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JRM Vol.36 No.3 pp. 721-731
doi: 10.20965/jrm.2024.p0721
(2024)

Paper:

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

Yuki Hirata*, Satoki Tsuichihara* ORCID Icon, Yasutake Takahashi* ORCID Icon, and Aki Mizuguchi** ORCID Icon

*University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

**Fukui Prefectural University
88-1 Futaomote, Awara, Fukui 910-4103, Japan

Received:
November 20, 2023
Accepted:
February 6, 2024
Published:
June 20, 2024
Keywords:
agriculture image processing, aerial image captured by drone, image alignment, automatic dataset generation, weed area estimation
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.

Alignment of two season’s orthomosaic images taken by drone

Alignment of two season’s orthomosaic images taken by drone

Cite this article as:
Y. Hirata, S. Tsuichihara, Y. Takahashi, and A. Mizuguchi, “Dataset Generation and Automation to Detect Colony of Morning Glory at Growing Season Using Alignment of Two Season’s Orthomosaic Images Taken by Drone,” J. Robot. Mechatron., Vol.36 No.3, pp. 721-731, 2024.
Data files:
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Last updated on Jul. 12, 2024