AgroConv-Eff: A Hybrid ConvNeXt–EfficientNetV2 Architecture with Adaptive Feature Gating for Multi-Crop Plant Disease Detection

Timely diagnosis of crop disease directly shapes yield outcomes, and for many smallholder farmers, a trained eye scanning a leaf is still the only diagnostic tool available. This paper presents AgroConv-Eff, a hybrid deep learning architecture that fuses ConvNeXt-Tiny and EfficientNetV2-S feature-extraction pathways through an adaptive channel-wise gating mechanism for automated multi-crop plant disease classification. The system was trained and evaluated on a 38-class, 14-crop plant disease dataset derived from the PlantVillage image collection, comprising leaf images spanning multiple crops and disease categories. Using ImageNet-pretrained backbones and transfer learning, the proposed model achieved 99.34% test accuracy and a macro-F1 score of 98.77%. To rigorously position this result, we additionally trained and evaluated three strong baselines under an identical protocol: MobileNetV2 (98.73% accuracy), ConvNeXt-Tiny alone (99.37% accuracy), and EfficientNetV2-S alone (99.16% accuracy). The comparison shows that, on this dataset, the single-backbone ConvNeXt-Tiny model is competitive with and marginally exceeds the proposed hybrid in raw accuracy while using substantially fewer parameters and lower inference latency, a finding we discuss openly along with per-class error analysis. To isolate the specific contribution of the proposed adaptive gating mechanism, we further design a controlled ablation comparing gated fusion against simple concatenation and fixed-weight averaging under identical backbones and training settings. Overall, this work contributes a fully reproducible training and evaluation pipeline, a systematic four-model comparison under one identical protocol, an ablation protocol isolating the fusion mechanism’s contribution, and an honest efficiency-accuracy trade-off analysis with explicitly stated limitations for multi-crop plant disease detection.