Version 1
: Received: 5 September 2020 / Approved: 6 September 2020 / Online: 6 September 2020 (15:33:05 CEST)
Version 2
: Received: 7 September 2020 / Approved: 7 September 2020 / Online: 7 September 2020 (10:08:00 CEST)
Version 3
: Received: 12 September 2020 / Approved: 14 September 2020 / Online: 14 September 2020 (06:24:16 CEST)
How to cite:
Khan, A.; Nawaz, U.; Ulhaq, A.; Robinson, R. W. Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens. Preprints2020, 2020090142. https://doi.org/10.20944/preprints202009.0142.v1
Khan, A.; Nawaz, U.; Ulhaq, A.; Robinson, R. W. Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens. Preprints 2020, 2020090142. https://doi.org/10.20944/preprints202009.0142.v1
Khan, A.; Nawaz, U.; Ulhaq, A.; Robinson, R. W. Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens. Preprints2020, 2020090142. https://doi.org/10.20944/preprints202009.0142.v1
APA Style
Khan, A., Nawaz, U., Ulhaq, A., & Robinson, R. W. (2020). Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens. Preprints. https://doi.org/10.20944/preprints202009.0142.v1
Chicago/Turabian Style
Khan, A., Anwaar Ulhaq and Randall W. Robinson. 2020 "Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens" Preprints. https://doi.org/10.20944/preprints202009.0142.v1
Abstract
In the 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. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (D.C.D.M.) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. 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 forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.
Keywords
Diseases; A.W.S. DeepLens; SageMaker; Machine Learning; Deep Learning
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
