Combining near-infrared hyperspectral imaging and ANN for varietal classification of wheat seeds

Sharma, Apurva; Singh, Tarandeep; Garg, Neerja Mittal

doi:10.1109/icicict54557.2022.9917725

Cited by 12 publications

(5 citation statements)

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“…It is seen that in a few studies researchers prefer Convolutional Neural Network (CNN) [17][18][19]. Other than CNN, it is observed that other DL algorithms such as ANN are used too [20,21]. Also, BiLSTM [22] is used for classification with an accuracy score of 99.50%.…”

Section: Discussionmentioning

confidence: 99%

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Pervan-Akman¹,

Çolak²,

Özkan³

et al. 2023

Preprint

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Sunn pest is one of the most crucial noxious species in agriculture. As the sunn pest penetrates and makes damage to the grains, they can no longer be utilized to make bakery goods like bread, pasta, and other baked foods since they become unusable. Therefore, analysis and detection of damaged wheat grains are necessary for production to continue. We recommend that the dataset could be used for analysis and segmentation of the wheat grains, detection of the sunn pest damage condition, and classification of the 6 species of which the dataset consists. As mentioned, in our dataset, there are 6 different species of wheat, including Bezostaja, Müfitbey, Nacibey, Sönmez-2001, Tosunbey, and Ekiz, which are the species that were made in Türkiye. Our dataset differs from the others due to its various species and the condition, whether it is healthy or sunn pest damaged or broken wheat grain, and because the dataset is a multiclass one, it can be used for the classification of the varieties. On the other hand, when the sunn pest damage condition is taken into account, the dataset is suitable for the detection of the sunn pest damage. In addition to that, the dataset includes grains that touch each other which makes it more applicable to problems that arise in real-life. The dataset consists of 83 sunn pest damaged and 87 healthy wheat images. In addition, there are 170 images that contain 3565 wheat grains. As can be observed, the dataset contains a variety of wheat species with sunn pest affected which displays that the promised dataset is suitable for numerous machine learning problems such as classification, segmentation, and detection.

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Section: Discussionmentioning

confidence: 99%

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Pervan-Akman¹,

Çolak²,

Özkan³

et al. 2023

Preprint

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“…Although a slight spectral difference was found between conventional and waxy wheat in the 940–1650 nm range, HIT still may be used to determine the mixture levels (0–100 %) with standard errors of 9–13 percentage units, offering a potential advantage of HIT to sort wheat ( Delwiche et al, 2018 ). The NIR spectra (900–1700 nm) preprocessed by Savitzky-Golay second derivative (SG2) were investigated by HIT combined with ANN algorithm to classify 15 Indian wheat varieties, giving a best classification accuracy of 97.77 % ( Sharma et al, 2022 ), which is better than 93 % obtained by Tyagi et al (2022) who identified other 16 Indian wheat varieties by HIT with the same spectral range. The same range of raw spectra without preprocessing generated a slightly better classification performance (accuracy was 99.94 %) in classifying 40 different Turkish wheat cultivars ( Işık et al, 2022 ).…”

Section: Application Of Hit In Wheat Quality Evaluationmentioning

confidence: 96%

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Wang,

Ou,

et al. 2024

Food Chemistry: X

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“…A PLS-DA (Partial Least Squares Discriminant Analysis) model based on full-band spectral data of cotton seeds of the five cultivars was established to evaluate the influence of different preprocessing methods on the accuracy of cotton seed cultivar identification model. The PLS-DA algorithm is a supervised regression model-based PLS multivariate statistical analysis [41,42]. It utilizes the spectral data to construct a identification model for identification purposes.…”

Section: Machine Learningmentioning

confidence: 99%

“…For instance, a study on cotton seed cultivar identification found that the combination of SG (Savitzky-Golay) smoothing (with a seven-point quadratic filter) and normalization yielded the best results [4]. Sharma et al [41] confirmed that the model based on the SG2 preprocessing obtained the highest wheat seed cultivar identification accuracy. Additionally, a study on the identification of frost-damaged rice seeds using hyperspectral imaging and a deep forest model found that the Multiplicative Scatter Correction pre-processing was the most effective in increasing identification accuracy [51].…”

Section: Effects Of Different Preprocessingsmentioning

confidence: 99%

Cotton seed cultivar identification based on the fusion of spectral and textural features

Liu,

Guo,

et al. 2024

PLoS ONE

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The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm—1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800–1000 nm), red region (620–700 nm), and blue-green region (420–570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

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Combining near-infrared hyperspectral imaging and ANN for varietal classification of wheat seeds

Cited by 12 publications

References 0 publications

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review

Cotton seed cultivar identification based on the fusion of spectral and textural features

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