AbstractIncreasing efficiency and productivity in the field of agriculture is important to provide sufficient food to the world’s increasing population. It is important to monitor crops using image processing in order to realize these increases in efficiency and productivity. In order to monitor crops with high quality and accuracy, high resolution images are needed. In this research, a crop monitoring method for pecan nut trees was developed using high-resolution video taken from the side of a vehicle. First, trees were extracted by applying an object detection model to the video data. Second, the extracted trees were divided into canopy and trunk areas. Finally, using labels made by experts and the canopy image as input, the convolutional neural network (CNN) model was trained to classify unhealthy and healthy trees. The model achieved an area under the curve for classification over 0.95. Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to the model for the purpose of evaluation, and it clarified that the model is focusing on the hollow features of the canopy when performing its classification.
RE-APPRAISAL OF THE CAVOTRICUSPID REGION AS A FUNCTIONAL ISTHMUS DURING ATRIAL FLUTTER: A HIGH RESOLUTION ACTIVATION MAPPING STUDY
