• Miss.Nagane P.A.
• Dr.S.N.Patil

One of the fruits with the highest market earnings is the pomegranate, which is cultivated in several Indian states with a very high yield. However, a variety of factors lead to the plants becoming infected with different diseases, which decimate the entire crop and produce a very low yield. Therefore, the paper suggests using image processing and neural network techniques to address the primary problems in phytopathology, namely the detection and categorization of diseases. Different illnesses that are brought on by fungus, bacteria, and environmental factors damage the pomegranate fruit as well as the leaves. Similar to Bacterial Blight, Fruit Spot, Fruit Rot, and Leaf Spot. The system employs a variety of pictures for testing, training, and other purposes.Pre-processing is done on the colour pictures, and segmentation is done using k-means clustering. Utilizing the GLCM technique, the artificial neural network is provided with the texture characteristics. This approach is 90% accurate overall. In contrast to manual grading, the results have been shown to be accurate and satisfying, and it is hoped that they will gain a solid reputation as one of the most effective methods.

disease detection and classification; pomegranate plant diseases; k-means clustering segmentation; GLCM method; artificial neural network

"Diagnosis of Pomegranate Plant Diseases using Neural Network Methadology.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e222-e227, December-2023, Available :http://www.jetir.org/papers/JETIR2312431.pdf

"Diagnosis of Pomegranate Plant Diseases using Neural Network Methadology.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe222-e227, December-2023, Available at : http://www.jetir.org/papers/JETIR2312431.pdf