[논문]드론 방제의 최적화를 위한 딥러닝 기반의 밀도맵 추정

성백겸; 한웅철; 유승화; 이춘구; 강영호; 우현호; 이헌석; 이대현

doi:10.7839/ksfc.2024.21.2.053

[국내논문] 드론 방제의 최적화를 위한 딥러닝 기반의 밀도맵 추정
Density map estimation based on deep-learning for pest control drone optimization

드라이브ㆍ컨트롤 = Journal of drive and control, v.21 no.2, 2024년, pp.53 - 64

성백겸 (Department of Biosystem Machinery Engineering, Chungnam National University) , 한웅철 (Department of Biosystems Engineering, Kangwon National University) , 유승화 (Department of Agricultural Engineering, National Institute of Agricultural Science) , 이춘구 (Department of Agricultural Engineering, National Institute of Agricultural Science) , 강영호 (Department of Crops and Foods, Jeonbuk State Agricultural Research and Extension Services) , 우현호 (Dronedivisison Co. Ltd) , 이헌석 (Woongjin Machinery Co. Ltd) , 이대현 (Department of Biosystem Machinery Engineering, Chungnam National University)

Abstract ▼ AI-Helper

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R2) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

주제어

표/그림 (23)

그림 Fig. 1 Photo of drone (SG-10P) used in this study
그림 Fig. 2 XR Nozzel Series (a) and AI Nozzle (b)
표 Table 1 Specification of drone (SG-10P)
표 Table 2 Specification of nozzle
그림 Fig. 3 Photo of indoor test system (a) and graphical representations of WSP bed (b) used in this study
그림 Fig. 4 Photo of test location with drone (a) and graphical representations of test flight (b) used in this study
표 Table 3 Test flight condition of indoor test system
그림 Fig. 7 Examples of data annotation process
표 Table 4 Specification of sensor (WTS-800)
그림 Fig. 5 Photo of sensor used in this study
그림 Fig. 6 Photo of digital image processing system
그림 Fig. 8 Architecture of model used in this study
그림 Fig. 9 Graphical representations of image patch
그림 Fig. 10 Curves of loss by weighting method
표 Table 4 Condition of comparing weight
표 Table 5 Condition of density classification
표 Table 6 Learning time by weighting method
표 Table 7 Evaluation result by weighting method
그림 Fig. 11 Scatter plot of linear regression analysis in test data set
그림 Fig. 12 Examples of density map estimation
표 Table 8 Result of statistical analysis
그림 Fig. 15 Scatter plot of linear regression analysis in dense (a) and medium (b) and sparse (c)
표 Table 9 Number of samples by density

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