Automatic Rice Leaf Disease Segmentation Using Image Processing Techniques

Archana, K. S.; Sahayadhas, Arun

doi:10.14419/ijet.v7i3.27.17756

Cited by 38 publications

(13 citation statements)

References 17 publications

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“…This technique had limitations and is not suited to very big MRI datasets. In [ 71 ], The study described how to predict leaf disease and segment it for easy identification, as well as how to use color transforms to find the affected leaf. This study used a k-means clustering algorithm to categorize disease symptoms and separate them as clusters at various stages.…”

Section: Significance Of Deep Learning Applications Using Medical Ima...mentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye’s microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.

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Section: Significance Of Deep Learning Applications Using Medical Ima...mentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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“…Image processing is rapidly used in agriculture for detecting plant diseases by applying the computer vision in identifying early symptoms of diseases in plant images (Archana & Sahayadhas, 2018). The disease in paddy and the pest attack on it reduces the yield and the profit in rice productivity.…”

Section: Core Ideasmentioning

confidence: 99%

Detection of rice plant disease using AdaBoostSVM classifier

Kumar

Kannan

2022

Agronomy Journal

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The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

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“…A filter learns to recognize specific patterns, and low-level filters are related to more complex patterns. The Google AI team introduced a hybrid model, which combines self-attention and convolution to achieve top accuracy levels [22]. The CoAtNets pronounced "coat" nets, combining the strength from different architecture.…”

Section: Proposed Hybrid Coatnet Architecture (Phcna)mentioning

confidence: 99%

Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification

Karunanithi¹,

Singh²,

Kannapiran³

2022

RIA

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In the Asian continent rice cultivation process provide staple food for livelihood. A current research work in the agriculture area involves recognizing and classifying plants diseases based on live images. Farmer can traditionally do the cultivation process, hence here the identification of the disease was by manual (visual appearance) or send the sample data set to the nearest laboratory. In our proposed method we will provide accurate and early detection of various diseases in Oryza sativa (rice) plants, that can help the farmers in applying suitable treatment on the rice plants and improve productivity. We are using optimized deep learning models such as the ResNet-152, CoAtNet for classification and identify the diseases. We have captured healthy and unhealthy images from Villupuram district, Tamil Nadu, India. The total amount of captured images was 3071 from our farmer's field with proper sunlight. It was highly efficient and detects the diseases or recognizes the diseases from the captured image with different categories (Bacterial Leaf Blight, Leaf Blast, Brown Spot, and Tungro / Leaf smut). The experimental results show according to the proposed method CoAtNet, was achieved for overall achieved accuracy of 96.56%.

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Automatic Rice Leaf Disease Segmentation Using Image Processing Techniques

Cited by 38 publications

References 17 publications

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Detection of rice plant disease using AdaBoostSVM classifier

Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification

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