Work

RGB Image-Based Lightweight Eggplant Fruit Disease Recognition via Frequency Spatial Collaborative Downsampling and Dynamic Attention

Year: 2026

Type: article

Abstract: Eggplant is an important vegetable crop, and accurate identification of fruit diseases is critical for ensuring yield and quality. However, field-captured eggplant images may contain small disease lesions, foliage occlusion, and illumination variations, posing challenges for accurate disease recognition. To address these challenges, this paper proposes E-GhostNetV3 (Eggplant-GhostNetV3), a lightweight disease recognition network for field-captured images. The core improvements include a frequency–spatial collaborative downsampling module (WCD-Down), which combines Haar-based multiband decomposition with parallel convolution for spatial feature extraction, and a Dynamic Spatial–Channel Attention (DySCA) module to improve spatial and channel-wise feature representation. Evaluated on an eggplant disease dataset comprising 1823 real field-captured images across five classes, E-GhostNetV3 achieves 89.61% Top-1 accuracy with 1.81 M parameters and an estimated 116.60 M MACs under the default THOP counting rules, outperforming the GhostNetV3 baseline (87.07%) by 2.54 percentage points. These results demonstrate the effectiveness of E-GhostNetV3 on the evaluated dataset and its potential as a lightweight model for edge-based smart agriculture applications. The study also provides a benchmark for evaluating models trained from scratch under data-scarce conditions.

Source: Sensors

DOI: 10.3390/s26216690

Authors: Shijie Mo, Chenglin Wen

Institution: Guangdong University of Petrochemical Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137