Estimation of the Moisture Content in Solanaceae Seedlings using Hyperspectral Image Sae-rom Jun*1, Chan-seok Ryu1, Seong-heon Kim1, Jeong-gyun Kang1, Ye-seong Kang1, Won-jun Kim1, Tapash Kumar Sarkar1, Si-hyeong Jang1, Dong-hyeon Kang2, Yang-gyu Ku3, Dong-eok Kim4 1Department of Bio-Systems Engineering, College of Agriculture and Life Science, Gyeongsang National University (Institute of Agriculture and Life Science), Jinju 52828, Republic of Korea 2Department of Agricultural Engineering, National Academy of Agricultural Science, RDA, Jeonju 54875, Republic of Korea 3Department of Horticulture Industry, College of Life Science and Resource, Wonkwang University, Iksan 54538, Republic of Korea 4Department of General Education, Korea National College of Agriculture and Fisheries, Jeonju, 54874, Republic of Korea Introduction This research was performed to develop the moisture prediction model for salanaceae seedlings such as chili pepper and tomato based on hyperspectral imagery. Materials and Methods The reflectance of chili pepper(n=45) and tomato (n=45) seedlings were calculated using the hyperspectral imagery, and the moisture content of all seedlings was measured. Then the predicting models for estimating moisture content were developed using PLS-Regression analysis with two factors Equipment Sample Schedule Image Processing Materials and Methods Statistical Analysis Results and Discussion The chilli model showed 0.68 of R2, 1.43% of RMSE and 1.61% of RE, which indicate accuracy and precision respectively. The tomato model showed 0.74 of R2, 2.77% of RMSE and 3.09% of RE. Combining all samples (n=90), the solanaceae model showed 0.67 of R2, 2.53% of RMSE and 1.61% of RE. Finally the full-cross validation showed 0.59 of R2, 2.83% RMSE and 3.17% of RE. Moisture Content Reflectance Ratio PLS Regression Model For the normalization, hyperspectral images of chili pepper and tomato seedlings were calibrated by the values of reference board in same image and camera noises were removed by dark current image. For the segmentation, The images were divided into the crop and background area by GNDVI (Green normalized difference vegetation index) - NDVI (normalized difference vegetation index). For the extraction, The images were calculated by image processing program (ENVI 4.7, Exelis Visual Information, USA). Variety Chilly Pepper (n=45) Tomato (n=45) Solanaceae (n=90) Moisture Content[%] (Mean±S.D.) 91.6 ± 2.83 89.6 ± 5.48 89.2 ± 4.27 LV 5 5 5 Cal. R² 0.68 0.74 0.67 RMSE [%] 1.43 2.77 2.53 RE [%] 1.61 3.09 2.84 Val. R² 0.45 0.60 0.59 RMSE [%] 1.88 3.44 2.83 RE [%] 2.12 3.84 3.17 Tomato (n=45) Chilly Pepper(n=45) Moisture Content [%] (Mean±S.D.) 89.9±5.60 88.8±2.56 Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project tile: Development of Heating and Cooling Energy Reduction Technology using Seedling Quality Monitoring for Nursery, Project No. PJ0116932017)" Rural Development Administration, Republic of Korea.Image ProcessingStatistical Analysis Hyperspectral Camera Estimation of Moisture Content 75 80 85 90 95 100 75 80 85 90 95 100 Measured moisture content [%] Predicted moisture content [%] Calibration Validation Fig. 1. Estimating Mechanism - Hyperspectral Camera Device name : VNIR spectral camera PS Manufacturer : Specim Spectral imaging Ltd, Finland Photographed Wavelength Range : 400 ~ 1000nm (resolving power : 2nm) - Dry Oven Device name : VS-4048D Manufacturer : Vision Scientific Co. Ltd., Korea Temperature range : Ambient +5 ℃ ~ 220 ℃ Capacity : 48L - Software Spectral DAQ ENVI 5.2 R project 3.3.3 Fig. 2. Hyperspectral Camera Fig. 3. Dry Oven Fig. 4. Software - Chili Pepper Vienna (53 growth days) - Tomato Noggwang (37 growth days) Fig. 5. Hyperspectral Image of Chili Pepper Fig. 6. Hyperspectral Image of Tomato Fig. 7. Schedule of Scanning and Sampling Reflectance Ratio PLS Regression Analysis Moisture Content R Full-Cross Validat