A novel method combining deep learning with the Kennard–Stone algorithm for training dataset selection for image‐based rice seed variety identification

BACKGROUND Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice‐breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. RESULTS This study col...

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Veröffentlicht in:	Journal of the science of food and agriculture Jg. 104; H. 13; S. 8332 - 8342
Hauptverfasser:	Jin, Chen, Zhou, Xinyue, He, Mengyu, Li, Cheng, Cai, Zeyi, Zhou, Lei, Qi, Hengnian, Zhang, Chu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester, UK John Wiley & Sons, Ltd 01.10.2024 John Wiley and Sons, Limited
Schlagworte:	Algorithms Artificial neural networks Classification Color imagery Datasets Deep Learning Feature extraction Image enhancement Image processing Image Processing, Computer-Assisted - methods Image resolution Kennard–Stone algorithm Machine learning Neural networks Neural Networks, Computer Oryza - classification Plant breeding Planting Rice rice variety classification Seeds - chemistry training dataset selection Unsupervised learning image processing deep learning rice variety classification Kennard–Stone algorithm training dataset selection
ISSN:	0022-5142, 1097-0010, 1097-0010
Online-Zugang:	Volltext
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