Abstract:

Mango Plant diseases have a great influence on the productivity and economics of agriculture. In the traditional method where manual labour and experiences play important roles, early mango plant disease detection and prevention for field crop are inefficient. This can bring negative effects in stopping the spread of diseases. Applications of image processing techniques and computer vision may provide a solution to these problems. Deep learning is known as one of the most powerful techniques which is able to address more complicated tasks compared to traditional machine learning thanks to the embedded complex layers. it presents an image-based diseases identification method using a deep neural network with contrast enhancement and transfer learning from the mango leaf dataset. Since there exist various sizes of leaf, rescaling and centre alignment are performed to standardize images. Besides, contrast enhancement is used to improve quality of visualization which gives a well-prepared step for further processing. By using a collected mango disease dataset, the proposed model is trained to identify 8 common diseases of mango and also mango healthy one or disease or not. The proposed model is then compared with other pre-trained models.proposed model achieved highest accuracy, which is higher than the results obtained from other pretrained models.
Keywords—Disease identification, mango leaf disease, deep learning models

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