Dataset Information

RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose.

ABSTRACT: For the welfare of self-development and the country's economic evolution, people invest their youth and money in different cultivation and sustainable production business sectors. The crops or fruits get all the attention for this purpose, but currently, the commercial cultivation of flowers is becoming a numerous beneficial investment. As a consequence, the rose(Genus Rosa) is one of the most beautiful and commercially demanding flowers among different flowers. However, insecticide resistance is considered one of the lion's share issues facing agricultural production of roses by decreasing plants' growth and the quality as well as the quantity of healthy-looking flowers. Apart from this, due to different natural and environmental issues, rose's quality and production level are losing their fame. Additionally, the cultivators of this sector are not educated enough to identify the initial affection of different diseases of leaves with beard eyes. Besides, the lack of communication skills to consult with an agriculturist timely turns the situation worst more than the estimation of the production. With this concern, early detection of diseases that affected different parts of roses, such as leaves, is crucial. Recently, image processing techniques and machine learning classifiers have been primarily applied to recognize multiple diseases. This article presents an extensive dataset of rose leaves images, both diseases affected and diseases free are classified into three classes (Blackspot, Downy Mildew, and Fresh Leaf). The dataset is composed of the collected images which were captured during the seasonal time of diseases affection with the consultation of a domain expert and the dataset is accessible at https://data.mendeley.com/datasets/7z67nyc57w/2.

SUBMITTER: Sazzad S

PROVIDER: S-EPMC9372599 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose.

Sazzad Sadia S Rajbongshi Aditya A Shakil Rashiduzzaman R Akter Bonna B Kaiser M Shamim MS

Data in brief 20220802

For the welfare of self-development and the country's economic evolution, people invest their youth and money in different cultivation and sustainable production business sectors. The crops or fruits get all the attention for this purpose, but currently, the commercial cultivation of flowers is becoming a numerous beneficial investment. As a consequence, the rose(<i>Genus Rosa</i>) is one of the most beautiful and commercially demanding flowers among different flowers. However, insecticide resis ...[more]

PMID: 35966946

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Project description:A Mobile Monitoring System (MMS) has been designed taking into account the use of technology with high sensor accuracy and the ability to be installed easily and quickly in different cardinal locations, distribution spaces, volumes and at different heights of a tertiary in-use building located in Leioa (Bilbao). Two types of MMS have been designed with the objective of carrying out two types of analysis; one intended to do a global indoor air temperature uncertainty analysis and the other focused on doing a global outdoor air temperature uncertainty analysis. Eight tripods make up the interior MMS with twenty sensors at different heights, which have been installed in different offices in the building to collect indoor air temperature measurements at different heights and locations. In addition, eight sensors make up the exterior MMS to collect data from outdoor air temperature measurements around the building envelope. Both MMS have been integrated into the existing Building Automation System (BAS) of the tertiary building; some other data collected by the BAS has also been taken into account for the uncertainty analysis of indoor and outdoor air temperature. The interior and exterior MMS datasets have been compiled based on a rigorous data collection process, with the potential to use the data to study the spatial air temperature behavior, taking into account the impact of solar radiation, the heating system and the electrical energy consumption. Furthermore, it enables the global uncertainty of indoor and outdoor air temperature measurements on an in-use building to be estimated and to break it down into the different uncertainty sources, such as the sensor accuracy, vertical and horizontal temperature variability, solar radiation, occupancy and heating system effects. Finally, it enables the optimization of monitoring and control systems for BAS, heating and HVAC systems, as well as any monitoring system implemented in research tests using indoor and/or outdoor temperature measurements as key variables.

Dataset Information

RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose.

Publications

RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose.

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