LAI, SPAD, and Yield of Paddy Rice Prediction from Data Hyperspectral Using Partial Least Square Regression (PLSR) Algorithm

Abstract

Hyperspectral is a new technology in remote sensing which exploits hundred of bands. Pusat Teknologi Inventarisasi Sumber Daya Alam Badan Pengkajian dan Penerapan Teknologi (PTISDA BPPT) applies hyperspectral in agriculture for yearly yield prediction. In this research, Leaf Area Index (LAI), number of chlorophyll (SPAD), and yield paddy yearly has been predicted with data hyperspectral using partial least square regression (PLSR) algorithm. Region used are Indramayu and Subang; the growth periods of paddy are vegetative, reproductive and ripening, while the heights of the spectral acquisition are 10 cm, 50 cm, and Hymap (2000 m). The data is owned PTISDA BPPT in cooperation with ERSDAC Japan. This research predicted R2 maximum for LAI 0.9500, SPAD 0.5262, and yield 0.4921. By using PLSR, LAI values can be predicted quite well, but not for the SPAD values and yield. This could be due to measurement error in the instrument of SPAD, while errors of yield are probably due to using a small area to represent a large area.