Yield Prediction of Chinese Cabbage (Brassicaceae) Using Broadband Multispectral Imagery Mounted Unmanned Aerial System in the Air and Narrowband Hyperspectral Imagery on the Ground

• Published in: Journal of biosystems engineering 2018-06, Vol.43 (2), p.138
• Main Authors: Ye Seong Kang, Chan Seok Ryu, Seong Heon Kim, Sae Rom Jun, Si Hyeong Jang, Jun Woo Park, Tapash Kumar Sarkar, Hye Young Song
• Format: Article
• Language: Korean
• Subjects: Chinese cabbage; hyperspectral image; linear regression model; multispectral image; UAV
• ISSN: 1738-1266

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its R 2 is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for R2, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for R 2 , 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. I f the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.