文献类型： 外文期刊

作者： Ma, Guifu ¹ ; Javidan, Seyed Mohamad ² ; Ampatzidis, Yiannis ³ ; Zhang, Zhao ⁴ ;

作者机构： 1.Xinjiang Acad Agr Sci, Res Inst Agr Mechanizat, Urumqi 830091, Peoples R China

2.Tarbiat Modares Univ, Dept Biosyst Engn, Tehran 14115111, Iran

3.Univ Florida, Southwest Florida Res & Educ Ctr, Agr & Biol Engn Dept, 2685 FL-29, Immokalee, FL 34142 USA

4.China Agr Univ, Key Lab Smart Agr Syst Integrat, Minist Educ, Beijing 100083, Peoples R China

关键词： deep feature extraction; early disease detection; hyperspectral images; one-shot and few-shot learning; precision agriculture; tomato fungal diseases

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2025 年 25 卷 14 期

页码：

收录情况： SCI

摘要： Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. Following inoculation, hyperspectral images were captured every other day from Day 1 to Day 7 post inoculation. The proposed hybrid method includes three main steps: (1) preprocessing of hyperspectral image cubes, (2) deep feature extraction using the EfficientNet model, and (3) classification using Manhattan distance within a few-shot learning framework. This combination leverages the strengths of both spectral imaging and deep learning for robust detection with minimal data. The few-shot learning approach achieved high detection accuracies of 85.73%, 80.05%, 90.33%, and 82.09% for A. alternata, A. solani, B. cinerea, and F. oxysporum, respectively, based on data collected on Day 7 post inoculation using only three training images per class. Accuracy improved over time, reflecting the progressive nature of symptom development and the model's adaptability with limited data. Notably, A. alternata and B. cinerea were reliably detected by Day 3, while A. solani and F. oxysporum reached dependable detection levels by Day 5. Routine visual assessments showed that A. alternata and B. cinerea developed visible symptoms by Day 5, whereas A. solani and F. oxysporum remained asymptomatic until Day 7. The model's ability to detect infections up to two days before visual symptoms emerged highlights its value for pre-symptomatic diagnosis. These findings support the use of few-shot learning and hyperspectral imaging for early, accurate disease detection, offering a practical solution for precision agriculture and timely intervention.