A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing

Jothiaruna, N.; Sundar, Kaarthik; Karthikeyan, B.

doi:10.1016/j.compag.2019.104934

Cited by 33 publications

(11 citation statements)

References 11 publications

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“…Figure 11 depicts the outcome of lotus root images taken and analyzed using region growing segmentation algorithm. Jothiaruna et al, (Jothiaruna, Joseph Abraham Sundar, & Karthikeyan, 2019) established that it is necessary to select good parameters for the blob formation. Their selected parameters were what made the segmentation process accurate and successful.…”

Section: Resultsmentioning

confidence: 99%

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Yuan

Wilson

Asamoah

et al. 2021

JFI

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Lotus root, which is a water plant cherished by people in the Asian continent and some other parts of the world, is manually inspected for quality by experts to detect impurities. There is the need to update this inspection process in order to improve the quality and safety of lotus root. Machine vision systems and techniques are used for consistent, efficient, effective, and reliable inspection of images. The lotus root inspection system has been proposed to inspect the lotus roots for impurities. The detection algorithms use the size, shape, texture and color of the lotus root images as parameters to analyze the quality of lotus roots. The lotus root undergoes some processes before image acquisition and image processing. The camera and illumination used, in collaboration with the edge detection, and image segmentation techniques, efficiently and effectively exposed the impurities in the lotus root at a much faster rate. Also, it is less expensive compared to the traditional human inspections.

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Section: Resultsmentioning

confidence: 99%

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Yuan

Wilson

Asamoah

et al. 2021

JFI

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“…The objective of using this criterion is to examine all pixels around the new attached pixels to decide whether they can be added to the working area or not. This procedure specifies the attribution of the novel pixel to the corresponding area [ 53 , 54 ].…”

Section: Extended Growth Region Methodsmentioning

confidence: 99%

“…When the initial seeds are determined for the algorithm commencement and also the similarity criterion is determined for pixels with areas, then, the area growth process gets started. The area growth, which starts from the initial seeds, is done through choosing the neighboring regions [ 53 , 54 ].…”

Section: Extended Growth Region Methodsmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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“…Argüeso et al [37] applied a Convolutional Neural Network using a few shot learning algorithm for plant leaf classification using deep learning with small datasets. A segmentation method for disease detection at the leaf scale using a colour features and region-growing was proposed in Jothiaruna et al [38]. Disease of avocado plants was researched in Abdulridha et al [9], using a multi-step approach including image acquisition from two cameras, image segmentation using region of interest and polygon region of interest, a multilayer perceptron for feature extraction and KNN classification.…”

Section: Agricultural Applicationsmentioning

confidence: 99%

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Storey

Meng

2022

Sustainability

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Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.

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A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing

Cited by 33 publications

References 11 publications

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

Detection of Lotus Root Contaminants Using Intelligent Visual Machine Vision Techniques

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

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