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In the Agriculture sector, control of plant leaf diseases is crucial as itinfluences the quality and production of plant species with an impact on theeconomy of any country. Therefore, automated identification and classificationof plant leaf disease at an early stage is essential to reduce economic lossand to conserve the specific species. Previously, to detect and classify plantleaf disease, various Machine Learning models have been proposed; however, theylack usability due to hardware incompatibility, limited scalability andinefficiency in practical usage. Our proposed DeepLens Classification andDetection Model (DCDM) approach deal with such limitations by introducingautomated detection and classification of the leaf diseases in fruits (apple,grapes, peach and strawberry) and vegetables (potato and tomato) via scalabletransfer learning on AWS SageMaker and importing it on AWS DeepLens forreal-time practical usability. Cloud integration provides scalability andubiquitous access to our approach. Our experiments on extensive image data setof healthy and unhealthy leaves of fruits and vegetables showed an accuracy of98.78% with a real-time diagnosis of plant leaves diseases. We used fortythousand images for the training of deep learning model and then evaluated iton ten thousand images. The process of testing an image for disease diagnosisand classification using AWS DeepLens on average took 0.349s, providing diseaseinformation to the user in less than a second.

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