A Novel Optimization Deep Learning Model With Dual Attention for Corn Leaf Disease Detection and Classification

ABSTRACT

The early detection of diseases in plants is essential to achieve maximum yield and minimum crop loss. This paper introduces an effective deep learning‐based pipeline to address the challenges, including a new FRCNN‐DA model for corn leaf disease classification and detection. The study consists of analyzing six models including FRCNN‐DA, OFDNN‐PDDC, CNN‐VGG, AHKM‐FCM, EfficientNet and ETL‐NET across four corn leaf type categories namely Northern leaf blight, Common rust, gray leaf spot and Healthy. The efficiency of the model was tested using performance metrics like Accuracy, Sensitivity, Specificity, F1‐Score, MAE, Detection Rate and AUC‐ROC. The FRCNN‐DA model achieved state‐of‐the‐art results with an accuracy of 97.85%, a sensitivity of 98.12% and an AUC score of 0.993 higher than all the benchmark models. Experimental results, combined with detection rate graph, MAE over 100 epochs and training loss graphs also show that the FRCNN‐DA converges rapidly in experimental learning, has high accuracy and is efficient for generalization. This framework provides a real‐world solution for utilizing AI to intelligently diagnose crop diseases in precision agriculture that could be extremely useful in decision‐making for crop health management.