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 analysis on reconstruction loss allow us to not only detect the anomalies but also to some degree estimate the anomalous regions.

本研究解决了温室农业中番茄裂果异常检测的难题，裂果会显著影响农产品的质量。通过运用定制的变分自编码器(VAE)和高光谱输入，本研究提出了一种无监督的检测方法，并发现530nm至550nm的波长范围最适合检测番茄裂果。研究结果不仅成功检测了异常，还能在一定程度上估计异常区域。

基于高光谱成像和变分自编码器的无监督番茄裂果异常检测