Severity Estimation of Grapevine Diseases from Leaf Images Using Fuzzy Inference System

Nagi, Reva; Tripathy, Sanjaya Shankar

doi:10.1007/s40003-021-00540-4

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Furthermore, a segmentation approach helped models learn relevant disease features within images, thus making them applicable for testing under field conditions. Recently, OTSU threshold color segmentation was used in fuzzy inference systems for identifying diseased regions with respect to the leaf area to calculate the percentage of infection [16]. However, OTSU thresholding in Fiji software was inefficient when working with large datasets as the hue, saturation, and brightness (HsB) settings in each image needed to be manually modified until the desired area turned black [17].…”

Section: Introductionmentioning

confidence: 99%

Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

2022

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Plant diseases lead to severe losses in crop yield worldwide. The conventional approach for diagnosing diseases relies on manual scouting. In recent years, advances in convolutional neural networks have motivated the use of deep learning-based computer vision for automatically identifying plant diseases. Although image classification techniques are commonly used for analyzing agricultural data, their use for accurately identifying diseased regions corresponding to different disease types on individual plant leaves is limited. In this study, Simple Linear Iterative Clustering (SLIC) segmentation was used on corn leaf images from the PlantVillage and CD&S datasets to create super-pixels, a cluster of pixels representing a region of interest on a corn leaf. The VGG16, ResNet50, DenseNet121, Xception, and InceptionV3, pre-trained deep learning models were utilized to identify diseased regions corresponding to five super-pixel classes (healthy, northern leaf blight (NLB), gray leaf spot (GLS), common rust, and background) for the PlantVillage dataset and four super-pixel classes (NLB, GLS, northern leaf spot, and background) for the CD&S dataset, on corn leaves. After setting the spatial proximity value (sigma) for SLIC segmentation to five, a total of 100 models were trained by using different numbers of segments per image (five and fifteen) in the SLIC algorithm for both datasets and choosing training: testing split ratios of 90:10, 80:20, 70:30, 60:40, and 50:50 for each of the five models. The highest overall testing accuracy of 97.77% was observed after training the DenseNet121 model to identify super-pixels created from the CD&S dataset, belonging to the four classes created using a sigma value of five, five segments per image, and an 80:20 training: testing split ratio. Web and mobile applications were developed to identify diseased regions on corn leaves using the best deep learning model as the classifier. The results suggest that SLIC segmentation on corn leaf images helps accurately identify diseased regions. This research demonstrates the potential of image-based scouting as an efficient alternative to manual scouting for disease monitoring.

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Section: Introductionmentioning

confidence: 99%

Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

2022

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“…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%

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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A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques

Patil

Manohar

2023

Lecture Notes in Networks and Systems

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Severity Estimation of Grapevine Diseases from Leaf Images Using Fuzzy Inference System

Cited by 3 publications

References 18 publications

Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

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

A Comprehensive Review on Crop Disease Prediction Based on Machine Learning and Deep Learning Techniques

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