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              <text>Plant disease diagnosis and solution system based on neural networks</text>
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              <text>Convolutional Neural Network (CNN); Image Processing; MobileNet</text>
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              <text>Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease.  2021, Engg Journals Publications. All rights reserved.</text>
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              <text>Megha Chandra Reddy N.V.; Ashish Reddy K.; Sushanth G.; Sujatha S.</text>
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              <text>Indian Journal of Computer Science and Engineering, Vol-12, No. 4, pp. 1084-1092.</text>
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              <text>2021-01-01</text>
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              <text>&lt;a href="https://doi.org/10.21817/indjcse/2021/v12i4/211204226" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.21817/indjcse/2021/v12i4/211204226&lt;/a&gt;
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 9765166</text>
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              <text>Megha Chandra Reddy N.V., Department of Electronics and Communication Engineering, School of Engineering, CHRIST University, Bangalore, Karnataka, India; Ashish Reddy K., Department of Electronics and Communication Engineering, School of Engineering, CHRIST University, Bangalore, Karnataka, India; Sushanth G., Department of Electronics and Communication Engineering, School of Engineering, CHRIST University, Bangalore, Karnataka, India; Sujatha S., Department of Electronics and Communication Engineering, School of Engineering, CHRIST University, Bangalore, Karnataka, India</text>
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