Classification of seven Iranian wheat varieties using texture features

Khojastehnazhand, Mostafa; Roostaei, Mozaffar

doi:10.1016/j.eswa.2022.117014

Cited by 26 publications

(13 citation statements)

References 33 publications

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“…The Artificial Neural Network (ANN) model has also exhibited its proficiency in the works [8,[10][11][12]29] achieving accuracies ranging from 95% to 98.2%. Furthermore, de Medeiros et al [30] achieved a high accuracy of 97% by using the Linear Discriminant Analysis (LDA) model, albeit with a relatively small dataset.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of textures, we calculate 102 features using both the Gray-Level Run Length Matrix (GLRM) and Gray-Level Co-occurrence Matrix (GLCM) algorithms [40,41]. For the GLRM, we consider multiple distance and angle configurations, including distances of 1, 2, 3, and 4 units, and angles of 0, π/4, π/2, and 3π/4 radians, effectively capturing texture information across various spatial relationships.…”

Section: Image Descriptorsmentioning

confidence: 99%

“…Various articles, such as Velesaca [6], exemplify the potential of computer vision systems and machine learning algorithms for performing complex tasks related to seed quality control. Several studies utilize machine learning algorithms [7][8][9][10][11][12], while others employ deep learning models [13][14][15][16][17][18], all of which have yielded promising.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning

Merzougui,

Boulelouah,

Mokhtari

et al. 2024

ISI

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This research aimed to develop computer vision and machine learning models to improve durum wheat quality control in Algeria. Durum wheat is a vital cereal crop in Algeria used for many staple foods. Currently, quality control relies on manual evaluation which is too lengthy and tedious. To address this, models utilizing image processing and 200 image descriptors, including 102 texture features, 8 morphological features, and 90 colour features, were developed to automate classification of durum wheat species, varieties, and impurities. An optimized Support Vector Machine (SVM) model was implemented hyperparameters tuning. The models achieved exceptional performance, classifying durum wheat species with 99% accuracy, varieties with 95% accuracy, and impurities with 94% accuracy. This illustrates the significant potential of tailored computer vision and machine learning techniques to enable automated quality control. The models could be integrated into crop certification workflows, increasing productivity.

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Section: Related Workmentioning

confidence: 99%

Section: Image Descriptorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning

Merzougui,

Boulelouah,

Mokhtari

et al. 2024

ISI

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“…Third, texture features (TFs) can be extracted to describe the distribution of pixels within a region of interest (ROI). TFs were originally designed for image classification ( Haralick et al., 1973 ) and have since been used for classification of forest stands ( Coburn and Roberts, 2004 ), wheat phenology ( Zhou et al., 2023 ) and wheat seeds ( Khojastehnazhand and Roostaei, 2022 ). Therefore, they might also be beneficial when identifying elite wheat varieties directly as suggested by Garriga et al.…”

Section: Introductionmentioning

confidence: 99%

Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering

Camenzind,

2024

Front. Plant Sci.

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High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

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“…Each keyframe belonging to real-world video sequences is processed to extract features using Uniform LBP and the scale-invariant feature identification. Conceptually similar work was performed that used LBP for extracting seven types of wheat texture features in East Azerbaijan Province to accelerate the seed supply for cultivation in seed supply centres [20].…”

Section: Introductionmentioning

confidence: 99%

Local Texture Representation for Timber Defect Recognition based on Variation of LBP

Rahiddin¹,

Hashim²,

Salahuddin³

et al. 2022

IJACSA

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This paper evaluates timber defect classification performance across four various Local Binary Patterns (LBP). The light and heavy timber used in the study are Rubberwood, KSK, Merbau, and Meranti, and eight natural timber defects involved; bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of LBP feature sets were created by employing the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP in a phase of feature extraction procedures. Several common classifiers were used to further separate the timber defect classes, which are Artificial Neural Network (ANN), J48 Decision Tree (J48), and K-Nearest Neighbor (KNN). Uniform LBP with ANN classifier provides the best performance at 63.4%, superior to all other LBP types. Features from Merbau provide the greatest F-measure when comparing the performance of the ANN classifier with Uniform LBP across timber fault classes and clean wood, surpassing other feature sets.

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Classification of seven Iranian wheat varieties using texture features

Cited by 26 publications

References 33 publications

Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning

Improving the Approval Process for Durum Wheat Grain Quality in Algeria Using Computer Vision and Machine Learning

Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering

Local Texture Representation for Timber Defect Recognition based on Variation of LBP

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