Cognitive Computing for Wheat Leaf Disease Detection System

Precision agriculture relies on crop disease detection to boost production and food security. This paper introduces Cognitive Computing-based wheat leaf disease detection. The approach uses machine learning, natural language processing, and interpretability to identify diseases accurately and easily. The results demonstrate the advantage of cognitive computing over traditional methods. In comparison to CNNs and SVMs, our model had superior accuracy, precision, recall, and F1 scores. The model’s strong performance metrics across folds were confirmed by cross-validation. The Cognitive Computing model was faster than CNNs and SVMs in inference, making it appropriate for real-time illness identification. Interpretability was examined by showing how the model’s decision-making process may be presented in normal language to build user trust. These findings show that Cognitive Computing can revolutionize wheat leaf disease diagnosis, providing farmers and agriculture stakeholders with a realistic and effective solution. The work advances precision agriculture by showing how modern technologies may solve difficult crop health monitoring problems.