Estimation of Moisture Content in Cucumber and Watermelon Seedlings Using Hyperspectral Imagery
2018
Kim, S.H., Gyeongsang National University, Jinju, Republic of Korea | Kang, J.G., Gyeongsang National University, Jinju, Republic of Korea | Ryu, C.S., Gyeongsang National University, Jinju, Republic of Korea | Kang, Y.S., Gyeongsang National University, Jinju, Republic of Korea | Tapash Kumar Sarkar, Gyeongsang National University, Jinju, Republic of Korea | Kang, D.H., National Institute of Agricultural Sciences, RDA, Jeonju, Republic of Korea | Ku, Y.G., Wonkwang University, Iksan, Republic of Korea | Kim, D.E., Korea National College of Agriculture and Fisheries, Jeonju, Republic of Korea
This research was conducted to estimate moisture content in cucurbitaceae seedlings, such as cucumber and watermelon, using hyperspectral imagery. Using a hyperspectral image acquisition system, the reflectance of leaf area of cucumber and watermelon seedlings was calculated after providing water stress. Then, moisture content in each seedling was measured by using a dry oven. Finally, using reflectance and moisture content, the moisture content estimation models were developed by PLSR analysis. After developing the estimation models, performance of the cucumber showed 0.73 of R2 , 1.45% of RMSE, and 1.58% of RE. Performance of the watermelon showed 0.66 of R2 , 1.06% of RMSE, and 1.14% of RE. The model performed slightly better after removing one sample from cucumber seedlings as outlier and unnecessary. Hence, the performance of new model for cucumber seedlings showed 0.79 of R2 , 1.10% of RMSE, and 1.20% of RE. The model performance combined with all samples showed 0.67 of R2 , 1.26% of RMSE, and 1.36% of RE. The model of cucumber showed better performance than the model of watermelon. This is because variables of cucumber are consisted of widely distributed variation, and it affected the performance. Further, accuracy and precision of the cucumber model were increased when an insignificant sample was eliminated from the dataset. Finally, it is considered that both models can be significantly used to estimate moisture content, as gradients of trend line are almost same and intersected. It is considered that the accuracy and precision of the estimating models possibly can be improved, if the models are constructed by using variables with widely distributed variation. The improved models will be utilized as the basis for developing low-priced sensors.
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