quality (business) Artificial intelligence business Practical implications computer 010606 plant biology & botany media_common |
| Zdroj: | ICSET |
| DOI: | 10.1109/icsengt.2019.8906329 |
| Popis: | Sweet potato (Ipomoea batatas) from being ‘a poor man’s crop,’ sweet potatoes it is now viewed as a good with high business assets also significance for a multitude of reasons. Due to its versatility and high nutritional value, it is now considered a cash crop, making it one of the fastest growing commodities on the market. However, variety classification on this commodity is considerably significant to ensure the quality of products before reaching production. This study aims to present a sweet potato classification using convolutional neural network method. The results of the study show that the convolutional neural network achieves 96.33 percent accuracy in classifying sweet potato variety. Thus, a convolutional neural network is appropriate in the classification of sweet potatoes. This paper could bring forward an approach of sweet potato classification. Practical implications and research directions are presented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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