Spatial and spectral features utilization on a Hyperspectral imaging system for rice seed varietal purity inspection

Vu, Hai; Tachtatzis, Christos; Murray, Paul; Harle, D.A.; Dao, Thiên-My; Le, Thi‐Lan; Andonović, Ivan; Marshall, Stephen

doi:10.1109/rivf.2016.7800289

Cited by 15 publications

(16 citation statements)

References 11 publications

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“…4 illustrates output of the spatial feature extraction for a sample image that contains 48 seeds. It is noted that because the spatial features are extracted using the high spatial resolution images, they are expected to be more accurate than those reported in [2], [15] where the spatial features are extracted from the HSI system which has lower spatial resolution.…”

Section: Spatial Feature Extractionmentioning

confidence: 95%

“…However, four cultivars in [9] were hybridized from other species, therefore, it is unclear how the inter/intra class varies among them. Recently, work in [2] and [15] explored different feature combination schemes: spectral and texture features; morphological, texture and spectral features; and morphological and texture features while seeking the optimal feature combination. The highest accuracy in [15] was obtained when using combined spectral, morphological and texture features (91.67%, with four polished rice species).…”

Section: Related Workmentioning

confidence: 99%

“…The highest accuracy in [15] was obtained when using combined spectral, morphological and texture features (91.67%, with four polished rice species). A classification accuracy of 84% was achieved in [2] utilising a combination of spectral and spatial features on a dataset of six rice seed species.…”

Section: Related Workmentioning

confidence: 99%

“…Fig. 5 clearly illustrates the advantages of the proposed segmentation method compared to the conventional work (e.g., in [2], [15]) where the seed segments are extracted directly from the low spatial resolution images collected by HSI system. When the seeds are segmented using only the HSI data, in many cases, the segments include some pixels at boundary regions of the shadow rather than the pure spectra of the seeds themselves.…”

Section: E Spectral Feature Extractionmentioning

confidence: 99%

“…These approaches extract appearance-based features from the seeds such as shape descriptors, texture or colours and train models using techniques from machine learning to discriminate between species. The advent of Hyperspectral Imaging (HSI) provides an alternative sensing modality with improved discrimination performance [9] and more recent approaches combine appearance and spectral features [3], [10], [2] to increase model robustness even further.…”

Section: Introductionmentioning

confidence: 99%

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Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images

Fabiyi

Tachtatzis

et al. 2020

IEEE Access

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Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes. INDEX TERMS Hyperspectral imaging, rice seed variety, spatio-temporal feature fusion. JINCHANG REN (Senior Member, IEEE) received the B.E. degree in computer software, the M.Eng. degree in image processing, and the D.Eng. degree in computer vision from Northwestern Polytechnical University, Xi'an, China, and the Ph.D. degree in electronic imaging and media communication from the

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Section: Spatial Feature Extractionmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: E Spectral Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images

Fabiyi

Tachtatzis

et al. 2020

IEEE Access

Self Cite

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Crop Seeds Classification Using Traditional Machine Learning and Deep Learning Techniques: A Comprehensive Survey

Kumar,

Aydav,

Minz

2024

SN COMPUT. SCI.

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Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

265

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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Spatial and spectral features utilization on a Hyperspectral imaging system for rice seed varietal purity inspection

Cited by 15 publications

References 11 publications

Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images

Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images

Crop Seeds Classification Using Traditional Machine Learning and Deep Learning Techniques: A Comprehensive Survey

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

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