The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Abstract

Crop diseases and insect pests are the most significant obstacles to agricultural production. Consequences include a decrease in crop yields and the obstruction of the long-term development of high-quality, high-efficiency agriculture. Rapid and accurate detection of wheat leaf disease categories promotes early deployment of field control and improvement of wheat yield and quality. It's no exaggeration to say that wheat is one of the world's most vital crops. Wheat leaf diseases, though, severely hamper development. The quality of wheat and the agricultural economy rely on prompt and correct identification of wheat leaf diseases. This work propose an integrated machine learning technique, According to the results of this study, when compared to other models, the Fuzzy Opponent Histogram Filter that makes use of Bayes Net performs the best. The Fuzzy Opponent Histogram Filter with Bayes Net gives best outcome which is 97% of accuracy.