Main Article Content
In India, the main source of income for healthy lifestyle is agriculture and it is occupying 70% of rural population. Crop cultivation of India is highly miscellaneous. In India, the varieties crop is about 500 types. Even though there is advancement in technology, the practices of agriculture are still manually preceded and low automation level is involved as compared to the western countries. Plants are affected by many diseases and so the leaves are get damaged. From the images of leaves, the affected plant will be identified. This research proposes novel technique in detection of various plant disease stages using feature extraction and classification using deep learning techniques. here the input data has been collected from tomato and grape leafs. This data has been processed for noise removal, image resize and normalization. Then this image features have been extracted using graph Convolutional networks and classification of extracted features has been done using ResNet-50. Furthermore, from the experimental model for the public dataset of grape leaf diseases, this proposed method realizes better outcomes and 94% of average identification accuracy was obtained. This attention module is added and certified will be extracted accurately as complex features of various diseases with some parameters. The proposed model delivers a high-performance solution for diagnosing crop disease under the real agricultural environment.