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  • Open Access

    ARTICLE

    CD-FL: Cataract Images Based Disease Detection Using Federated Learning

    Arfat Ahmad Khan1, Shtwai Alsubai2, Chitapong Wechtaisong3,*, Ahmad Almadhor4, Natalia Kryvinska5,*, Abdullah Al Hejaili6, Uzma Ghulam Mohammad7

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1733-1750, 2023, DOI:10.32604/csse.2023.039296

    Abstract A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time. Automatic cataract prediction based on various imaging technologies has been addressed recently, such as smartphone apps used for remote health monitoring and eye treatment. In recent years, advances in diagnosis, prediction, and clinical decision support using Artificial Intelligence (AI) in medicine and ophthalmology have been exponential. Due to privacy concerns, a lack of data makes applying artificial intelligence models in the medical field challenging. To address this issue, a federated learning framework named CD-FL based on a VGG16… More >

  • Open Access

    ARTICLE

    Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

    Fawad Ali Shah1, Habib Akbar1, Abid Ali2,3, Parveen Amna4, Maha Aljohani5, Eman A. Aldhahri6, Harun Jamil7,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1385-1413, 2023, DOI:10.32604/csse.2023.036144

    Abstract The detection of rice leaf disease is significant because, as an agricultural and rice exporter country, Pakistan needs to advance in production and lower the risk of diseases. In this rapid globalization era, information technology has increased. A sensing system is mandatory to detect rice diseases using Artificial Intelligence (AI). It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases. Deep Neural Network (DNN) is a novel technique that will help detect disease present on a rice leave because DNN is also considered… More >

  • Open Access

    ARTICLE

    Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

    Sana Parez1, Naqqash Dilshad2, Turki M. Alanazi3, Jong Weon Lee1,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

    Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

    N. Vasudevan*, T. Karthick

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242

    Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network… More >

  • Open Access

    ARTICLE

    Hybrid Convolutional Neural Network for Plant Diseases Prediction

    S. Poornima1,*, N. Sripriya1, Adel Fahad Alrasheedi2, S. S. Askar2, Mohamed Abouhawwash3,4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2393-2409, 2023, DOI:10.32604/iasc.2023.024820

    Abstract Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products. The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agriculture. Manual system to monitor the diseases in plant is time consuming and report a lot of errors. There is high demand for technology to detect the plant diseases automatically. Recently image processing approach and deep learning approach are highly invited in detection of plant diseases. The diseases like late blight, bacterial spots, spots on Septoria leaf and yellow leaf curved are widely found in plants.… More >

  • Open Access

    ARTICLE

    Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

    Javed Rashid1,2, Imran Khan1, Ghulam Ali3, Shafiq ur Rehman4, Fahad Alturise5, Tamim Alkhalifah5,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1235-1257, 2023, DOI:10.32604/cmc.2023.032005

    Abstract The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a… More >

  • Open Access

    ARTICLE

    Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

    Mazen Mushabab Alqahtani1, Ashit Kumar Dutta2, Sultan Almotairi3, M. Ilayaraja4, Amani Abdulrahman Albraikan5, Fahd N. Al-Wesabi6,7,*, Mesfer Al Duhayyim8

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

    Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

  • Open Access

    ARTICLE

    Rice Disease Diagnosis System (RDDS)

    Sandhya Venu Vasantha1, Shirina Samreen2,*, Yelganamoni Lakshmi Aparna3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1895-1914, 2022, DOI:10.32604/cmc.2022.028504

    Abstract Hitherto, Rice (Oryza Sativa) has been one of the most demanding food crops in the world, cultivated in larger quantities, but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern. During cultivation, the crops are most prone to biotic stresses such as bacterial, viral, fungal diseases and pests. These stresses can drastically damage the crop. Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy. The proven methods of molecular biology can provide accurate… More >

  • Open Access

    ARTICLE

    Feature Extraction and Classification of Plant Leaf Diseases Using Deep Learning Techniques

    K. Anitha1, S. Srinivasan2,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 233-247, 2022, DOI:10.32604/cmc.2022.026542

    Abstract In India’s economy, agriculture has been the most significant contributor. Despite the fact that agriculture’s contribution is decreasing as the world’s population grows, it continues to be the most important source of employment with a little margin of difference. As a result, there is a pressing need to pick up the pace in order to achieve competitive, productive, diverse, and long-term agriculture. Plant disease misinterpretations can result in the incorrect application of pesticides, causing crop harm. As a result, early detection of infections is critical as well as cost-effective for farmers. To diagnose the disease at an earlier stage, appropriate… More >

  • Open Access

    ARTICLE

    MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

    Bobbinpreet1, Sultan Aljahdali2,*, Tripti Sharma1, Bhawna Goyal1, Ayush Dogra3, Shubham Mahajan4, Amit Kant Pandit4

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4771-4787, 2022, DOI:10.32604/cmc.2022.023150

    Abstract In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a… More >

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