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  1. 1
    Automated plant classification system using a hybrid of shape and color features of the leaf

    Automated plant classification system using a hybrid of shape and color features of the leaf by Hamid, Laith Emad

    Published 2016
    “…Automated plant leaf classification is a computerized approach that employs computer vision and machine learning algorithms to identify a plant based on the features of its leaf. …”
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  2. 2
    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
    “…Then, based on the characteristics of each species, decision making is done by means of ant colony optimisation as a search algorithm to return the optimal subset of features regarding the related species. …”
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  3. 3
    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
    “…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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  4. 4
    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
    “…Besides using solely a single leaf organ to recognize plant species, numerous studies have employed DL methods to solve multi-organ plant classification problem. …”
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  5. 5
    Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system

    Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system by Seyed Mohammad Hussein, Ahmad, Siti Anom, Hassan, Mohd Khair, Ishak, Asnor Juraiza

    Published 2013
    “…The results show the outperformance of the two proposed methods for image processing and optimized classifier for classification part. …”
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  6. 6
    Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib

    Hevea leaf features extraction and recognition algorithm for hevea clones classification using image / Mohamad Faizal Ab Jabal, Suhardi Hamid, Salehuddin Shuib by Ab Jabal, Mohamad Faizal, Hamid, Suhardi, Shuib, Salehuddin

    Published 2013
    “…Final result produced by the algorithm is 92.312% of average accuracy and the classification for the leaf was based on the leaf-shape information.…”
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  7. 7
    Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC)

    Leaf lesion classification (LLC) algorithm based on artificial bee colony (ABC) by Ahmad, Faudziah, Ku-Mahamud, Ku Ruhana, Sainin, Mohd Shamrie, Airuddin, Ahmad

    Published 2015
    “…Results showed that the Leaf Lesion Classification (LLC) algorithm based on Artificial Bee colony (ABC) produced an average 96.83% of accuracy and average 1.66 milliseconds of processing time, indicating that LLC algorithm is better than algorithm such as Otsu, Canny, Roberts and Sobel. …”
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  8. 8
    Classification of Citrus (Rutaceae) by Using Image Processing

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

    Published 2019
    “…The study present how to classify selected Citrus genus species with similar leaf shapes based on leaf images by using digital image vision machine classification. …”
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    Undergraduate Final Project Report
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  9. 9
    Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm

    Evaluation of multiple In Situ and remote sensing system for early detection of Ganoderma boninense infected oil palm by Ahmadi, Seyedeh Parisa

    Published 2018
    “…In conclusion, for early detection of Ganoderma, better accuracies were derived from the spectroradiometer which is a destructive and ground based method and still requires individual leaf sampling. …”
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  10. 10
    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
    “…Image processing techniques are used to extract the leaf feature from histogram of the leaf image. …”
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  11. 11
    Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves

    Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves by Mat Lazim, Siti Saripa Rabiah, Sulaiman, Zulkefly, Mat Nawi, Nazmi, Mohd Mustafah, Anas

    Published 2023
    “…The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). …”
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    Book Section
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  12. 12
    Automated leaf alignment and partial shape feature extraction for plant leaf classification

    Automated leaf alignment and partial shape feature extraction for plant leaf classification by Hamid, Laith Emad, Syed Mohamed, Syed Abdul Rahman Al-Haddad

    Published 2019
    “…The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. …”
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  13. 13
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Chong, Hou Ming, Yin Yap, Xien, Seng Chia, Kim

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. …”
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  14. 14
    A novel framework for potato leaf disease detection using an efficient deep learning model

    A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

    Published 2022
    “…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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  15. 15
    A novel framework for potato leaf disease detection using an efficient deep learning model

    A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

    Published 2022
    “…Therefore, this article proposes a technique based on an improved deep learning algorithm that uses the potato leaf visual features to classify them into five classes i.e., Potato Late Blight (PLB), Potato Early Blight (PEB), Potato Leaf Roll (PLR), Potato Verticilliumwilt (PVw) and Potato Healthy (PH) class. …”
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  16. 16
    Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection

    Application of Fuzzy C-Means with YCbCr and DenseNet-201 for Automated Corn Leaf Disease Detection by Chyntia Jaby, Entuni

    Published 2021
    “…The results show that the proposed method performed better than the previous methods with 96.81% for segmentation as well as 95.11% for classification and it is discovered to be a good fusion of algorithms to detect plant leaf diseases.…”
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  17. 17
    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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  18. 18
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. …”
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  19. 19
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. …”
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  20. 20
    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification

    Effects of Different Pre-Trained Deep Learning Algorithms as Feature Extractor in Tomato Plant Health Classification by Hou Ming Chong, Hou Ming Chong, Xien Yin Yap, Xien Yin Yap, Kim Seng Chia, Kim Seng Chia

    Published 2023
    “…This study proposes a system that can classify tomato plant health into five categories of healthy, early blight, late blight, bacterial spot, and yellow leaf curl virus based on their leaves using deep learning algorithms as feature extractors. …”
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