<abstract xmlns="http://www.w3.org/1999/xhtml">

Indonesia’s rice production has decreased by 6.83% (on average) in the last five years (2015 – 2019) because of some factors. Salinity (42%) is one of the leading factors that cause decreasing rice production besides climate change (21%), drought (9%), and other factors (28%). The smartphone camera serves as an alternative technology to prevent macronutrient deficiencies due to salinity. This study used aerial photos from android with visible light (R, G, and B), and the image was taken from a height of 5 m. The observation of macronutrient content in plant biomass was carried out using a free grid to adjust rice fields and saline soil. The formula was obtained from regression analysis and paired t-test between the biomass macronutrient and the extracted digital number of aerial photographs that have been stacked. The results showed that digital number (DN) from a smartphone was reliable to predict nitrogen (N), phosphorus (P), and potassium (K) content in rice with formula N = 0.0035 * DN + 0.8192 (<italic>R</italic>2 0.84), P = 0.0049 * DN – 0.2042 (<italic>R</italic>2 0.70), and K = 0.0478 * DN – 2.6717 (<italic>R</italic>2 0.70). There was no difference between the macronutrient estimation results from the formula and the field’s original data.
</abstract>

Indonesia’s rice production has decreased by 6.83% (on average) in the last five years (2015 – 2019) because of some factors. Salinity (42%) is one of the leading factors that cause decreasing rice production besides climate change (21%), drought (9%), and other factors (28%). The smartphone camera serves as an alternative technology to prevent macronutrient deficiencies due to salinity. This study used aerial photos from android with visible light (R, G, and B), and the image was taken from a height of 5 m. The observation of macronutrient content in plant biomass was carried out using a free grid to adjust rice fields and saline soil. The formula was obtained from regression analysis and paired t-test between the biomass macronutrient and the extracted digital number of aerial photographs that have been stacked. The results showed that digital number (DN) from a smartphone was reliable to predict nitrogen (N), phosphorus (P), and potassium (K) content in rice with formula N = 0.0035 * DN + 0.8192 (R2 0.84), P = 0.0049 * DN – 0.2042 (R2 0.70), and K = 0.0478 * DN – 2.6717 (R2 0.70). There was no difference between the macronutrient estimation results from the formula and the field’s original data.

Rapid Identification of Rice Macronutrient Content in Saline Soils Using Smartphone Camera

Agriculture (Pol'nohospodárstvo)

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Indonesia’s rice production has decreased by 6.83% (on average) in the last five years (2015 – 2019) because of some factors. Salinity (42%) is one of the...

Rapid Identification of Rice Macronutrient Content in Saline Soils Using Smartphone Camera

Data publikacji: 05 sie 2021

Zakres stron: 61 - 75

Otrzymano: 02 lut 2021

Przyjęty: 02 cze 2021

DOI: https://doi.org/10.2478/agri-2021-0006

Słowa kluczowe
remote sensing, visible light, android, nitrogen, phosphorus, potassium, salinity

© 2021 Aditya Nugraha Putra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Rapid Identification of Rice Macronutrient Content in Saline Soils Using Smartphone Camera

Data publikacji: 05 sie 2021

Zakres stron: 61 - 75

Otrzymano: 02 lut 2021

Przyjęty: 02 cze 2021

DOI: https://doi.org/10.2478/agri-2021-0006

Słowa kluczoweremote sensing, visible light, android, nitrogen, phosphorus, potassium, salinity

© 2021 Aditya Nugraha Putra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
remote sensing, visible light, android, nitrogen, phosphorus, potassium, salinity