Hyperspectral Classification of Frost Damage Stress in Tomato Plants Based on Few-Shot Learning

作者：Shiwei Ruan, Hao Cang, Huixin Chen, Tianying Yan, Fei Tan, Yuan Zhang, Long Duan, Peng Xing, Li Guo, Pan Gao, Wei Xu · 发表于：Agronomy · 年份：2023 · DOI：10.3390/agronomy13092348 · 被引用次数：17 · 研究领域：Smart Agriculture and AI、Remote Sensing in Agriculture、Climate change impacts on agriculture

Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. However, acquiring a large amount of properly annotated hyperspectral data on stressed crops requires extensive biochemical experiments and specialized knowledge. This limitation poses a challenge to the construction of large-scale datasets for crop stress analysis. Meta-learning is a learning approach that is capable of learning to learn and can achieve high detection accuracy with limited training samples. In this paper, we introduce meta-learning to hyperspectral imaging and crop detection for the first time. In addition, we gathered 88 hyperspectral images of drought-stressed tomato plants and 68 images of freeze-stressed tomato plants. The data related to drought serve as the source domain, while the data related to frost damage serve as the target domain. Due to the difficulty of obtaining target domain data from real-world testing scenarios, only a limited amount of target domain data and source domain data are used for model training. The results indicated that meta-learning, with a minimum of eight target domain samples, achieved a detection accuracy of 69.57%, precision of 59.29%, recall of 66.32% and F1-score of 62.61% for classifying the severity of frost stress, surpassing other methods with a target domain sample size ...