• Gishamol R
• John J
• S H Muhsina Parvin
• Srithi Sethukumar
• Anu Mary Mathew

Our project presents an analysis of the characteristics of the leaf using image processing techniques for an automated vision system used in agricultural lands. In agriculture, the search for automatic detection of leaf characteristics is essential for monitoring large fields and automatically detects the symptoms of the leaves as soon as they appear on the leaves of the plants. The proposed decision-making system uses the characterization of the content of the image and a network of supervised classification neurons. Image processing techniques for this type of decision analysis include pretreatment, feature extraction and classification steps. During processing, the size of an input image will be resized and the region of interest will be selected. Here, the characteristics of color and texture are extracted from an input for the formation and classification of the network. Color characteristics as an average, standard deviation of the HSV color space and texture characteristics such as energy, contrast, homogeneity and correlation. The system will be used to automatically classify the test images to determine the characteristics of the leaf. For this approach, the automatic Neural Network classifier can be used for classification based on learning with examples of learning in this category. This network uses the sigmoid tangent function as a function of the kernel. Finally, the simulated result shows that the network classifier used provides a minimum error during training and better classification accuracy.

Plant disease, Pre-processing, DWT, Feature Extraction, NN classifier

"Leaf Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.147-152, May 2019, Available :http://www.jetir.org/papers/JETIRCV06027.pdf

"Leaf Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp147-152, May 2019, Available at : http://www.jetir.org/papers/JETIRCV06027.pdf