Determination of soil nutrients and pH level using image processing and artificial neural network

John Carlo Puno; Edwin Sybingco; Elmer Dadios; Ira Valenzuela; Joel Cuello

doi:10.1109/HNICEM.2017.8269472

Determination of soil nutrients and pH level using image processing and artificial neural network

John Carlo Puno, Edwin Sybingco, Elmer Dadios, Ira Valenzuela, Joel Cuello

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

38 Scopus citations

Abstract

In this study, image processing and artificial neural network was used to efficiently identify the nutrients and pH level of soil with the use of Soil Test Kit (STK) and Rapid Soil Testing (RST) of the Bureau of Soils and Water Management: (1) pH, (2) Nitrogen, (3) Phosphorus, (4) Potassium, (5) Zinc, (6) Calcium, and (7) Magnesium. The composition of the system is made of five sections namely soil testing, image capturing, image processing, training system for neural network, and result. The use of Artificial Neural Network is to hasten the performance of image processing in giving accurate result. The system will base on captured image data, 70% for training, 15% for testing and 15% for validation as default of neural network tool of MATLAB. Based on the result, the program will show the qualitative level of soil nutrients and pH. Overall, this study identifies the soil nutrient and pH level of the soil and was proven accurate.

Original language	English (US)
Title of host publication	HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538609101
DOIs	https://doi.org/10.1109/HNICEM.2017.8269472
State	Published - Jul 2 2017
Event	9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2017 - Manila, Philippines Duration: Nov 29 2017 → Dec 1 2017

Publication series

Name	HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management
Volume	2018-January

Other

Other	9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2017
Country/Territory	Philippines
City	Manila
Period	11/29/17 → 12/1/17

Keywords

Artificial Neural Network
Digital Image Processing
MATLAB
Nutrients
Soil

ASJC Scopus subject areas

Ecological Modeling
Management, Monitoring, Policy and Law
Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction
Information Systems
Control and Optimization
Artificial Intelligence

Access to Document

10.1109/HNICEM.2017.8269472

Cite this

Puno, J. C., Sybingco, E., Dadios, E., Valenzuela, I., & Cuello, J. (2017). Determination of soil nutrients and pH level using image processing and artificial neural network. In HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (pp. 1-6). (HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HNICEM.2017.8269472

Determination of soil nutrients and pH level using image processing and artificial neural network. / Puno, John Carlo; Sybingco, Edwin; Dadios, Elmer et al.
HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Puno, JC, Sybingco, E, Dadios, E, Valenzuela, I & Cuello, J 2017, Determination of soil nutrients and pH level using image processing and artificial neural network. in HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2017, Manila, Philippines, 11/29/17. https://doi.org/10.1109/HNICEM.2017.8269472

Puno JC, Sybingco E, Dadios E, Valenzuela I, Cuello J. Determination of soil nutrients and pH level using image processing and artificial neural network. In HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management). doi: 10.1109/HNICEM.2017.8269472

Puno, John Carlo ; Sybingco, Edwin ; Dadios, Elmer et al. / Determination of soil nutrients and pH level using image processing and artificial neural network. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management).

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N2 - In this study, image processing and artificial neural network was used to efficiently identify the nutrients and pH level of soil with the use of Soil Test Kit (STK) and Rapid Soil Testing (RST) of the Bureau of Soils and Water Management: (1) pH, (2) Nitrogen, (3) Phosphorus, (4) Potassium, (5) Zinc, (6) Calcium, and (7) Magnesium. The composition of the system is made of five sections namely soil testing, image capturing, image processing, training system for neural network, and result. The use of Artificial Neural Network is to hasten the performance of image processing in giving accurate result. The system will base on captured image data, 70% for training, 15% for testing and 15% for validation as default of neural network tool of MATLAB. Based on the result, the program will show the qualitative level of soil nutrients and pH. Overall, this study identifies the soil nutrient and pH level of the soil and was proven accurate.

AB - In this study, image processing and artificial neural network was used to efficiently identify the nutrients and pH level of soil with the use of Soil Test Kit (STK) and Rapid Soil Testing (RST) of the Bureau of Soils and Water Management: (1) pH, (2) Nitrogen, (3) Phosphorus, (4) Potassium, (5) Zinc, (6) Calcium, and (7) Magnesium. The composition of the system is made of five sections namely soil testing, image capturing, image processing, training system for neural network, and result. The use of Artificial Neural Network is to hasten the performance of image processing in giving accurate result. The system will base on captured image data, 70% for training, 15% for testing and 15% for validation as default of neural network tool of MATLAB. Based on the result, the program will show the qualitative level of soil nutrients and pH. Overall, this study identifies the soil nutrient and pH level of the soil and was proven accurate.

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Determination of soil nutrients and pH level using image processing and artificial neural network

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