Research Article
Plant syndrome recognition by Gigapixel Image using Convolutional Neural Network
@INPROCEEDINGS{10.4108/eai.16-5-2020.2304207, author={C. Saravanakumar and P. Gururama Senthilvel and D. R. Thirupurasundari and P. N. Periyasamy and K. Vijayakumar}, title={Plant syndrome recognition by Gigapixel Image using Convolutional Neural Network}, proceedings={Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India}, publisher={EAI}, proceedings_a={ICASISET}, year={2021}, month={1}, keywords={gigapixel image neural image compression(nic) plant diseases convolutional neural networks(cnn) probability estimator algorithm image processing}, doi={10.4108/eai.16-5-2020.2304207} }
- C. Saravanakumar
P. Gururama Senthilvel
D. R. Thirupurasundari
P. N. Periyasamy
K. Vijayakumar
Year: 2021
Plant syndrome recognition by Gigapixel Image using Convolutional Neural Network
ICASISET
EAI
DOI: 10.4108/eai.16-5-2020.2304207
Abstract
The plants play a vital role in our day-to-day life. It is important to monitor the health of the plants. Generally, plant diseases are identified using image processing techniques. In those techniques, the input images of plants are of an only megapixel size. In this method image processing of plants is done using a gigapixel input image that covers the entire area of the crop. To process this huge gigapixel image a method called Neural Image Compression(NIC) is used. The identification of the plant diseases is done using Convolutional Neural Networks(CNN) from the neutrally compressed gigapixel image. CNN is trained using a probability estimation algorithm to identify the affected portion of the plant crop.
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