Dataset Information

VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system.

ABSTRACT: Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset "VegNet' to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot,downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms. The dataset is hosted by the Institute - National Institute of Textile Engineering and Research (NITER),the Department of Computer Science and Engineering and is available at the link following: https://data.mendeley.com/datasets/t5sssfgn2v/3.

SUBMITTER: Sara U

PROVIDER: S-EPMC9256540 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeThe purpose of this study was to develop an auto-planning platform to be interfaced with a commercial treatment planning system (TPS). The main goal was to obtain robust and high-quality plans for different anatomic sites and various dosimetric requirements.MethodsMonaco (Elekta, St. Louis, US) was the TPS in this work. All input parameters for inverse planning could be defined in a plan template inside Monaco. A software tool called Robot Framework was used to launch auto-planning trials with updated plan templates. The template modifier external to Monaco was the major component of our auto-planning platform. For current implementation, it was a rule-based system that mimics the trial-and-error process of an experienced planner. A template was automatically updated by changing the optimization constraints based on dosimetric evaluation of the plan obtained in the previous trial, along with the data of the iterative optimization extracted from Monaco. Treatment plans generated by Monaco with all plan evaluation criteria satisfied were considered acceptable, and such plans would be saved for further evaluation by clinicians. The auto-planning platform was validated for 10 prostate and 10 head-and-neck cases in comparison with clinical plans generated by experienced planners.ResultsThe performance and robustness of our auto-planning platform was tested with clinical cases of prostate and head and neck treatment. For prostate cases, automatically generated plans had very similar plan quality with the clinical plans, and the bladder volume receiving 62.5 Gy, 50 Gy, and 40 Gy in auto-plans was reduced by 1%, 3%, and 5%, respectively. For head and neck cases, auto-plans had better conformity with reduced dose to the normal structures but slightly higher dose inhomogeneity in the target volume. Remarkably, the maximum dose in the spinal cord and brain stem was reduced by more than 3.5 Gy in auto-plans. Fluence map optimization only with less than 30 trials was adequate to generate acceptable plans, and subsequent optimization for final plans was completed by Monaco without further intervention. The plan quality was weakly dependent on the parameter selection in the initial template and the choices of the step sizes for changing the constraint values.ConclusionAn automated planning platform to interface with Monaco was developed, and our reported tests showed preliminary results for prostate and head and neck cases.

Project description:BackgroundHuman transmission of Chagas disease (CD) most commonly occurs in domiciliary spaces where triatomines remain hidden to feed on blood sources during inhabitants' sleep. Similar to other neglected tropical diseases (NTDs), sustainable control of CD requires attention to the structural conditions of life of populations at risk, in this case, the conditions of their living environments. Considering socio-cultural and political dynamics involved in dwellings' construction, this study aimed to explore social factors that contribute or limit sustainability of CD's prevention models focused on home improvement.Methods and main findingsUsing Healthy Homes for Healthy Living (HHHL)-a health promotion strategy focused on improvement of living environments and system-based health promotion-as a reference, a qualitative study was conducted. Research participants were selected from three rural communities of a CD endemic region in southern Ecuador involved in HHHL's refurbishment and reconstruction interventions between 2013 and 2016. Folowing an ethnographic approach, data were collected through interviews, participant observation, informal conversations and document analysis. Our results indicate that the HHHL model addressed risk factors for CD at the household level, while simultaneously promoting wellbeing at emotional, economic and social levels in local communities. We argue that sustainability of the CD prevention model proposed by HHHL is enhanced by the confluence of three factors: systemic improvement of families' quality of life, perceived usefulness of control measures, and flexibility to adapt to emerging dynamics of the context.ConclusionHHHL's proposed home improvement, facilitated through system-based rather than disease specific health promotion processes, enhances agency in populations at risk and facilitates community partnerships forged around CD prevention. Although an independent analysis of cost-effectiveness is recommended, structural poverty experienced by local families is still the most important factor to consider when evaluating the sustainability and scalability of this model.

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

VegNet: An organized dataset of cauliflower disease for a sustainable agro-based automation system.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets