Discriminating Healthy Wheat Grains from Grains Infected with <i>Fusarium graminearum</i> Using Texture Characteristics of Image-Processing Technique, Discriminant Analysis, and Support Vector Machine Methods

Abbaspour‐Gilandeh, Yousef; Ghadakchi-Bazaz, Hamed; Davari, Mahdi

doi:10.1515/jisys-2018-0430

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…They did this by utilizing SVM and DA algorithms. (Abbaspour-Gilandeh et al, 2020). The obtained results showed that classification using SVM method is better than non-linear SVM, having a 100% performance accuracy.…”

Section: Resultsmentioning

confidence: 89%

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Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Usman,

Mati,

Naganna

et al. 2024

Preprint

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The need for qualitative determination of mycotoxins in food samples is of paramount importance in designing procedures for their prevention, as well as understanding their adverse effects on humans and animals. In this article, a machine learning technique, specifically support vector regression (SVR) hyphenated with two metaheuristic algorithms - Harris-hawks optimization (HHO) and Particle Swarm Optimization (PSO) models (i.e., SVR-HHO and SVR-PSO) was used to forecast the chromatographic behaviour of various classes of mycotoxins in food samples. Three different metrics were employed to anticipate the model's performance: mean square error (MSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency (NSE). The simulation results showed that the M3 input variable combination demonstrated higher performance accuracy than M1 and M2 with both SVR and its hybridized versions (SVR-HHO and SVR-PSO) during both the training and testing stages. In general, the hybridized model, based on the used assessment measures, SVR-HHO performed better in the training and testing phases than the other two data-driven techniques. Overall, the results show that employing chromatographic techniques, machine learning and metaheuristic approaches can both accurately predict the qualitative characteristics of mycotoxins in food samples.

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Section: Resultsmentioning

confidence: 89%

“…The collected findings exhibited that, in contrast to the other models used, classification using the linear SVM technique performs better. (Abbaspour-Gilandeh et al, 2020). The relative performance analysis of MLR and ANN models for the forecastion of cheese's overall quality was published by (Ge et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Usman,

Mati,

Naganna

et al. 2024

Preprint

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“…Agarwal et al [23] utilized not one but three different ML algorithms; Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). SVM was used once again [24] and achieved an accuracy score of 100%. Khatri et al [25] used multiple ML algorithms which are KNN, CART, Ensemble, and NB.…”

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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“…Red-green-blue imaging was widely employed to detect FHB-infected and FHB-damaged kernels ( Jaillais et al, 2015 ; Cambaza et al, 2019 ; Abbaspour-Gilandeh et al, 2020 ), but few studies explored this technique to detect FHB on spikes. Huang et al (2020) proposed an FHB diagnostic model of disease severity based on the fusion of RGB and spectral imaging.…”

Section: Discussionmentioning

confidence: 99%

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat

et al. 2021

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Wheat is one of the world’s most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike’s temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike’s temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.

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Discriminating Healthy Wheat Grains from Grains Infected with Fusarium graminearum Using Texture Characteristics of Image-Processing Technique, Discriminant Analysis, and Support Vector Machine Methods

Cited by 6 publications

References 11 publications

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Emerging Harris Hawks Optimization for the Environmental Prediction of Mycotoxins in Food-Virtual Water Samples: A Comparative Study of Nature-Inspired Algorithms

Sunn Pest Damaged and Healthy Wheat Grains Dataset Across Different Species

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat

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