Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Abstract

Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

Plant phenotyping relevance

種子の品種分類を目的としたハイパースペクトル画像と深層転移学習の手法開発・評価が研究の中心であり、育種・遺伝資源スクリーニングに再利用可能な表現型取得ワークフローを扱うため。

Code and data availability

The paper's hyperspectral seed datasets (Pea source dataset and rice/oat/wheat/cotton target datasets) and trained deep transfer learning models are paper-specific assets, but no public deposit or authors' URL is provided. The data availability statement only promises data from the authors upon request. The ehu.eus and

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