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

• Published in: Journal of the science of food and agriculture Vol. 104; no. 13; pp. 8332 - 8342
• Main Authors: Jin, Chen, Zhou, Xinyue, He, Mengyu, Li, Cheng, Cai, Zeyi, Zhou, Lei, Qi, Hengnian, Zhang, Chu
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.10.2024; John Wiley and Sons, Limited
• Subjects: 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
• DOI: 10.1002/jsfa.13668

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 collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super‐resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high‐resolution dataset demonstrated superior performance to that of the model using the low‐resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard–Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. CONCLUSION The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.