M18K: A Comprehensive RGB-D Dataset and Benchmark for Mushroom Detection and Instance Segmentation

TL;DR

This paper introduces M18K, a large RGB-D dataset with over 18,000 mushroom instances for detection and segmentation, advancing automated mushroom farming research.

Contribution

It provides the first comprehensive mushroom-specific dataset and benchmark for detection and segmentation algorithms in smart agriculture.

Findings

Dataset contains 423 RGB-D image pairs with detailed annotations.

Advanced algorithms evaluated show promising detection and segmentation performance.

Resources including images, code, and models are publicly available.

Abstract

Automating agricultural processes holds significant promise for enhancing efficiency and sustainability in various farming practices. This paper contributes to the automation of agricultural processes by providing a dedicated mushroom detection dataset related to automated harvesting, growth monitoring, and quality control of the button mushroom produced using Agaricus Bisporus fungus. With over 18,000 mushroom instances in 423 RGB-D image pairs taken with an Intel RealSense D405 camera, it fills the gap in mushroom-specific datasets and serves as a benchmark for detection and instance segmentation algorithms in smart mushroom agriculture. The dataset, featuring realistic growth environment scenarios with comprehensive annotations, is assessed using advanced detection and instance segmentation algorithms. The paper details the dataset's characteristics, evaluates algorithmic performance, and for broader applicability, we have made all resources publicly available including images, codes, and trained models via our GitHub repository https://github.com/abdollahzakeri/m18k