Simplified image analysis for early detection of missing and low-vigor cabbage plants using UAV RGB imagery and deep learning

Kyeong Soo Jeong; Seong Hwan Go; Won Ki Jo; Jong Hwa Park

doi:10.7744/kjoas.520105

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2025 Vol.52, Issue 1 Preview Page

Engineering

Simplified image analysis for early detection of missing and low-vigor cabbage plants using UAV RGB imagery and deep learning

1 March 2025. pp. 51-65

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Abstract

Early and accurate detection of missing and low-vigor plants is crucial for optimizing crop yields and resource use in agriculture. Traditional manual scouting methods are time-consuming and labor-intensive. This study addresses the need for a rapid and efficient solution for assessing stand establishment in cabbage crops. This research presents a simplified image analysis pipeline for the early detection of missing and low-vigor cabbage plants using unmanned aerial vehicle (UAV) RGB imagery and a deep learning-based object detection model. The system employs a Faster region-based convolutional neural networks (R-CNN) model with a ResNet-50 feature pyramid network (FPN) backbone for plant detection. A streamlined preprocessing workflow, incorporating feature extraction using ResNet-50 FPN, enhances image contrast. Plant vigor is assessed using a computationally efficient, area-based classification method, with the 30th percentile of predicted bounding box areas serving as the threshold between “healthy” and “low-vigor” plants. This threshold was determined through visual assessment and iterative testing. Missing plants (also known as “knots”) are identified by comparing detected plant locations with a grid of expected planting positions, generated from known planting patterns and refined by removing overlaps with detected plants. The Faster R-CNN model achieved an F1-score of 0.90 on a test dataset (using a confidence score threshold of 0.7). A field trial in a Smart Organic Farming Demonstration Complex in Goesan-gun, South Korea, revealed a significant stand establishment issue: a missing plant rate of 54.81% and 13.19% of plants classified as low-vigor. The developed system, combining UAV RGB imagery, deep learning, and simplified image analysis, offers a practical and efficient solution for precise crop monitoring. Visualizations and a geo-referenced planting map provide actionable information for targeted replanting. This research demonstrates significant advancements over traditional methods, paving the way for more sustainable and resilient agricultural practices by enabling timely intervention to minimize yield loss in the critical early stages of cabbage growth.

Keywords

deep learning

early detection of abnormal plant

Faster R-CNN

ResNet-50 FPN

UAV

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Information

Publisher :Institute of Agricultural Science, Chungnam National University
Publisher(Ko) :충남대학교 농업과학연구소
Journal Title :Korean Journal of Agricultural Science
Journal Title(Ko) :농업과학연구
Volume : 52
No :1
Pages :51-65
Received Date : 2025-02-07
Revised Date : 2025-02-24
Accepted Date : 2025-02-25
DOI :https://doi.org/10.7744/kjoas.520105

Korean Journal of Agricultural Science ISSN:2466-2402(Print) 2466-2410(Online) 농업과학연구

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