Wheat grain classification by using dense SIFT features with SVM classifier

Olgun, Murat; Onarcan, Ahmet Okan; Özkan, Kemal; Işık, Şahin; Sezer, Okan; Özgi̇şi, Kurtuluş; Ayter, Nazife Gözde; Başçiftçi, Zekiye Budak; Ardıç, Murat; Koyuncu, Onur

doi:10.1016/j.compag.2016.01.033

Cited by 72 publications

(30 citation statements)

References 17 publications

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“…In a study by Olgun et al ,. a smart decision mechanism was developed to ensure the automatic and rapid classification of wheat grains in a manner similar to that used in our proposed study.…”

Section: Introductionmentioning

confidence: 98%

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Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

2019

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Background The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry

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Section: Introductionmentioning

confidence: 98%

“…In a study by Olgun et al 6 , a smart decision mechanism was developed to ensure the automatic and rapid classification of wheat grains in a manner similar to that used in our proposed study. The 88.33% accuracy was determined using traditional dense-scale invariant features (DSIFT) and an SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

2019

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“…SIFT exhibits invariance to image translation, rotation, and scaling transformations and good robustness to light changes, noise, and affine transformation [19,20]. SIFT is the main extraction method for feature points and has been applied to the matching of agricultural remote sensing visible and multispectral images, crop classification, and pest identification, and all these applications achieve good results [4,21,22].…”

Section: Introductionmentioning

confidence: 99%

Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

et al. 2019

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To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

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“…Similarly, in order to automatic fruit recognition in images, a Bag-of-Words along with a SVM classifier model was proposed by (Song et al, 2014). In another study by applying dense SIFT features and SVM, 40 different wheat grain varieties were classified with a satisfactory accuracy rate (Olgun et al, 2016). Pires et al (2016) introduced a high accuracy method based on image local descriptors and SVM classifier for detecting soybean disease.…”

Section: Introductionmentioning

confidence: 99%