Iot-Based Leaf Disease Identification And Detection Uing Successive Method For Feature Extraction
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Abstract
Developing a prototype system to identify paddy diseases, such as bacterial leaf spot, sectorial leaf spot, target spot, and leaf mold disease, is the primary goal of this project. This research focuses on the use of neural networks to classify paddy disease and image processing techniques to improve image quality. The technique includes gathering images, segmenting and pre-processing them, then analyzing and categorizing the paddy illness. The K-means clustering approach is utilized to segment images, and characteristics are generated from the cluster impacted by the disease. Extracted are characteristics including contrast, homogeneity, correlation, energy, mean, variance, and standard deviation. To classify the disease, the extracted features from disease cluster have been given as classifier inputs.