Viewpoint Analysis for Maturity Classification of Sweet Peppers.

Abstract

The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB-Red Green Blue (images acquired in a photocell) and 417 RGB-D-Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit's orientation on the plant.

Code and data availability

The paper's sweet pepper image datasets (photocell, robotic, orientation) are paper-specific phenotyping assets, but they are only referenced via a 'Reserved DOI' on Mendeley Data with no live public URL among the allowed URLs, so they are not directly actionable; authors/DOI resolution would be needed.

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