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Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection

Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection

   Plant disease is the main issue in the field of agriculture. Plant disease detection is the process of detecting the abnormalities in the plants caused by any bacterial or fungus diseases to prevent the disease from spreading to other plants and reduce farming maintenance costs. Multi-class imbalanced classification problem arises in plant disease detection due to insufficient distribution of datasets across more than one class.

   The presence of imbalance datasets for multiple plant disease detection affects the detection performance of the model. Deep learning achieves a high recognition rate in detecting plant diseases. Deep learning models utilize generative networks and data augmentation techniques to handle the class imbalance problem. Multi-Class Imbalance handling with deep learning in plant disease detection increase the efficiency and performance of the plant disease detection model.