CLASSIFICATION OF EARLY AND LATE BLIGHT DISEASE OF POTATO USING CONVOLUTIONAL NEURAL NETWORK
| dc.contributor.author | Jesmin Akther | |
| dc.date.accessioned | 2025-11-23T06:24:42Z | |
| dc.date.available | 2025-11-23T06:24:42Z | |
| dc.date.issued | 2020-12-08 | |
| dc.description.abstract | Crop diseases are a major threat to cultivate, and need to supervise growth and detrimental diseases in time. Potato early and late blight disease symptoms and detection both are vague to distinguish and isolate. The rising combination of smartphone penetration and recent advances in deep learning has paved the way for smart device assisted disease prognosis. Relying on pure naked-eye observation to detect and classify diseases can be very cumbersome. Absolute detection process proves to be effective and convenient for researchers. The technique of training deep learning models on increasingly vast and globally available image datasets presents a clear path toward smartphone aid crop disease experiment around the world. The color and analyze layer features are used to best match to recognize and classify different agriculture produce into early blight and late blight affected disease. Both features prove to be very effective in disease detection. This paper deployed a Sequential convolutional neural network (CNN) model to detect and identify diseases in real time survey potato leaves labeling early and late blight. In addition to normalization, divide and extract the images to prepare data prior CNN. This work adopts slight variation during CNN model finalization with the help of Tensorboard analysis. This analyzed effectively optimized model validation accuracy each layer by layer hierarchically. Best layers ensure to create the final model which assures 94% accuracy for this dataset and short timing classification. The experimental results indicate that the approach significantly can be modified model accuracy in automatic detection of both affected bight. | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/139 | |
| dc.title | CLASSIFICATION OF EARLY AND LATE BLIGHT DISEASE OF POTATO USING CONVOLUTIONAL NEURAL NETWORK |