Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.
Objective: To determine the research activities and perceived barriers among house officers working in different institutions in Rawalpindi/ Islamabad.
Study Design: Cross-sectional study.
Place and Duration of Study: Private and Government Hospitals in Rawalpindi and Islamabad Pakistan, from May to Oct 2019.
Methodology: A questionnaire based on thirteen questions with close-ended answers was administered to house officers at different dental hospitals in Rawalpindi and Islamabad to observe the attitudes toward research and perceived difficulties in doing research.
Results: A total of 126 participants with the mean age of 24.26±2.192 years participated in the study. Out of 126, 68(58.3%) participants showed research interest, and 86(68.25%) participants reported no previous experience with research. In addition, barriers/difficulties in the research were noted regarding personal interests, funding, future job commitment and the data collection process.
Conclusions: Within the limitations of this study, it is concluded that the barriers identified in this study need to be addressed in order to enhance students' participation in research and improve the quality of research done in our country: these barriers such as lack of interest, lack of funding, poor availability of research mentors and proper awareness should be removed. Amendments may be required in the dental undergraduate curriculum to overcome these barriers.
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