International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 3, June 2021, pp. 2680~2687 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp2680-2687  2680 Journal homepage: http://ijece.iaescore.com Predictive model of water stress in Tenera oil palm by means of spectral signature methods Angie Marcela Galvez-Valencia 1 , Yeison Alberto Garcés-Gómez 2 , Erwin Leandro Lemus Rodríguez 3 , Miguel Andrés Arango Argoti 4 1,3 Manuelita Aceites y Energía, Aceites Manuelita S.A., Colombia 2 Universidad Católica de Manizales, Unidad Académica de Formación en Ciencias Naturales y Matemáticas, Colombia 4 Corporación Colombiana de Investigación Agropecuaria-AGROSAVIA, Colombia Article Info ABSTRACT Article history: Received Aug 10, 2020 Revised Jan 8, 2021 Accepted Jan 19, 2021 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. Keywords: Predictive model Remote sensing Spectral signature Tenera oil palm Water stress This is an open access article under the CC BY-SA license. Corresponding Author: Yeison Alberto Garcés-Gómez Unidad Académica de Formación en Ciencias Naturales y Matemáticas Universidad Católica de Manizales Cra 23 No 60 - 63, Manizales, Caldas, Colombia Email: ygarces@ucm.edu.co 1. INTRODUCTION Climate variability has made it difficult to analyse weather conditions that are indispensable for planning in agriculture. Currently, this planning of agricultural activities varies constantly according to the study of the climatic events that occur day by day. Therefore, it is essential to develop tools that allow a quick analysis of the information and that are available to the farmers [1-5]. In Colombia, many organizations from different sectors have advanced in the consolidation of these tools. However, in the case of oil palm, the current information is insufficient, which does not allow most palm growers to plan their activities better [6, 7]. In this aspect the use of satellite images allows an important development in many aspects of humanity. In the field of agriculture it is presented as a low-cost and highly applicable tool for the improvement of conditions or productivity rates that can help in the development and quality of food security [8-10].