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. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has same as well least outcome which is 83% of accuracy. The Fuzzy Opponent Histogram Filter with Bayes Net has 0.97 of precision which is best result than other models. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has 0.84 of precision which are least as well same outcome. The Fuzzy Opponent Histogram Filter with BN has 0.97 of recall which is best result than other models. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has 0.83 of recall which are least as well same outcome. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has same as well as least outcome which is 0.75 of MCC. The Fuzzy Opponent Histogram Filter with Bayes N has best result which is 0.95 of MCC. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has same as well as least outcome which is 0.83 of F-Measure. The Fuzzy Opponent Histogram Filter with Bayes Net has best result which is 0.97 of F-Measure. The Color Layout Filter with Sequential Minimal Optimizer and Color Layout Filter with Ada Boost has same as well as least outcome which is 0.75 of Kappa. The Fuzzy Opponent Histogram Filter with Bayes Net has best result which is 0.95 of Kappa value. The Fuzzy Opponent Histogram Filter with Bayes Net has highest ROC 0.99 and Color Layout Filter with Sequential Minimal Optimizer has 0.90 of ROC which is least value compare than other models. The Fuzzy Opponent Histogram Filter with Bayes Net has 0.99 of PRC which is maximum value than other models. The Color Layout Filter with Sequential Minimal Optimizer has 0.78 of PRC. This work governs the performances of all models but Fuzzy Opponent Histogram Filter with machine learning models have given best performances than Color Layout Filter techniques and also Bayes Net gives best outcome as well as less deviations compare than other Ada Boost and Sequential Minimal Optimizer.