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.

Plant phenotyping relevance

スイートペッパー果実の成熟度という植物形質を、画像取得視点・果実向き・分類アルゴリズムの性能評価により推定しており、フェノタイピング手法の評価が中心である。

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.

No evidence-backed public reproduction asset is currently recorded.