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  1. 1
    Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

    Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201 by CHYNTIA JABY, ENTUNI, TENGKU MOHD AFENDI, ZULCAFFLE

    Published 2022
    “…The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. …”
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  2. 2
    Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm

    Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm by Wang, Xinming, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Mohd Idris Shah

    Published 2023
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  3. 3
    Classification of Citrus (Rutaceae) by Using Image Processing

    Classification of Citrus (Rutaceae) by Using Image Processing by Najwa Bari'ah Mohd Tabri

    Published 2019
    “…A machine learning algorithms, SVM have been used to build species identification models. …”
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    Undergraduate Final Project Report
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  4. 4
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…Plants identification has become a significant and incentive research area. …”
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  5. 5
    Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari

    Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari by Sha’ari, Nor Silawati

    Published 2018
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  6. 6
    Maize leaf disease detection and classification using Convolutional Neural Network (CNN) / Syafiqah Amir

    Maize leaf disease detection and classification using Convolutional Neural Network (CNN) / Syafiqah Amir by Amir, Syafiqah

    Published 2023
    “…The dataset used in this project consist of 800 images of four category of leaf achieved 90 percent of accuracy by using the CNN algorithm.…”
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  7. 7
    Feature selection for Malaysian medicinal plant leaf shape identification and classification

    Feature selection for Malaysian medicinal plant leaf shape identification and classification by Sainin, Mohd Shamrie, Alfred, Rayner

    Published 2014
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    Conference or Workshop Item
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  8. 8
    SMART AGRICULTURAL MONITORING SOLUTION FOR CHILLI LEAF DISEASES USING A LOW-COST KINECT CAMERA AND AN IMPROVED CNN ALGORITHM

    SMART AGRICULTURAL MONITORING SOLUTION FOR CHILLI LEAF DISEASES USING A LOW-COST KINECT CAMERA AND AN IMPROVED CNN ALGORITHM by Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kismet, Hong Ping, Amit Baran, Sharangi, Tarun Kumar, Upadhyay, Mohd, Saeedd

    Published 2023
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  9. 9
    Deep plant: A deep learning approach for plant classification / Lee Sue Han

    Deep plant: A deep learning approach for plant classification / Lee Sue Han by Lee , Sue Han

    Published 2018
    “…They look for the procedures or algorithms that maximize the use of leaf databases for plant predictive modelling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. …”
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  10. 10
    Identification of reliable reference genes for gene expression studies of oil palm plantlets using normfinder and bestkeeper algorithms

    Identification of reliable reference genes for gene expression studies of oil palm plantlets using normfinder and bestkeeper algorithms by A. H., Muhamad Afiq, Shaharuddin, Noor Azmi, R., Zubaidah

    Published 2019
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  11. 11
    Plant leaf recognition algorithm using ant colony-based feature extraction technique

    Plant leaf recognition algorithm using ant colony-based feature extraction technique by Ghasab, Mohammad Ali Jan

    Published 2013
    “…To do this, at first, based on the proposed algorithm,the physiological dimensions of leaves are automatically measured and with regard to these parameters, specified features such as shape, morph, texture and colour are extracted from the image of the plant leaf through image processing to create a reserved feature database to be used for different species of plants. …”
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  12. 12
    Analysis of banana plant health using machine learning techniques

    Analysis of banana plant health using machine learning techniques by Thiagarajan, Joshva Devadas, Kulkarni, Siddharaj Vitthal, Jadhav, Shreyas Anil, Waghe, Ayush Ashish, Raja, S.P., Rajagopal, Sivakumar, Poddar, Harshit, Subramaniam, Shamala

    Published 2024
    “…This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.…”
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  13. 13
    Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model

    Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model by Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kismet, Hong Ping

    Published 2023
    “…One of the major challenges in capsicum cultivation is the accurate identification of leaf diseases. Leaf diseases can have a detrimental effect on the quality of capsicum production, leading to substantial losses for farmers. …”
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  14. 14
    Cabbage disease detection system using k-NN algorithm

    Cabbage disease detection system using k-NN algorithm by Mohamad Ainuddin Sahimat

    Published 2022
    “…Finally, the KNN algorithm will be used to classify the disease based on sample nature and a cabbage disease data set. …”
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    Academic Exercise
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  15. 15
    A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

    A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework by Bari, Bifta Sama, Islam, Md Nahidul, Rashid, Mamunur, Hasan, Md Jahid, Mohd Azraai, Mohd Razman, Musa, Rabiu Muazu, Ahmad Fakhri, Ab. Nasir, Majeed, Anwar P.P. Abdul

    Published 2021
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  16. 16
    Review of Wheat Disease Classification and Severity Detection Models

    Review of Wheat Disease Classification and Severity Detection Models by Hongyan, Zang, Annie, Joseph, Shourong, Zhang, Rong, Liu, Wanzhen, Wang

    Published 2023
    “…To combat these diseases, researchers have been studying the use of advanced techniques such as deep plant disease learning and image processing methods for identification. …”
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  17. 17
    Detection of multiple mangoes using histogram of oriented gradient technique in aerial monitoring

    Detection of multiple mangoes using histogram of oriented gradient technique in aerial monitoring by Mohd Ali, Nursabillilah, Karis, Mohd Safirin, Mohd Sobran, Nur Maisarah, Bahar, Mohd Bazli, Oh, Kok Ken, Mat Ibrahim, Masrullizam, Johan, Nurul Fatiha

    Published 2016
    “…The project uses shape identification algorithm and Histogram of Oriented Gradient principle to detect and count the total number of mango on its tree using a quad copter with an attachable webcam. …”
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  18. 18
    Delineating mangrove forest zone using spectral reflectance

    Delineating mangrove forest zone using spectral reflectance by Abdul Whab @ Abdul Wahab,, Zulfa

    Published 2020
    “…Species identification with spectral library derived from in-situ measurements using SID algorithm and derived from Landsat 8 using SAM algorithm was done. …”
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  19. 19
    Surface Reflectance And Discriminant Analysis For Mapping Of Mangrove Species In Kuala Sepetang Mangrove Forest Reserve, Perak

    Surface Reflectance And Discriminant Analysis For Mapping Of Mangrove Species In Kuala Sepetang Mangrove Forest Reserve, Perak by Beh, Boon Chun

    Published 2016
    “…The first method is to develop an algorithm that was based on species’ surface reflectance to map the mangrove species by using high-resolution CropCam Unmanned Aerial Vehicle (UAV) data. …”
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  20. 20
    Development of an automated detector and counter for bagworm census

    Development of an automated detector and counter for bagworm census by Ahmad, Mohd Najib

    Published 2020
    “…As for detection, the reflector method was applied to find the reflected apparent temperature and emissivity of the bagworms using thermographic measurement techniques. Then, the experiment on identification of bagworm under thermal imaging is conducted using a thermal infrared camera, T 440 at different sites. …”
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