Modeling shapes using uniform cubic B-splines for rice seed image analysis

Peralta, Caroline Natalie M.; Pabico, Jaderick P.; Mariano, Vladimir Y.

doi:10.1109/cce.2016.7562657

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…For each classifier, 50 seed samples were collected randomly as positive samples, the negative samples were collected from all other species so that total negative samples are equal 50 (in other words, 10 from each other species). To evaluate the performance, two criteria measures are defined in (4). The performance criteria are calculated by averaging over 10 runs.…”

Section: Resultsmentioning

confidence: 99%

“…More recent works [1]- [3] focused on rice seed variety classification. Commonly, shape descriptors of the seed samples are extracted, then the classifiers such as Random Forests [3], Neural Networks [1] or Cubic B-Splines shape model [4] are trained. An automatic machine-vision system includes several stages, in which the most important steps are image data collection, feature extraction (such as shape, size, color, and orientation etc.…”

Section: Related Workmentioning

confidence: 99%

“…Automatic rice seed inspection systems that employ machine vision addressing this challenge have been shown in previous works [1]- [3]. Commonly, shape descriptors of the seed samples are extracted through image processing and visionbased approaches [1], [3], [4]. The challenge in comparing and quantifying performance between these approaches, is that each one has been evaluated on different rice seed varieties.…”

Section: Introductionmentioning

confidence: 99%

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

Tachtatzis

Murray

et al. 2016

2016 IEEE RIVF International Conference on Computing &Amp; Communication Technologies, Research, Innovation, and Vision for The

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A conventional method to inspect the varietal purity of rice seeds is based on evaluating human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NIR) and Visible cameras are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shapebased features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tachtatzis

Murray

et al. 2016

2016 IEEE RIVF International Conference on Computing &Amp; Communication Technologies, Research, Innovation, and Vision for The

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“…In the literature, a wide range of computer vision approaches have been proposed to automate the nondestructive inspection of rice seeds [4] that commonly rely on conventional RGB cameras [5]- [8]. 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.…”

Section: Introductionmentioning

confidence: 99%

“…From the review in Table 1, it is not clear whether the differences in performance between existing techniques is caused by superior algorithms and the effectiveness of feature descriptors used, or, if this is simply due to differences in the inter-class or intra-class variation of species used in each study. That said, Kue et al [11] and Peralta et al [8] used relatively large number of species in their study having evaluated their method using 30 and 754 species and reported accuracy of up to 89.1% and 44.87% respectively. Their reported performance is not as strong as some of the other methods listed in the table, and this supports the hypothesis that other techniques do not necessarily utilise better algorithms or feature descriptors.…”

Section: Introductionmentioning

confidence: 99%

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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