Vol. 7 No. 1 (2026)

Standard Journal Issues

Noise-Resilient Hybrid EfficientNet–Vision Transformer Framework with Adaptive Symmetric Cross-Entropy Loss for Robust Plant Disease Detection

Published 2026-03-27

  • Pradeep Gupta
    • ,
  • Rakesh Singh Jadon

Pradeep Gupta
Department of CSE, Madhav Institute of Technology & Science, Gwalior (M.P.), India

Rakesh Singh Jadon
Department of CSE, Madhav Institute of Technology & Science, Gwalior (M.P.), India

Abstract

The errors of human annotation and the noise of the environment such as lighting changes, occlusions and cluttered backdrop limit the correct detection of the plant diseases in the field condition. The research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification. The hybrid EfficientNet-Vision Transformer (ViT) network proposed is based on an EfficientNet-B4 branch of CNN and a branch of Vision Transformer (ViT) network, which focuses on capturing fine-grained lesion features and global contexts information. A data augmentation pipeline based on CycleGAN is used to introduce field-style distortions (e.g., (lighting shifts, shadowing, debris and partial occlusions), to be more robust to environmental noise, and an Adaptive Symmetric Cross-Entropy (ASCE) loss identifies and down-weights uncertain samples with normalized prediction entropy. The training is done in two phases, Stage 1 pretraining with clean images of PlantVillage and Stage 2 with increasingly noisy samples. The framework is tested in two different noise conditions, and these include the controlled synthetic label noise with PlantVillage and the real environmental noise with PlantDoc. The proposed model has an accuracy of 94.5% on the clean PlantVillage test set. It achieves 85.0% accuracy on the PlantVillage dataset under the 20% synthetic label noise protocol, outperforming ResNet-50V2 (76.5%), DenseNet-121 (78.9%), and Co-Teaching (79.5%). Macro-precision, macro-recall and macro-F1 of the model on the external PlantDoc field dataset are 0.718, 0.681, 0.681, respectively with a top-1 accuracy of 72.0, which is a manifestation of cross-domain generalization. The lesion-centric Grad-CAM images indicate that the model places emphasis on symptomatic areas of leaves and represses reactions of background soil, shadows, and clutters. The suggested hybrid EfficientNet-ViT architecture offers, in general, a robust and explainable solution to precision agriculture and intelligent crop tracking systems that are resistant to noise.

Keywords

Adaptive Symmetric Cross-Entropy, Deep Learning, Noise-Robust Learning, Vision Transformer

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