[논문]다층신경망을 이용한 드론 방제의 살포 균일도 예측

성백겸; 강승우; 조수현; 한웅철; 유승화; 이춘구; 강영호; 이대현

doi:10.7839/ksfc.2023.20.3.025

다층신경망을 이용한 드론 방제의 살포 균일도 예측
Predicting the spray uniformity of pest control drone using multi-layer perceptron 원문보기

드라이브ㆍ컨트롤 = Journal of drive and control, v.20 no.3, 2023년, pp.25 - 34

성백겸 (Department of Biosystem Machinery Engineering, Chungnam National University) , 강승우 (Department of Biosystem Machinery Engineering, Chungnam National University) , 조수현 (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, Jeollabuk-do Agricultural Research and Extension Science) , 이대현 (Department of Biosystem Machinery Engineering, Chungnam National University)

Abstract ▼ AI-Helper

In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R2), the test samples showed an R2 of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

주제어

표/그림 (17)

그림 Fig. 1 A photo of rail system (left) and fan system (right)
그림 Fig. 2 A photo of Drone (SG-10P) used in this test
그림 Fig. 3 A photo of WSP bed (a) and the WSPs arrangement by 3 rows and 17 columns (b)
표 Table 1 Specification of drone
표 Table 2 Condition of test flight
그림 Fig. 4 A photo of Image processing system (left) and processed WSP sample (right)
그림 Fig. 5 Example of spray pattern
그림 Fig. 6 Example of spraying method
그림 Fig. 7 Example of overlapping spray pattern in back and forth (a) and in race and track (b)
그림 Fig. 8 Multi-layer perceptron model structure
표 Table 4 Coefficient of variation prediction condition 2
표 Table 3 Coefficient of variation prediction condition 1
그림 Fig. 9 Graphs of loss
그림 Fig. 10 Coefficient of variation comparison in train data set (a) and validation data set (b) and test data set (c)
표 Table 5 Coefficient of variation predict performance evaluation metric
그림 Fig. 11 Coefficient of variation comparative analysis
그림 Fig. 12 Coefficient of variation comparative analysis at when swath width 4m (a) and swath width 5m (b)

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