Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods

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Yousefvand A.; AMIRI PARIAN J.

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Journal: JOURNAL OF AGRICULTURAL MACHINERY Year:2021 | Volume: | Issue: Page(s): 317-329

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Title

Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods

Author(s)

Yousefvand A. | AMIRI PARIAN J. | Issue Writer Certificate

Keywords

Color featuresQ1

Image processingQ2

Morphology featuresQ1

Walnut treeQ1

Abstract

Introduction: Control of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has been proposed as an approach to determine the extent and type of damage to different products in farms and gardens. The aim of this study was to design an algorithm based on the processing of walnut leaf images under natural light conditions in order to provide a rapid and non-destructive detection of diseases for the protection of trees using imaging methods. In this research, the possibility of detecting Anthracnose disease was investigated by processing walnut leaf images. The disease was detected using in situ images taken from the leaves to provide the basis for designing application software on smart mechatronic systems. Materials and Methods: Images of leaves on Walnut trees were taken under outdoor light conditions. Color and morphological properties extracted from the images were used to detect the pest on the leaves. Gnomonia leptostyla disease diagnostic algorithm was based on process of color and morphological characteristics, leaves background and disease-stained spots. The range of changes in R, G, and B indices was obtained in histograms and then two-dimensional spaces were analyzed statistically on GR, GB, and BR planes. All points from these regions were used as statistical samples, for which bivariate regressions of GR, GB, and BR were obtained as y = b0 + b1x. Segments containing anthracnose spots from the leaves were segregated by extracting the coordinates of the points in each side on the RGB color space cube. Finally, anthracnose content was detected based on the number of spots detected by the algorithms. The percentage of contamination was used to determine the amount of contamination in each imaged area. Results and Discussion: Examination of the colored spaces indicated that the domain of the anthracnose color components on the GR side has nothing in common with the color components of the leaves. The analysis of color space data revealed that the leaves and anthracnose were more distinguishable on the GB and RB sides, respectively. According to the histogram of the HSV color space, anthracnose spots were isolated from the leaves by determining the H range. In the evaluation of the proposed method for diagnosis of anthracnose, the infection severity calculated by the algorithm with the true infection intensity. T-test results for comparing the mean of the two infection intensity samples showed no significant differences between the two groups at 1% probability level. Conclusions: The evaluation of the proposed method showed a 98% segregation accuracy for G. leptostyla detection method. Based on the results, the proposed method for detecting anthracnose spots is suitable for determining the contamination severity in the imaged areas.

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APA: Copy

Yousefvand, A., & AMIRI PARIAN, J.. (2022). Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods. JOURNAL OF AGRICULTURAL MACHINERY, 11(2 (22) ), 317-329. SID. https://sid.ir/paper/394649/en

Vancouver: Copy

Yousefvand A., AMIRI PARIAN J.. Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2022;11(2 (22) ):317-329. Available from: https://sid.ir/paper/394649/en

IEEE: Copy

A. Yousefvand, and J. AMIRI PARIAN, “Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 11, no. 2 (22) , pp. 317–329, 2022, [Online]. Available: https://sid.ir/paper/394649/en

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