Pepper-4D: Spatiotemporal 3D Pepper Crop Dataset for Phenotyping

TL;DR

Pepper-4D is a new 3D dataset capturing pepper plant growth over time, enabling detailed analysis for improved agricultural practices.

Contribution

The novel contribution is a high-precision 4D point cloud dataset with plant and organ-level annotations for pepper phenotyping.

Findings

Pepper-4D includes 916 point clouds from 29 pepper plants with plant- and organ-level annotations.

The dataset supports multiple phenotyping tasks like growth classification, segmentation, and organ tracking.

It enables synthetic 3D plant generation and advanced breeding research.

Abstract

Pepper (Capsicum annuum) is a globally significant horticultural crop cultivated for its culinary, medicinal, and economic value. Traditional approaches for boosting the agricultural production of pepper, notably, expanding farmland, have become increasingly unsustainable. Recent advancements in artificial intelligence and 3D computer vision have started to transform crop cultivation and phenotyping, which has shed new light on increasing production by advanced breeding. However, currently, the field still lacks 3D pepper data that contains enough detail for organ-level analysis. Therefore, we propose Pepper-4D, a new, high-precision 4D point cloud dataset that records both the spatial structure and temporal development of pepper plants across various continuous growth stages. Our dataset is divided into three subsets, including a total of 916 individual point clouds from 29 indoor-cultivated pepper plant samples. Our dataset provides manual annotations at both the plant-level and organ-level, supporting phenotyping tasks such as pepper growth status classification, organ semantic segmentation, organ instance segmentation, organ growth tracking, new organ detection, and even the generation of synthetic 3D pepper plants.

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