Raspberry PhenoSet: A Phenology-based Dataset for Automated Growth Detection and Yield Estimation

TL;DR

Raspberry PhenoSet is a high-quality, phenology-based dataset for raspberry fruit detection and segmentation across developmental stages, enabling improved yield estimation and harvest timing through deep learning models.

Contribution

This paper introduces the first fruit dataset integrating biology-based phenology classification with detection tasks, along with benchmarking of state-of-the-art models.

Findings

High-quality dataset with 1,853 images and 6,907 annotations.

Deep learning models face challenges distinguishing subtle phenology stages.

Benchmark results provide insights for future model development.

Abstract

The future of the agriculture industry is intertwined with automation. Accurate fruit detection, yield estimation, and harvest time estimation are crucial for optimizing agricultural practices. These tasks can be carried out by robots to reduce labour costs and improve the efficiency of the process. To do so, deep learning models should be trained to perform knowledge-based tasks, which outlines the importance of contributing valuable data to the literature. In this paper, we introduce Raspberry PhenoSet, a phenology-based dataset designed for detecting and segmenting raspberry fruit across seven developmental stages. To the best of our knowledge, Raspberry PhenoSet is the first fruit dataset to integrate biology-based classification with fruit detection tasks, offering valuable insights for yield estimation and precise harvest timing. This dataset contains 1,853 high-resolution images, the highest quality in the literature, captured under controlled artificial lighting in a vertical farm. The dataset has a total of 6,907 instances of mask annotations, manually labelled to reflect the seven phenology stages. We have also benchmarked Raspberry PhenoSet using several state-of-the-art deep learning models, including YOLOv8, YOLOv10, RT-DETR, and Mask R-CNN, to provide a comprehensive evaluation of their performance on the dataset. Our results highlight the challenges of distinguishing subtle phenology stages and underscore the potential of Raspberry PhenoSet for both deep learning model development and practical robotic applications in agriculture, particularly in yield prediction and supply chain management. The dataset and the trained models are publicly available for future studies.