MushR-Project-Raw-Image-Dataset (Oyster Mushrooms)

# MushR General Summary:
**MushR** is a modular and scalable gourmet mushroom growing and harvesting system that goes beyond the state of the art, which merely monitors and controls the growing environment, by introducing an image recognition system that determines when and which mushrooms are ready to be harvested in conjunction with a proof-of-concept of an automated mushroom harvesting mechanism for harvesting the mushrooms without human interaction. The image recognition setup monitors the growing status of the mushrooms and guides the harvesting process. We present a Mask R-CNN model for the detection of oyster mushroom maturity as well as a semi-automated harvesting system, integrating a Raspberry Pi for control, an electrical switch, an air compressor, and a pneumatic cylinder with a cutting knife to facilitate timely mushroom harvesting. The modularity and scalability of the system allow for industry-level usage and can be scaled according to the required mushroom-growing systems within the facility.

#MushR Dataset:
The dataset created for this project focuses on capturing images of the mushroom-growing environment from three different perspectives within each of our two growth tents for mushroom production. Instead of providing images of every individual bucket and mushroom, we capture the overall scene and its variations. The images from each perspective are captured simultaneously and automatically hourly. This approach allows for monitoring the development and maturity of the oyster mushrooms over time. We captured and accumulated 34,400 images over ten months to ensure a comprehensive dataset. This repository contains the raw images while the annotated images used to train the model are available in a separate repository: [Link](https://www.kaggle.com/datasets/etcelab/mushr-project-raw-image-dataset-oyster-mushrooms)

# Further Resources:
The AI model, a digital twin for mushroom production, the setup of our growth and control chambers, and additional information are all made available under an open-source license.

- MushR Github Repository (AI model digital twin, etc.) - [Link](https://github.com/ETCE-LAB/MushR)

{'# MushR General Summary': '**MushR**乃一项模块化且可扩展的珍馐蘑菇栽培与收获系统，其技术先进性远超现有水平。传统系统仅限于监控与调控生长环境，而MushR则在此基础上引入了一项图像识别系统，用以判断何时以及哪些蘑菇适宜收获，并辅以一项自动化收获机制的概念验证，以实现无需人工干预的蘑菇收获。该图像识别系统对蘑菇的生长状况进行监控，并指导收获过程。本研究提出了一种基于Mask R-CNN模型的牡蛎菇成熟度检测方法，以及一套半自动收获系统，该系统集成了Raspberry Pi控制器、电气开关、气动压缩机和带有切割刀的气动缸，以促进蘑菇的及时收获。系统的模块化和可扩展性使其适用于工业级应用，并可按设施内所需的蘑菇栽培系统进行扩展。', '# MushR Dataset': '为此项目所创建的数据集专注于捕捉两个蘑菇生产帐篷内蘑菇生长环境从三个不同视角的图像。不同于提供每个桶和蘑菇的独立图像，我们捕捉的是整体场景及其变化。从每个视角的图像同步且自动每小时捕捉一次。这种做法使得能够监控牡蛎菇随时间的发育和成熟。我们历时十个月共捕捉并累积了34,400张图像，以确保数据集的全面性。本仓库包含原始图像，而用于训练模型的标注图像则可在单独的仓库中找到：[链接](https://www.kaggle.com/datasets/etcelab/mushr-project-raw-image-dataset-oyster-mushrooms)', '# Further Resources': 'AI模型（蘑菇生产数字孪生）、生长与控制室配置以及额外信息等均以开源许可证提供。', '- MushR Github Repository (AI model digital twin, etc.)': '- [链接](https://github.com/ETCE-LAB/MushR)'}

搜集汇总

背景与挑战

背景概述

该数据集是MushR项目的一部分，包含34,400张原始图像，从三个不同视角每小时自动拍摄蘑菇生长环境，覆盖十个月时间，用于监测平菇的发育和成熟度。数据集支持图像识别系统，以确定蘑菇收获时机，并与自动化收获机制结合，具有模块化和可扩展性，适用于工业级蘑菇生产。