用高光谱成像与变分自编码器无监督检测番茄裂果，准确率达97%。
Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders
- 基于高光谱图像设计专用变分自编码器，实现无监督异常检测。
- 530-550nm波段对干裂型番茄裂果检测效果最佳，测试准确率达97%。
- 可通过重构误差定位裂果区域，适合农业质检场景应用。
番茄异常损伤是温室种植中的重大挑战，尤其裂果会导致果实品质下降。由于外观和尺寸动态变化以及数据集稀缺，此类异常检测困难。本文提出一种无监督方法，利用高光谱图像输入的定制化变分自编码器（VAE）进行检测。初步分析确定530nm–550nm波段最适合识别番茄干裂。所提VAE模型在测试数据上达到97%的检测准确率。通过重构误差分析，不仅能检测异常，还能部分定位异常区域。
原文摘要 · Abstract (English)
Tomato anomalies/damages pose a significant challenge in greenhouse farming. While this method of cultivation benefits from efficient resource utilization, anomalies can significantly degrade the quality of farm produce. A common anomaly associated with tomatoes is splitting, characterized by the development of cracks on the tomato skin, which degrades its quality. Detecting this type of anomaly is challenging due to dynamic variations in appearance and sizes, compounded by dataset scarcity. We address this problem in an unsupervised manner by utilizing a tailored variational autoencoder (VAE) with hyperspectral input. Preliminary analysis of the dataset enabled us to select the optimal range of wavelengths for detecting this anomaly. Our findings indicate that the 530nm - 550nm range is suitable for identifying tomato dry splits. The proposed VAE model achieved a 97% detection accuracy for tomato split anomalies in the test data. The analysis on reconstruction loss allow us to not only detect the anomalies but also to some degree estimate the anomalous regions.
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