A Controlled Evaluation of Dual-Channel Feature Enhancement and Multi-Level Knowledge Distillation for Lightweight Plant Disease Recognition
TL;DR
DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel interactions in parallel and injects them through gated residual fusion is introduced and shows that adding intermediate transfer constraints does not guarantee a stronger student.
Abstract
Plant disease symptoms combine local texture changes with patterns distributed across a leaf, while practical recognition models must remain compact. We introduce DC-FEN, a MobileNetV3-based design that models spatial-token relations and channel interactions in parallel and injects them through gated residual fusion. We also examine output-distribution, direct-feature, and token-relation transfer under same-backbone and heterogeneous teachers. PlantVillage and Plant Pathology 2021 (FGVC8) are evaluated with duplicate-audited, group-aware 70/15/15 splits, an explicit unresolved-leaf sensitivity check, validation-only selection, five training seeds, class-sensitive metrics, and paired seed-wise descriptive summaries. On PlantVillage, the no-additional-attention student, DC-FEN teacher, and DC-FEN joint student obtain macro F1 scores of 96.46±0.91%, 96.90±0.40%, and 96.55±0.25%. On FGVC8, the corresponding scores are 87.29±0.63%, 87.14±0.52%, and 87.20±0.26%. At the prespecified FGVC8 threshold of 0.5, DCAB changed sample-wise F1 by −0.02±0.55 percentage points relative to the unmodified backbone; validation-selected global and label-specific thresholds changed this contrast to +0.28±0.55 and +0.55±0.29 points, while threshold-free macro mAP remained essentially unchanged. A duplicate-audited PlantDoc pressure test reduced frozen-checkpoint accuracy to 30.34±1.10% and 29.57±1.10%, showing that external generalization remains unestablished. A ResNet50 teacher gives logit-only students 97.42±0.51% macro F1 on PlantVillage and 89.82±0.43% sample-wise F1 on FGVC8. After separately weighting the direct and relation terms, the corresponding joint students obtain 97.37±0.56% and 89.94±0.27%, recovering the degradation seen with unit internal weights while remaining close to logit-only transfer. Thus, the study evaluates the benefits and limits of explicit spatial–channel interaction and shows that adding intermediate transfer constraints does not guarantee a stronger student.