Infected Area Segmentation and Severity Estimation of Grapevine Using Fuzzy Logic

Nagi, Reva; Tripathy, Sanjaya Shankar

doi:10.1007/978-981-13-8222-2_5

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…A crisp threshold (Cui et al, 2010;Gutiérrez et al, 2021), dividing the pixels of the discs into disease infection and the rest of the leaf using a fixed value, was used. Furthermore, a fuzzy threshold based on Nagi and Tripathy (2020), assigning a degree of infection to the pixels depending on the value of the pixels using membership functions within a range, was also used due to the variability of saturation values that represent the sporulation. For this purpose, the distribution of the thresholds obtained from all the discs was used to select three representative threshold ranges: from the first quartile to the median (th1, between 92 and 103), from the first quartile to the third quartile (interquartile range, th2, between 92 and 119) and from the minimum to the third quartile (th3, between 58 and 119).…”

Section: Segmentation Of Pre-processed Imagesmentioning

confidence: 99%

“…Moreover, infection ratings were also analysed discretely, taking into account severity as levels (Nagi and Tripathy, 2020). To do this, different levels of infection severity were defined: low, including ratings between 0 and 25 %; middle, comprising severity values between 26 and 50 %; and high, including severity ratings between 51 and 100 %.…”

Section: Visual Rating and Validationmentioning

confidence: 99%

“…Recently, this approach has been suggested to evaluate plant diseases (Mukherjee, 2020;Sibiya and Sumbwanyambe, 2019) and enhance image segmentation to locate diseased areas (Sekulska-Nalewajko and Goclawski, 2011). The application of a fuzzy threshold in segmentation has been suggested to improve disease quantification algorithms (Nagi and Tripathy, 2020;Nagi and Tripathy, 2021). These works pointed to the diversity of the use of fuzzy logic and the closeness of the results provided by the fuzzy logic to the expert's evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

et al. 2022

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Downy mildew is a major disease of grapevine. Conventional methods for assessing crop diseases are time-consuming and require trained personnel. This work aimed to develop and validate a new method to automatically estimate the severity of downy mildew in grapevine leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under indoor conditions. Computer vision techniques were applied for leaf disc location in Petri dishes, image pre-processing and segmentation of pre-processed disc images to separate the pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied to improve the segmentation of disc images, rating pixels with a degree of infection according to the intensity of sporulation. To validate the new method, the downy mildew severity was visually evaluated by eleven experts and averaged score was used as the reference value. A coefficient of determination (R2) of 0.87 and a root mean squared error (RMSE) of 7.61 % was observed between the downy mildew severity obtained by the new method and the visual assessment values. Classification of the severity of the infection into three levels was also attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that computer vision and fuzzy logic can be used to automatically estimate the severity of downy mildew in grapevine leaves. A new method has been developed and validated to assess the severity of downy mildew in grapevine. The new method can be adapted to assess the severity of other diseases and crops in agriculture.

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Section: Segmentation Of Pre-processed Imagesmentioning

confidence: 99%

Section: Visual Rating and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

et al. 2022

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“…Low Low 53 0.35 PLS-DA Disease severity estimation of downy mildew using computer vision techniques obtained similar results to expert evaluation. This severity estimation could consider the expert subjectivity, achieving a greater relationship between automatic and manual assessment [11]. The possibility to adopt new sensing technologies for detecting grapevine downy mildew and for evaluating disease severity gives new opportunities for disease assessment in other key commercial crops in agriculture.…”

Section: Chine Learningmentioning

confidence: 99%

“…Nowadays, the evaluation of this disease has been based mostly on visual assessment of leaves in the vineyards or histological analyses at the laboratory [9]. Computer vision and artificial intelligence could be very useful to recognise and quantify some diseases in grapevine [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine

et al. 2021

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Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are time-consuming and require expert personnel. This work investigates novel sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. In our methodology, machine vision is applied to assess downy mildew sporulation, while hyperspectral imaging is used to explore its potential capability towards early detection of this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew (Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R2) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation between downy mildew severity by computer vision and expert’s visual assessment. Furthermore, an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in grapevine.

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Infected Area Segmentation and Severity Estimation of Grapevine Using Fuzzy Logic

Cited by 2 publications

References 11 publications

Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

Artificial Intelligence and Novel Sensing Technologies for Assessing Downy Mildew in Grapevine

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