Rice Productivity Estimation Using Remote Sensing Method

Abstract

The calculation of crop productivity has now been facilitated by technological development using remote sensing technology or data generated by satellites. Determining the value of productivity using images will shorten the time and does not require much effort. A remote sensing model that connects satellite image reflectance data with rice plant parameters will be handy for monitoring biomass growth and predicting crop yields more quickly and efficiently. This study aimed to determine the regression equation to estimate the productivity of regional rice in Harau District, Lima Puluh Kota Regency. This study consisted of several stages: data collection, data processing, and calculation of rice productivity, NDVI regression analysis with rice productivity, and Nash-Sutcliffe Efficiency (NSE) test against the obtained equations. The regression equation obtained from the results of data analysis to estimate rice productivity in Harau District, Lima Puluh Kota Regency is y = - 82152x4 + 208465x3 - 197654x2 + 82986x - 13014, with an NSE value of 0.64 which is categorized as ‘sufficient’.Keywords: MODIS, NDVI, Rice, Remote sensing; Productivity

Estimation of Rice Productivity Using the Normalized Difference Vegetation Index (Ndvi) Algorithm (Case Study of Gunung Talang District, Solok Regency)

“…Rice plants will experience a peak in the early generative phase (10-13 WAP) and when the rice plant is in a state of grain filling until the harvest period, it will decrease. The results research of Yanti et al (2022), show that NDVI value increased during the vegetative phase and decreased when it entered the generative phase until harvest. The graph in Figure 4 continued to decline after experiencing a peak period, namely at the age of 95 days to a fallow period of 120 days.…”

“…The results research of Yanti et al (2022), monitoring rice productivity in Harau District, Lima Puluh Kota Regency with MODIS showed the value of the validity of the equation model linking NDVI with rice productivity of 0.64 which was categorized as sufficient with polynomial trend line. The results research of Prasetyo et al (2018), namely a model for estimating rice productivity with NDVI and LSWI parameters, show that the NDVI and LSWI values affect the value of rice productivity by 63.9% while the remaining 36.1% is caused by other factors.…”

Molecular screening of local Indonesian rice to identified resistant varieties against brown planthopper (Nilaparvata lugens) attacks

“…The number of tillers of a rice variety is influenced by genetic traits and environmental factors, which play an essential role in determining the productivity of rice plants (Yanti et al 2022). The number of productive tillers was divided into three groups, namely fewer tillers (less than 12 stems per clump), medium tillers (13-20 stems per clump), and many tillers (more than 20 stems per clump) (Ekamber 2014).…”