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A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA
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Academic Editor: Bugao Xu
Received: 2 May 2021 / Revised: 8 June 2021 / Accepted: 25 June 2021 / Published: 1 July 2021
Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species. View Full-Text
Keywords: plant leaf diseases; agriculture; mobile app; convolutional neural networks (CNN); deep learning plant leaf diseases; agriculture; mobile app; convolutional neural networks (CNN); deep learning
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MDPI and ACS Style

Ahmed, A.A.; Reddy, G.H. A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning. AgriEngineering 2021, 3, 478-493. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030032

AMA Style

Ahmed AA, Reddy GH. A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning. AgriEngineering. 2021; 3(3):478-493. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030032

Chicago/Turabian Style

Ahmed, Ahmed A., and Gopireddy H. Reddy 2021. "A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning" AgriEngineering 3, no. 3: 478-493. https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030032

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