ABSTRACT
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.
ABSTRACT
Coronavirus infecious disease 2019 (COVID-19) pandemic has brought a myriad of challenges to the dental education. Amidst the quarantine and lockdown measures, face-to-face education is disrupted around the globe. Dental educators have come up with innovative solutions to resume dental education remotely. Different online platforms are being utilised for didactic teaching and learning as well as for students 2019; assessment and examination. Clinical learning has also shifted to virtual learning. Manikins and virtual reality/augmented reality (VR/AR)-based simulation devices along with haptic technology can be very helpful for skills training. However, some sorts of blended learning and virtual curriculum may be incorporated in dental education in the future. For this narrative review, a thorough in-depth review of the available literature, relevant to our field, was carried out. In this article, impact of COVID-19 on dental education has been discussed along with some solutions to these challenges. Key Words: COVID-19, Dental education, E-learning, Virtual/augmented reality.
