Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Yang, Yong; Chen, Jianping; He, Yong; Liu, Feng; Feng, Xuping; Zhang, Jinnuo

doi:10.1039/d0ra06938h

Cited by 12 publications

(5 citation statements)

References 41 publications

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“…The authors also pointed out that the advantage of deep models was not in dealing with small datasets. Yang et al employed a self-built CNN model to identify seed vitality [ 20 ]. And the model accuracy was better than ResNet18 with a deeper network structure, which verified that simple model structure can also handle information-rich spectral data.…”

Section: Resultsmentioning

confidence: 99%

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Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network

Feng

Fang

et al. 2023

Plant Methods

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Background Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object. Results To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year’s classification with fine-tuning and met with 94.8% accuracy. Conclusions The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.

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Section: Resultsmentioning

confidence: 99%

“…As a new study direction in machine learning, deep learning can automatically learn features through computers and has excellent feature extraction capabilities [ 20 – 26 ]. Convolutional Neural Network (CNN) is one of the typical and commonly used models and has been gradually applied to spectral analysis recently [ 27 – 29 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network

Feng

Fang

et al. 2023

Plant Methods

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“…A total of 54 color, shape, and texture features for a single seed were extracted using Phenoseed (a software program developed by our lab and Nanjing AgriBrain Big Data Technology Co, Ltd.). The dataset of two categories was randomly divided into the training set and test set at the ratio of 3:1, to build a model with excellent generalization and robustness [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

Wen

Cheng

et al. 2022

Plant Methods

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Background Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

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“…Transfer learning is a method for improving the performance of deep learning models on hyperspectral image data by transferring relevant knowledge from a source domain to a target domain, where training and test data exist in different feature spaces [14]. Yang et al [15] collected hyperspectral images of two different varieties of rice seeds and recorded their germination rate as an index to evaluate seed vitality. First, rice seeds of two different varieties were divided into source domain and target domain according to their varieties.…”

Section: Introductionmentioning

confidence: 99%

ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images

Zhao,

Liu,

Que

et al. 2023

Sensors

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Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model’s generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability.

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Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Abstract: Rice seed vigor plays a significant role in determining the quality and quantity of rice production.

Cited by 12 publications

References 41 publications

Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network

Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images

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