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Sayali, Gondkar
- Survey on Detection and Prediction of Leaf Diseases using CNN
Authors
1 Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, IN
Source
Indian Journal of Automation and Artificial Intelligence, Vol 6, No 10 (2019), Pagination: 1-8Abstract
Background: This study is to help reader to understand detection and prediction of leaf diseases using CNN.
Methods: The main purpose of the planned system is to grow an application which identifies cotton leaf diseases and be cooperative for the farmers. With help of image processing idea we can get a fully digitized colour image of a diseased leaf and then we can continue with applying CNN (Convolutional Neural Network) to forecast cotton leaf disease. System gears CNN to sense cotton leaf infections. Disease detection in early stages it very stimulating task for farmer but once the infection is detected he can take prior steps to cure them and save his crops from getting infected.
Findings: Farming is most important living in many countries. Indian economic system is reliant on agricultural production. The main good way towards food manufacture is necessary. While keeping path of infections in plants by specialists it becomes costly and cannot be inexpensive by normal farmers. As farming is main occupation in India and maximum farmers are average in economy. So there is a requirement for a structure which can mechanically sense the diseases and can tell about what pesticides to use so that suitable remedy can be taken after finding of diseases
Application:
* System can be implemented in agricultural farms.
* System can be use by agro service centers to help farmers.
* Provides efficient remedies for occurred disease on plants.
* System can be used by agro industries to prepare depending on the diseases occurred.
Keywords
CNN (Convolutional Neural Network), K-Means Clustering, SGLDM (Spatial Gray Level Difference Method).Full Text
References
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