Dataset Generation and Automation to Detect Colony of Morning Glory at Growing Season Using Alignment of Two Season’s Orthomosaic Images Taken by Drone

Abstract

Weed control has a significant impact on crop yield during cultivation. In this research, semantic segmentation is used to detect morning glories in soybean fields. By removing morning glory earlier in the growing season, the decrease in soybean crop yield can be minimized. However, it is difficult to create annotated images necessary for semantic segmentation at the growing season because soybeans and morning glories are both green and similar in color, making it difficult to distinguish them. This research assumes that morning glory colonies, once located at the growing season, remain stationary during the harvest season. The colonies of the morning glory at the growing season are identified by aligning the orthomosaic image from the growing season with the orthomosaic image from the harvest season because the leaves of the soybeans wither and turn brown during the harvest season. The proposed method trains a model of morning glory at the growing season based on its location at the harvest season and estimates the colonies of morning glory on the harvest season orthomosaic image. In this research, we investigated the accuracy of a deep learning-based morning glory detection model and discovered that the performance of the model varied depending on the proportion of morning glory areas on each image in the training dataset. The model demonstrated an optimal performance when only 3.5% of the proportion of the morning glory areas achieved an F2 score of 0.753.