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Quantification of root-knot nematode infestation in tomato using digital image analysis

02/12/2021 - François-Xavier Branthôme
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.). RKN is the most common plant-parasitic nematode, causing significant yield and economic losses in tomato production and other agricultural crops and vegetables; the damage caused by RKN on crops is estimated to be USD 80–USD 110 billion per year. 

 RKN causes the formation of galls in the roots. It is a worm-like micro-fauna. RKN cause damage to plant roots and obstruct water and nutrient intake from the soil. Hence, RKN infestation can be mistaken for nutrient deficiency based on above-ground symptoms displayed by the plant. RKN damage can be confirmed with the presence of galls in the roots. Thus, ascertaining the quantity of RKN is crucial in the estimation of level of damage and extent of yield losses. The assessment of RKN population can be done on plants and soil whenever diagnosis is deemed important.

RKN must be accurately identified so that they can be quantified. However, the identification of nematodes is challenging because they have limited distinct features and require expertise. Traditionally, morphological characteristics were used to identify RKN, but the morphology of the RKN changes in each stage of the life cycle. In addition, the life cycle duration of RKN depends on environmental factors such as host plant and temperature.

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. Computer vision and image analysis techniques are applied in agriculture and the food industry for precision farming, weed detection and control, agricultural pattern analysis, and automated inspection of agricultural products. Monitoring of crop health and disease control, automatic crop harvesting, classification, quality testing of agricultural products, and monitoring of farmland are the major applications of computer vision technology in agricultural automation. Further, computer vision has been used in the detection and quantification of nematodes.

Numerous studies on nematodes with image analysis methods have been carried out; however, the detection and quantification of RKN using the microscopic image analysis approach has not been explored. This new study aims to develop a semi-automated method to discern and quantify RKN based on size using image analysis techniques to help growers prototype their biological characteristics for management purposes.

The proposed method estimates RKN size and enumerates it based on length and width. The length of RKN is computed using three methods. The contour arc (CA), thin structure (TS), and skeleton graph (SG) methods were implemented to automate the measurement of RKN. The skeleton graph structure was the most accurate in detecting RKN.
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 (one-way analysis of variance) 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 a ratio range between 10 and 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 minimize crop and vegetable losses.

Some additional information
Read the complete research at

Pun, Top & Neupane, Arjun & Koech, Richard. (2021). Quantification of Root-Knot Nematode Infestation in Tomato Using Digital Image Analysis. Agronomy. 11. 2372. 10.3390/agronomy11122372.






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