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Process of image generation using image augmentation: 1) high brightness, 2) low brightness, 3) cropping, 4) horizontal flip, 5) 90-degree rotation, 6) 180-degree rotation, 7) 270-degree rotation, 8) high contrast, and 9) low contrast
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Diseases of papaya impeded quality production and caused severe financial damages to growers. An efficient diagnosis approach for papaya diseases is enormously desired to control and prevent the spread of diseases. At first, using a dataset of 138980 images of affected and healthy leaves and fruits of papaya which was generated with image augmentat...
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Citations
... An effective diagnostic method is needed to control and prevent disease spread. Researchers trained eight E cientNet models (B0-B7) using 138,980 images of papaya leaves and fruits [4], then ne-tuned the three best-performing models for ensemble learning. The ensemble model achieved higher test accuracy (1.61% increase) and offers a more effective approach for recognizing papaya diseases. ...
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... Sari, Kurniawati, and Santosa [11] adopted a Fuzzy Naïve Bayes classifier for papaya disease detection, highlighting the potential of fuzzy logic in handling uncertainties in disease identification. Hridoy and Tuli [12] proposed a novel deep ensemble approach, utilizing EfficientNet models to achieve robust recognition of papaya diseases. Islam et al. [13] expanded the horizon of image classification using machine learning in papaya disease recognition. ...
This comprehensive review delves into the application of deep learning techniques for the precise identification of papaya diseases. With the increasing importance of papaya as a major tropical fruit crop, the accurate and timely diagnosis of diseases is crucial for effective disease management. The paper synthesizes recent advancements in deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to image-based disease identification in papaya plants. The review assesses the strengths and limitations of various deep learning models, explores the integration of multi-modal data sources, and evaluates the performance metrics employed for disease detection accuracy. Additionally, the study discusses challenges and future directions in leveraging deep learning for papaya disease identification, aiming to provide a comprehensive understanding of the current state and potential advancements in this critical agricultural domain.
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Doi: 10.28991/HEF-2022-03-01-09
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This study investigates the application of Vision Transformers (ViTs) in deep learning for the accurate identification of papaya diseases. ViTs, known for their effectiveness in image classification tasks, are utilized to develop a robust model capable of precisely diagnosing various diseases that affect papaya plants. Through rigorous experimentation and validation, the study showcases the superior performance of ViTs compared to traditional convolutional neural networks (CNNs) in terms of classification accuracy and computational efficiency. The results highlight the potential of ViTs in real-world agricultural systems, enabling early and accurate disease detection to improve crop yield and ensure food security. This research contributes to the advancement of computer vision techniques in agriculture, emphasizing the importance of leveraging cutting-edge deep learning models like ViTs for enhanced disease management and sustainable agricultural practices.
