Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders
This work addresses a specific challenge in agricultural quality control for tomato farming, but it is incremental as it applies existing VAE methods to a new domain with tailored data processing.
The paper tackled the problem of detecting tomato split anomalies in greenhouse farming using an unsupervised approach with a tailored variational autoencoder and hyperspectral imaging, achieving 97% detection accuracy on test data and identifying the 530nm-550nm wavelength range as optimal for detection.
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.