RESUMO
Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.
RESUMO
INTRODUCTION: Train-pedestrian conflicts result in a substantial number of serious and fatal injuries annually. Signs indicating safe and permissible behaviors near railroad rights of way are commonly relied upon to mitigate collisions. However, the effectiveness of these signs in preventing accidents often depends on clarity and interpretation of the sign. The objective of this study was to evaluate the (1) effectiveness of sign messaging strategies and designs at pedestrian-railroad crossings, and (2) effects of context and risk-taking propensity on decisions to cross tracks. METHOD: A survey study (N = 1011) was conducted comparing likeliness to cross for a variety of pedestrian-railroad scenarios. The DOSPERT scale was included to estimate an individuals' risk-taking. RESULTS: Findings reveal that action-conveying and emotionally-motivated signs are more effective in discouraging railroad crossing in high-risk situations (i.e., train present, crossing gates down, or warning lights flashing), compared to information-only signs. However, decisions to cross are primarily influenced by the presence of a train and crossing gates' status, followed by sign type. MaxDiff analysis show that yellow signs with black symbols and square shaped signs are perceived as the clearest in conveying safety information, compared to black on white, red on white, and circular signs. Additionally, individuals who cross railroad tracks as pedestrians more frequently exhibit higher risk-taking tendencies, while there is no relationship between driving across tracks and risk-taking propensity. Males and younger individuals also have higher risk-taking tendencies. CONCLUSIONS AND PRACTICAL APPLICATIONS: These findings have implications for policy and practice, such as revising manuals to incorporate more effective sign designs and targeted educational campaigns for high-risk groups. It is also crucial to conduct ongoing monitoring of implemented interventions, which could follow the framework presented in this paper. The study emphasizes collaboration across sectors to improve overall safety at pedestrian-railroad crossings, contributing to safer transportation infrastructure for all.
