Advanced Driver Assitance System – ADAS using Raspberry Pi and OpenCV

Description

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https://youtu.be/-nZ_tWdbsOM

  • The project presents leaf characteristics analysis using image processing techniques for automated vision system used at agricultural field. In agriculture research of automatic leaf characteristics detection is essential one 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 supervised classifier type of neural network. Image processing techniques for this kind of decision analysis involves preprocessing, feature extraction and 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 an input for network training and classification. Color features like mean, 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 METHOD

  • Principal Component Analysis
  • Texture based segmentation
  • KNN classifier

DRAW BACKS ON EXISTING:

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

PROPOSED METHOD:

  • Leaf classification system based on, Hybrid spatial features involves color features and texture descriptors and a classifier is used for prediction

ADVANTAGES:

  • Low complexity and better features discrimination
  • Better classification accuracy

Applications

  • Computer vision
  • Agricultural field

Software Requirement

MATLAB2014&ABOVE VERSIONS

REFERENCES:

[1] Wenjiang Huang, Qingsong Guan, Juhua Luo, Jingcheng Zhang, Jinling Zhao, Dong Liang, Linsheng Huang, and Dongyan Zhang, ?New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases?, IEEE journal of selected topics in applied earth observation and remote sensing,Vol. 7, No. 6, June 2014

[2] Dr.K.Thangadurai, K.Padmavathi, ?Computer Visionimage Enhancement For Plant Leaves Disease Detection?, 2014 World Congress on Computing and Communication Technologies.

[3] Monica Jhuria, Ashwani Kumar, and Rushikesh Borse, ?Image Processing For Smart Farming: Detection Of Disease And Fruit Grading?, Proceedings of the 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013

[4] Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin Md Shakaff Rohani Binti S Mohamed Farook, ?Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques?, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.

[5] Mrunalini R. Badnakhe, Prashant R. Deshmukh, ?Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering?, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March 2012.

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