Leaf Disease Detection using CNN with OpenCV

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Description

The project presents leaf characteristics analysis using image processing techniques for an automated vision system used in the agricultural fields.? In agriculture research on automatic leaf characteristics detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of leaf characteristics as soon as they appear on plant leaves. The proposed decision-making system utilizes image content characterization and a supervised classifier type of neural network. Image processing techniques for this kind of decision analysis involve pre-processing feature extraction and a classification stage. At Processing, an input image will be resized and region of interest selection performed if needed. Here, color and texture features are extracted from the input for network training and classification. Color features like mean, the standard deviation of HSV color space, and texture features like energy, contrast, homogeneity, and correlation. The system will be used to classify the test images automatically to decide leaf characteristics.


EXISTING SYSTEM

  • Principal Component Analysis
  • Texture based segmentation
  • KNN classifier

DRAWBACKS ON EXISTING:

  • High Computational load?
  • Poor discriminatory power
  • Less accuracy in classification

PROPOSED SYSTEM:

  • Feature extraction of glam
  • Convolutional neural network
  • Thresholding segmentation

ADVANTAGE:

It easily identifies the disease by using a convolutional neural network.


Block diagram:

Leaf Disease Detection using Opencv and Python 1


Conclusion:

The above Literature survey has a detailed explanation of the importance of disease detection both to plants and to mankind. To have a meaningful impact on plant diseases & techniques in the area of agriculture, deliberation of proper input is necessary. Research issues addressed here are to develop a systematic approach to detect and recognize the plant diseases that would assist farmers and pathologists in prospect exploration. The paper depicts the importance of image processing in the agriculture field and considers the type of disease for further research work.


Reference :

[1] ?Indian agriculture economy.?. Available: http:// statistics times.com/economy/sectorwise-gdp-Contribution-ofindia. Php

[2] ?Common rust in maize?, Available: https://www. pioneer.com/home/site/us/agronomy/library/common-rustin-corn/?

[3] Indian Council of Agricultural Research?, Available: https://www.apsnet.org/publications/imageresource/ Pages/Fi00158.aspx

[4] ?family of trees?, https:// plant village .psu. edu/ topics/ co conut/infos

[5] ?A gropedia?, Available:http://agropedia.iitk.ac.in / content /papaya-diseases-its-control?

[6] Prof.Sonal, P.Patil, Rupali, Zambre,?Classification of Cotton Leaf Spot Disease Using SVM,? International Journal of Engineering Research & Applications?, Vol.4, pp. 92-97, May 2014?

[7] HTTPS:// worldofchillies.com/growing_chillies/chilli_pest problems diseases/chilli diseases/chillidiseases.html?

[8] Pragya Adhikari, Yeonyee Oh, Dilip R. Panthee? Current Status of Early Blight Resistance in Tomato: An Update,? International Journal of Molecular Science?, September 2017

[9] Akansha Pandey, Sanjeev Dubey,? Evaluations of brinjal germplasm for resistance to fusarium wilt disease,? International Journal of Scientific and Research Publications, Volume 7, Issue 7, July 2017

[10] Gittaly Dhingra, Vinay Kumar, Hem Dutt Joshi,?Study of digital image processing techniques for leaf disease


 

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