International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Solanum Nigrum Leaf Disease Classification using SVM and CNN Algorithms
ABSTRACT To detect solanum nigrum leaf diseases, we are utilizing image processing with a Convolution neural network (CNN). A convolutional neural network (CNN) is a form of artificial neural network that is specifically intended to process pixel input and is used in image recognition. The data collection consists of five plant leaf diseases. These images are preprocessed by converting the HIS color to RGB images. Using the K-mean cluster algorithm, the 'H' elements of the affected portion of the leaf are segmented. Using the GLCM algorithm, feature extracted from cluster part of leaf image. The features are tested, and train then sorted based on maximum accuracy. With the best linear combination SVM and CNN get input of selected of image features. There are 70% images for training, and 30% images are being tested. To evaluate the data properly, the K-fold method is used. The CNN deep learning algorithm offers 95.33 % accuracy, and the SVM machine learning algorithms offer 85.07 % accuracy. Therefore, CNN is said to be better than SVM. In future other leaf disease detection is also performing using various dataset with different diseases.
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AUTHOR S.VISHNUPRIYA, K.VENKATESAN, DR. R. UMAMAHESWARI, K.VIJAYPRABAKARAN PG Student, Department of CSE, Gnanamani College of Technology, Namakkal, Tamil Nadu, India Assistant Professor, Department of CSE, Gnanamani College of Technology, Namakkal, Tamil Nadu, India Professor and Head, Department of CSE, Gnanamani College of Technology, Namakkal, Tamil Nadu, India
VOLUME 177
DOI DOI: 10.15680/IJIRCCE.2025.1312013
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KEYWORDS
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