Comparing human and machine clustering for tomato ripening stage classification

Abstract

ABSTRACT The classification of tomato ripening stages involves assigning a tomato to a category based on the visual indicators of its maturity. Indeed, the specific number of categories and their attributes are determined by the agricultural standards of each country, which rely on an empirical understanding of visual characteristics. Conversely, automatic unsupervised classification techniques, such as deep learning-based methods, autonomously learn their characteristics. In this research, a comparison is made between expert-based classification and unsupervised classification, with a particular focus on the analysis of the number of clusters and their respective features. Remarkably, this investigation finds an alignment in the number of clusters identified by both methods. This discovery supports the notion that the expert-based classification system is compatible with automated approaches. The outcomes of this research could aid the agricultural sector in refining automatic classification techniques. Furthermore, this work provides the scientific community with valuable insights into the clustering of images by machine learning methods.

Keywords

• Precision farming
• deep learning
• unsupervised learning
• clustering.