Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases.
Background: The health systems are not producing the desired output due to some factors such as insufficient skilled and experienced health personnel that lack motivation, lack of management skills, poor working and environment, and inadequate remuneration.
Aims of the study: The aim of study is to assess factors affecting of nursing work performance, and identify the relationship between some socio demographic characteristics of nurses, and the factors affecting of nursing work performance at Al-Hamdaniya general hospital in Mosul City.
Methodology: A study conducted by using a descriptive design to collect sample from nurses in Al-Hamdaniya general hospital. The study started from the 1st of November 2018 to the 10th of April 2019. The systematic random sample was (75) nurses. The validity of the questionnaire was established through a panel of (8) experts. The internal consistency for questionnaire was (r = 0.72).
Results: The study results show factors affecting of nursing work performance which include the increasing of workload were (49.3%), lack of communication between the nurse and the patient was (65.3%), effort and emotional fatigue (54.7%), the noise in the workplace (54.7%), and not give incentives and rewards (74.7%).
Conclusion: The study finding the most factors that affected of nurses' work such as workload and noise in the workplace, also, there relationship association between that the factors affecting of nurses and the socio-demographic characteristics. While shows no significant relationship between the factors affecting the working of nurses with age and tenure years.
Recommendations: The factor which causes the dissatisfaction among nurses with the working which advantage the hospital to helping nurses for continue their services. The manager uses new strategies at Al-Hamdaniya general hospital in Mosul City which leads to improving the nurses' performance circumstances
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.