Automated detection of crop leaf diseases and classification of remote sensing land cover categories remain challenging owing to complex backgrounds, illumination vari-ability, spectral distortions, and high intra-class visual similarity. Existing frameworks provide strong baselines but commonly suf-fer from scale-sensitive segmentation, redundant feature fusion, limited contextual representation, and slow convergence in high-dimensional feature spaces. This paper proposes the Adaptive Recurrent FusionNet (ARFusionNet) framework — a Flask-based web application that integrates four coordinated inno-vations: Multi-Scale Adaptive Contrast Normalisation (MACN) for illumination-robust preprocessing; Graph-Based Superpixel Attention Segmentation (GSAS) for adaptive Region-of-Interest extraction; Bidirectional Gated Recurrent Units (BiGRU) embed-ded within Residual Efficient Convolution Blocks with Adaptive Weighted Feature Aggregation (AWFA); and Hybrid Binary Differential Evolution controlled Particle Swarm Optimisation (BDE-PSO) for efficient feature selection. DenseNet121 serves as the backbone feature extractor. We validate the system on the Plant Pathology 2020 dataset (1,821 high-resolution apple leaf im-ages; four disease classes). ARFusionNet achieves 98.2% classifi-cation accuracy, surpassing the state-of-the-art baseline (97.6%), while reducing training time by approximately 78 seconds and remaining fully executable on a standard CPU laptop without GPU dependency. The accompanying web application exposes eight interactive diagnostic modules including leaf visualisation, Canny edge display, convolved feature maps, neural network architecture visualisation, and real-time per-image prediction.
Sanjay et al. (Thu,) studied this question.