M18K: A Comprehensive RGB-D Dataset and Benchmark for Mushroom Detection and Instance Segmentation
It addresses the lack of mushroom-specific datasets for smart agriculture, providing a benchmark for researchers and practitioners, though it is incremental as it focuses on a new dataset rather than novel methods.
The paper introduces M18K, a comprehensive RGB-D dataset with over 18,000 mushroom instances for detection and instance segmentation, aimed at automating agricultural processes like harvesting and monitoring in mushroom farming.
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