The project presents object characteristics analysis using image processing techniques for automated vision systems used in the agricultural field. In agriculture research of automatic object characteristics detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of object 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 preprocessing 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 object characteristics. For this approach, the automatic classifier NN is used for classification based on learning with some training samples of that same category. This network uses the tangent sigmoid function as the kernel function. Finally, the simulated result shows that the used network classifier provides minimum error during training and better accuracy in classification.
- SUPPORT VECTOR MACHINE
- K-MEANS CLUSTERING
- RANDOM tree classifier
- RGB COLOR MODEL
- GLCM FEATURE EXTRACTION
- BLOB DETECTION
- Artificial NEURAL NETWORK
- Maximum accuracy in classification
- Real-time achievement
- Machine-based prediction
- HDD: 1TB
- RAM: 8GB
PYTHON 3.9 VERSION
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