Real time Plant Health Assessment Via Implementing Cloud based Scalable Transfer Learning On AWS DeepLens

In Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country . We used forty thousand images for the training of the deep learning model and then evaluated it on ten thousand images . The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0.349s, providing disease information to the user in less than a second . Cloud integration provides scalability and ubiquitous access to our approach . Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used 40 thousand images to train and evaluate the model and . evaluate it on 10 thousand images. The process took 0 .349s. It took 0,349s to analyze an image on an image to analyze and evaluate an image. It provided disease information for disease information in just over a second. It provides disease information on an analysis of an image that took less than two minutes to test and evaluate it. The model was successful .

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Keywords : image - disease - evaluate - plant - information -

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