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All Journal JTSL (Jurnal Tanah dan Sumberdaya Lahan)
Aini, Yuli Kurrotul Binti
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Agriculture, Biological Sciences & Forestry Environmental Science

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HUBUNGAN INDEKS VEGETASI DENGAN KLOROFIL DAN NITROGEN PADA DAUN TANAMAN JAGUNG BERBASIS CLOUD COMPUTING PLATFORM Rahman, Fahmi Arief; Aini, Yuli Kurrotul Binti; Prasetyo, Syukur Toha; Suhartono; Suryawati, Sinar
JTSL (Jurnal Tanah dan Sumberdaya Lahan) Vol. 13 No. 1 (2026)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2026.013.1.14

Abstract

Vegetation indices are algorithms that represent aspects of vegetation such as leaf area index, biomass, and chlorophyll concentration. The index is thought to be used to determine chlorophyll and nitrogen content which makes it more effective and efficient. The purpose of this study is to determine the relationship between vegetation indices and chlorophyll and nitrogen content of corn leaves through a cloud computing platform. Vegetation indices were obtained from Sentinel 2A image processing with Google Earth Engine. Chlorophyll and nitrogen content were obtained from laboratory analysis. This study used 13 sample points in corn crop areas spread across Dlanggu, Bangsal, and Jatirejo sub-districts in Mojokerto district. The correlation test was used to determine the level of relationship between variables. The results showed that the vegetation index generated from image processing through GEE had no correlation with chlorophyll and nitrogen content from laboratory analysis. The weak correlation was thought to be due to atmospheric effects such as clouds, as well as climatic effects such as the rainy season and cloudy skies, which cause the calculation of vegetation indices to be disrupted. The dominant phase of the sample plants in the early generative phase and the possibility of errors in the analysis of chlorophyll and nitrogen content were thought to contribute to the lack of correlation in this study.
Co-Authors Fahmi Arief Rahman Prasetyo, Syukur Toha Sinar Suryawati Suhartono