Detection of dead entomopathogenic nematodes in microscope images using computer vision

Kurtulmuş, Ferhat; Ulu, Tufan Can

doi:10.1016/j.biosystemseng.2013.11.005

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…The Naïve Bayes classifier and Linear Discriminant Analysis attained 96.1% and 93.5% accuracy, whereas the SG method achieved 97.3% [23]. Similarly, the detection rate was 87% in [19], whereas the proposed method outperformed it. The fluorescence-based imaging system revealed R 2 = 0.95, whereas the proposed CA, TS, and SF methods achieved better results than this method [21].…”

Section: Discussionmentioning

confidence: 91%

“…The one way-ANOVA test performed on count from CA, TS, and SG method found F-statistic = 4.440 and p-value = 0.004. In addition, three methods were investigated with ratio variation from [19][20][21][22][23][24][25][26][27][28][29][30]. The ratio between 10-35 was found to have the lowest number of misidentified and missed RKN.…”

Section: Resultsmentioning

confidence: 99%

“…The application was able to detect size and growth rate differences in terms of morphological phenotypes. Identification of dead and live Heterorhabditis bacteriophora nematodes was explored using computer vision [19]. The authors of [20] presented automated Wormscan to measure the size, mortality, and fecundity rate of C. elegans.…”

Section: Introductionmentioning

confidence: 99%

“…Netto [31] implemented otsu, ridler, and triangle methods to evaluate the effect of vegetation indices (VI) (ExG, ExGR, and NDI). Hakim [32] used adaptive local thresholding to binarise the image on WorMachine, otsu thresholding [19,21], and auto threshold implemented to detect and count entomopathogenic nematodes [33]. Triangle thresholding algorithms are used to phenotype crop root systems and estimate root length using ImageJ software [34].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis

2021

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Tomato is the most popular vegetable globally. However, in certain conditions, the vegetable is susceptible to plant parasites such as the root-knot nematode (RKN; Meloidogyne spp.). A proper detection method is required to identify RKN and eliminate related diseases. The traditional manual quantification of RKN using a microscope is a time-consuming and laborious task. This study aims to develop a semi-automated method to discern and quantify RKN based on size using an image analysis method. The length of RKN was assessed using three novel approaches: contour arc (CA), thin structure (TS), and skeleton graph (SG) methods. These lengths were compared with the manual measurement of RKN length. The study showed that the RKN length obtained by manual measurement was highly correlated to the length based on this method, with R2 of 0.898, 0.875, and 0.898 for the CA, TS, and SG methods, respectively. These approaches were further tested to detect RKN on 517 images. The manual and automated counting comparison revealed a coefficient of determination R2 = 0.857, 0.835 and 0.828 for CA, TS, and SG methods, respectively. The one-way ANOVA test on counting revealed F-statistic = 4.440 and p-value = 0.004. The ratio of length to width was investigated further at different ranges. The optimal result was found to occur at ratio range between 10–35. The CA, TS, and SG methods attained the highest R2 of 0.965, 0.958, and 0.973, respectively. This study found that the SG method is most suitable for detecting and counting RKN. This method can be applied to detect RKN or other nematodes on severely infected crops and root vegetables, including sweet potato and ginger. The study significantly helps in quantifying pests for rapid farm management and thus minimise crop and vegetable losses.

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Section: Discussionmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis

2021

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“…With the intensive development of the wood processing industry and the demanding of the high quality of the wood surface processing quality, the traditional artificial detection method has been difficult to meet the processing and production of wood products [4]. In recent years, machine vision technology has been applied in a wide range of fields because of its advantages of non-contact, high automation and high efficiency in the field of industrial defect detection, and has achieved good application effect [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Wood Veneer Defect Detection System Based on Machine Vision

Yang¹,

Wang²,

Wang³

et al. 2018

Proceedings of the 2018 International Symposium on Communication Engineering &Amp; Computer Science (CECS 2018)

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In view of the problems of low surface detection efficiency and high error detection rate in domestic wood skin, a corresponding detection system is designed to identify four types of wood skin defects such as dead knot, slipknot, hole and crack on the surface of the wood. Based on the image recognition of industrial cameras, the detection system can collect data in real time and quickly. Then the system can recognize and classify the collected images. The system is of great significance to the rational utilization of wood. The experimental results show that the detection system is safe and reliable, and has high accuracy.

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