SoilMATe: Soil Macronutrients and pH Level Assessment for Rice Plant through Digital Image Processing Using Artificial Neural Network

Authors

  • Nilo M. Arago Technological University of the Philippines, Manila, Philippines.
  • John William F. Orillo Technological University of the Philippines, Manila, Philippines. De La Salle University, Manila 1004, Philippines.
  • Jenskie Jerlin Haban Technological University of the Philippines, Manila, Philippines.
  • Jomer Juan Technological University of the Philippines, Manila, Philippines.
  • John Carlo Puno Technological University of the Philippines, Manila, Philippines.
  • Jay Fel Quijano Technological University of the Philippines, Manila, Philippines.
  • Gian Matthew Tuazon Technological University of the Philippines, Manila, Philippines.

Keywords:

Soil, Digital Image Processing, Artificial Neural Network,

Abstract

In this study, digital image processing technique was used to efficiently identify the Macronutrients and pH level of Soil in the farmland of Philippines: (1) Nitrogen, (2) Phosphorus, (3) Potassium and (4) pH. The composition of the system is made of four sections namely, image acquisition, image processing, training system, and result. The Artificial neural network was applied in this study for its features that make it well suited in offering fast and accurate performance for the image processing. The system will base on 448 captured image data, 70% for training, 15% for testing and 15% for validation. Based on the result, the program will generate a report in printed form. Overall, this study identifies the soil macronutrient and pH level of the soil and gives fertilizer recommendation for inbred rice plant and was proven 98.33% accurate.

References

Practical Guidelines in Predicting Fertility Status of Lowland Rice Soil.

Nutritional Recommendation for Rice by IRRI (2008).

Soil and Soil Nutrients. Retrieved by http://www.greenlandgarden.com/.

Bureau of Soils and Water Management. (2013). Soil Analysis and Fertilizer Usage. pp. 1-2.

Image Processing. (2012). Retrieved from: Engineersgarage.com.

P. Padmasree, Maheswari. R. (June 2013). A Novel Technique for Image Compression in Hand Writted Recognition using Back Propagation in Neural Network. International Journal of Computer Science and Engineering Technology (IJCSET).

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Published

2017-06-01

How to Cite

M. Arago, N., F. Orillo, J. W., Jerlin Haban, J., Juan, J., Puno, J. C., Quijano, J. F., & Tuazon, G. M. (2017). SoilMATe: Soil Macronutrients and pH Level Assessment for Rice Plant through Digital Image Processing Using Artificial Neural Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-5), 145–149. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2415

Issue

Vol. 9 No. 2-5: Circuit and System Advancement in Modern World IV

Section

Articles

License

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