Quantitative evaluation of  Heterodera avenae  females in soil and root extracts by digital image analysis

Grillo, Oscar; Andrés, María Fe; Moreno-Vázquez, Santiago

doi:10.1016/j.cropro.2015.12.010

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…There was a high correlation between the length of RKN measured by the manual method and that obtained using the machine method. 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.…”

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%

“…Similarly, cereal cyst nematodes (Heterodera avenae) and substrate particles were identified and discriminated against using image analysis [23]. This method used a scanner to acquire nematode images and different morphometric and textual features to characterize and discriminate nematodes and substrate particles.…”

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%

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

2021

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“…This procedure, also called rotation estimation (Picard and Cook 1984;Kohavi 1995), was applied, both to evaluate the performance and to validate any classifier. The validation procedure used here is the leave-one-out cross-validation (LOOCV) (Grillo et al 2016). It involves using a single case from the original sample set as the validation dataset and the remaining cases as the training set.…”

Section: Discussionmentioning

confidence: 99%

First finds of Prunus domestica L. in Italy from the Phoenician and Punic periods (6th–2nd centuries bc)

Ucchesu

Sarigu

Vais

et al. 2017

Veget Hist Archaeobot

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scanner and analysed by applying image analysis techniques to measure 26 morphometric features. By applying stepwise linear discriminant analysis, a morphometric comparison was made between the archaeological fruitstones of Prunus and the modern ones collected in Sardinia. These analyses allowed identification of 53 archaeological fruitstones as P. spinosa and 11 as P. domestica. Moreover, the archaeological samples of P. spinosa showed morphometric similarities in 92.5% of the cases with the modern P. spinosa samples currently growing near the Phoenician and Punic site. Likewise, the archaeological fruitstones identified as P. domestica showed similarities with the modern variety of P. domestica called Sanguigna di Bosa which is currently cultivated near the village of Bosa. Currently, these findings represent the first evidence of P. domestica in Italy during the Phoenician and Punic periods.

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“…Various methods have been developed to identify nematodes including morphology, molecular and differential host test [17]. RKN infestation can be determined based on the number of eggs in the roots [18,19], juvenile populations in the soil [20,21], the number of adult females in the roots [22,23], and root gall index and reproduction factor [24]. The number of egg mass and gall rating are also used for nematode infestation [25].…”

Section: Introductionmentioning

confidence: 99%

Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis

et al. 2022

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Root-knot nematodes (Meloidogyne spp.; RKN) are major plant-parasitic nematodes that cause significant loss to agricultural production. An accurate assessment of the RKN population density at a field level is crucial for decisions about the application of control measures to minimise yield losses. Traditionally, RKN populations are identified and counted by nematologists using a microscope. This method is a specialised, timeconsuming process and prone to errors. In our study, we investigated three semi-automated methods to detect and count RKN eggs using image analysis: contour arc (CA), skeleton structure (SS), and extreme point (EP). These methods were used to automate the length measurement of RKN eggs, and the results were compared with traditional methods of quantification. The EP method produced the highest correlation with the manual length measurement of RKN eggs. Further, these methods were used to detect and count RKN eggs to quantify low and highly cluttered images. We estimated the optimal range of the ratio of each method to detect and count RKN eggs. Overall, the EP method computed using mid-width of RKN eggs revealed better detection and counting of RKN eggs as compared to the SS and CA methods. A counting correlation up to R 2 = 0.905 was obtained. This study found that the difference between mid-width and the average width of RKN eggs and soil particles could be used to discriminate 70-80 % of soil particles. Our research thus contributes a new feature that can be used to discriminate or classify objects in object detection techniques.

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Quantitative evaluation of Heterodera avenae females in soil and root extracts by digital image analysis

Cited by 5 publications

References 26 publications

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

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

First finds of Prunus domestica L. in Italy from the Phoenician and Punic periods (6th–2nd centuries bc)

Detection and Quantification of Root-Knot Nematode (Meloidogyne Spp.) Eggs From Tomato Plants Using Image Analysis

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