Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging and Variational Autoencoders

TL;DR

This paper presents an unsupervised hyperspectral imaging method using a variational autoencoder to detect tomato split anomalies with high accuracy, addressing challenges of dataset scarcity and appearance variability.

Contribution

The study introduces a tailored VAE approach with optimal wavelength selection for effective unsupervised detection of tomato splits in hyperspectral images.

Findings

Achieved 97% detection accuracy on test data.

Identified 530-550nm as optimal wavelength range.

Enabled anomaly localization through reconstruction loss analysis.

Abstract

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.