Indian Journal of Automation and Artificial Intelligence

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Survey on Detection and Prediction of Leaf Diseases using CNN


Affiliations
1 Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India
 

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).
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Notifications

  • S. Kumbhar, A. Nilawar. Farmer buddy-web based cotton leaf disease detection using CNN. International Journal of Applied Engineering Research. 2019; 14(11), 1-5.

  • V. Ramya, M. Anthuvan Lydia. Leaf disease detection and classification using neural networks. International Journal of Advanced Research in Computer and Communication Engineering. 2016; 5(11), 1-4.

  • M. Ranjan, M.R. Weginwar, N. Joshi, A.B. Ingole. Detection and classification of leaf disease using artificial neural network. International Journal of Technical Research and Applications. 2015; 3(3), 331-333

  • S.P. Patil, R.S. Zambre. Classification of cotton leaf spot diseases using support vector machine. International Journal of Engineering Research and Application. 2014; 4(5), 92-97.

  • N. Shah, S. Jain. Detection of disease in cotton leaf using artificial neural network. Amity International Conference on Artificial. 2019.


Abstract Views: 516

PDF Views: 240




  • Survey on Detection and Prediction of Leaf Diseases using CNN

Abstract Views: 516  |  PDF Views: 240

Authors

Chaudhari Vaishnavi
Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India
Gondkar Sayali
Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India
Shivarkar Pooja
Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India
Shivshran Pooja
Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India
T. Bhaskar
Computer Engineering, Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, India

Abstract


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).

References