Leaf Disease Classification using OpenCV, Python
₹5,000.00 Exc Tax
The project presents leaf disease diagnosis using image processing techniques for automated vision system used at agricultural field. The proposed decision making system utilizes image content characterization and supervised classifier type back propagation with feed forward neural network.
Platform : Python (OpenCV)
Delivery : One Working Day
Support : Online Demo ( 2 Hours)
100 in stock
The project presents leaf disease diagnosis using image processing techniques for automated vision system used at agricultural field. In agriculture research of automatic leaf disease detection is essential one in monitoring large fields of crops, and thus automatically detects symptoms of disease as soon as they appear on plant leaves. The proposed decision making system utilizes image content characterization and supervised classifier type back propagation with feed forward 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 either abnormality or good one. For this approach, automatic classifier BPN with FF will be used for classification based on learning with some training samples of that two category. This network uses tangent sigmoid function as kernel function. Finally, the simulated result shows that used network classifier provides minimum error during training and better accuracy in classification.
The main objective of this system is to identify the disease in the leaves and notify to the farmers so that they can give the corresponding pesticides to that leaves. It decrease the nearby leaves affection in a short period of time. Using image processing we can easily spot the affected area in the leaves. In reference the normal leaves are taken for the comparison purpose so that we can easily identify the affected leaves. Increase the database the accuracy will be high, images taken for the comparison should not be affected with any of the disease.
- Principal Component Analysis
- Texture based segmentation
- KNN classifier
- High Computational load
- Poor discriminatory power
- Less accuracy in classification
Groundnut Leaf classification system based on,
- Hybrid spatial features involves color features and texture descriptors
- Back Propagation Network (BPN) Classifier
- Color Space Conversion
- Color and Texture Features Extraction
- BPN-FF classifier
- Low complexity and better features discrimination
- Better classification accuracy
- Computer vision
- Agricultural field
Thus the disease attacked in the leaves can be easily identified and further pestisides corresponding to that disease can be treated easily.
 Jayamala K. Patil, Raj Kumar, ―Advances In Image Processing For Detection of Plant Diseases‖, JABAR, 2011, 2(2), 135-141.
 P.Revathi, M.Hemalatha, ―Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques‖, ISBN, 2012, 169-173, IEEE.
 H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh, Fast and Accurate Detection and Classification of Plant Diseases‖, IJCA, 2011, 17(1), 31-38, IEEE-2010
 Piyush Chaudhary, Anand K. Chaudhari, Dr. A. N. Cheeran and Sharda Godara, Color Transform Based Approach for Disease Spot Detection on Plant Leaf‖, IJCST, 2012, 3(6), 65-70.
 S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, ―Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features‖, CIGR, 2013, 15(1), 211-217.