Residual Life Assessment (RLA) Analysis of Apple Disease Based on Multimodal Deep Learning Model
Keywords:
Apple, Disease recognition, Deep Learning, Training, Transfer learningAbstract
Few works have been carried out for the vision-based Apple disease framework throughout the year. Mainly, apple disease recognition includes two issues: infection identification and disease classification. Because of the advancement of vision-based innovation, we got a better framework for this issue. The datasets are mainly grouped into four categories, i.e., typical, rot, blotch, and scab, the last three being the three major kinds of defects found in apples. The aim is to distinguish these defective apples from the normal ones. In this chapter, we propose an Alex net and VGG-16-based deep learning model for classifying disease in all categories of apples. The performance of the Alex-Net model is 95.56 percent, whereas VGG-16 produces 94 percent accuracy rates. In both models, the highest classification accuracy has been produced for the rot disease apple category.
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