Abstract:
Crop diseases are a major challenge to agricultural productivity and hence diseases it at is the crucial earliest to identify to and avoid detect huge the loss of crops and ensure approach food for security. identifying the crop study diseases presents through a a new machine learning model in the (GNN). form the of work Graph is Neural divided Network into six phases to accomplish the paper’s objective, collecting the a first large of dataset which from entails a public domain that comprises images of crop diseases Powdery Mildew including and Ash Bitter Gourd Gourd Downy Mildew. These images are further divided into different disease categories to facilitate specific and examination. Further, employed several on image the processing techniques. In (GLCM) the with feature statistical extraction measures also with Co-occurrence deep Matrix learning extracted features using DenseNet121. Both kinds of features are critical for disease identification as as well they as provide high-level patterns. both detailed The most important contribution of this CropGNN study model is which the implements proposed graphs to represent the correlations between the features that have provides been an extracted. effective way for This Crop framework Disease Detection graph-based and learning. Through it enhancing integrates the state-of-the-art disease machine identification, learning this methods approach with enables better decision making in agriculture and helps in addressing one of the biggest challenges of our time which is sustainable farming and food security.
