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  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

    Javed Rashid1,2, Imran Khan1, Irshad Ahmed Abbasi3, Muhammad Rizwan Saeed4, Mubbashar Saddique5,*, Mohamed Abbas6,7

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356

    Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… 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

    A Novel Framework for Multi-Classification of Guava Disease

    Omar Almutiry1, Muhammad Ayaz2, Tariq Sadad3, Ikram Ullah Lali4, Awais Mahmood1,*, Najam Ul Hassan5, Habib Dhahri1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1915-1926, 2021, DOI:10.32604/cmc.2021.017702

    Abstract Guava is one of the most important fruits in Pakistan, and is gradually boosting the economy of Pakistan. Guava production can be interrupted due to different diseases, such as anthracnose, algal spot, fruit fly, styler end rot and canker. These diseases are usually detected and identified by visual observation, thus automatic detection is required to assist formers. In this research, a new technique was created to detect guava plant diseases using image processing techniques and computer vision. An automated system is developed to support farmers to identify major diseases in guava. We collected healthy and unhealthy images of different guava… More >

  • Open Access

    ARTICLE

    Genetic diversity of guava (Psidium guajava L.) from Central Mexico revealed by morphological and RAPD markers

    Valera-Montero LL, PJ Muñoz-Rodríguez, H Silos-Espino, S Flores-Benítez

    Phyton-International Journal of Experimental Botany, Vol.85, pp. 176-183, 2016, DOI:10.32604/phyton.2016.85.176

    Abstract Guava fruit produced in Calvillo, Aguascalientes (Mexico) is considered to be of the best quality in this country. Nevertheless, growers from this place empirically know that there is a noticeable variation among individual trees within the same orchard, and variation among individuals from different orchards. In order to have a clear evidence of this, morphology analysis of guava was performed taking data from seventy nine individuals out of thirty six orchards, while RAPD was performed on a subset of twenty six individuals. Similarity was found for morphology data ranging from 87-100%, while similarity from RAPD data ranged from 30- 100%.… More >

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