Open AccessPredictive model of water stress in tenera oil palm by means of spectral signature methods
Author(s) -
Angie Marcela Galvez Valencia,
Yeison Alberto Garcés-Gómez,
Erwin Leandro Lemus Rodriguez,
Miguel Arango
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i3.pp2680-2687
Subject(s) - normalized difference vegetation index , environmental science , agriculture , agricultural engineering , palm , tenera , soil texture , water content , regression analysis , palm oil , mathematics , soil water , soil science , agroforestry , agronomy , statistics , ecology , biology , geology , physics , geotechnical engineering , quantum mechanics , leaf area index , engineering
Agriculture as a competitive business, seeks to improve productivity within crops with a more sustainable environmental management. It is important that agriculture includes new technologies that allow it to generate differential, precise and real-time information. In Colombia, the current lack of knowledge about techniques that allow early identification of water stress in African palm could generate a loss in the investment made in the fertilization of the crop, cause an increase in diseases, pests, and susceptibility to compaction or abortions in female flowers that would lead to decreases in production. In this work, a predictive model is established to quantify water stress based on spectral, physiological and soil information in African palm plants. To this end, a study was carried out in an oil palm plantation where treatments were established with 3 ranges of humidity. It was found that the indices with the highest correlation with the biophysical variable soil moisture were: NDVI_1 and NDVI_16 for treatment 1, SR_4 for treatment 2 and NDVI_16 and NDVI_20 for treatment 3. Finally, the third order polynomial regression model that obtained higher correlation coefficients of Pearson R^2=0.73 was selected as the most suitable model to estimate soil moisture content for treatments 2 and 3.