Promoting sustainable farming through remora improved invasive attention based deep learning model for root disease classification

Abstract Early identification of root diseases is vital for safeguarding crop health and maximizing agricultural yield. Traditional diagnostic approaches are often constrained by their manual nature, slow response time, limited scalability, and inconsistent accuracy. To overcome these limitations, this study proposes a novel hybrid RIFATA Attention-PSPNet (RA-PSPNet) framework designed for automated and precise root disease identification. The RA-PSPNet replaces the CNN to Convolutional block attention module (CBAM) in the Pyramid Scene Parsing Network (PSPNet). Further, RA-PSPNet is optimized with a metaheuristic optimization technique called the Remora Improved Feedback Artificial Tree Algorithm (RIFATA), which enhances model performance through dynamic hyperparameter tuning. The proposed system includes a multi-stage pipeline: root images are first pre-processed using Rot Sensitive Gaussian (RSG) filtering, followed by processing with CBAM to provide multiscale attention feature map and finally segmented PSPNet. Both the segmentation and classification components of proposed RA-PSPNet are optimized using RIFATA. RIFATA combines the strengths of the Remora Optimization Algorithm (ROA) and Improved Feedback Artificial Tree Algorithm (IFATA), which incorporates elements from Improved Invasive Weed Optimization (IIWO) and Feedback Artificial Tree (FAT) models for efficient hyperparameter tuning. Experimental evaluations conducted on multiple datasets from Maize root, Root cowpea, Wheat root, Rice root gellan, Alfalfa root, Chrono root, Root crown images of soybean and wheat datasets in the ratio of 80:10:10. Implementation reveals that proposed RA-PSPNet outperforms with the accuracy of 99.63%, high sensitivity (99.13%), specificity (99.23%), precision (99.03%), recall (99.13%), and F1-score (99.08%) towards healthy and rot root classification. These results demonstrate the effectiveness and scalability of RA-PSPNet for sustainable precision agriculture and root disease diagnosis. The implementation are documented in our publicly accessible repository on Zenodo to enhance the transparency and reproducibility. ( https://doi.org/10.5281/zenodo.16825955 ).