As of now decade, many researchers have
worked in the tomato plant disease detection field. It’s not
easy for farmers to identify tomato leaf diseases detect is
difficult, for farmers challenging for them to discover
other plant illnesses, such as tomato plant disease. So, the
ongoing development with the help of machine-learning
and deep-learning has greatly helped in identifying
tomato plant disease detection by operating various
methods along with tools. Precise the outcome but the
accuracy of models depends on the volume as well as the
quality of labeled data for training. In this article, for the
detection of the disease convert the image into RBG and
then identify the region-based image along with the help
of segmentation by using the k-mean method. Then
extract the image together with gray level co-occurrence
matrix (GLCM) features used to identify a diseased
infected part. Performance-based, classify images with
respect to improving the efficiency of the overall model.
The final output indicates that the proposed method
achieved an accuracy of 95% through resnet50 for ten
classes, nine disease classes, and one class that is healthy.
Keywords :
Grey Level Co-Occurrence Matrix (GLCM), Convolution Neural Network (CNN)