Effect of Preprocessing on Modelling Soil Images Captured Using Smartphone
Keywords:
Soil Images Modelling, Image Capturing Using Smartphone, Image Preprocessing
Abstract
Knowing soil characteristics is one important step in agricultural process. Soil characteristics such as NPK and pH values could differ the production quantity and quality of a farm. To know soil characteristics, various methods could be implemented including the use of tools such as Soil Test Kit (STK), and Rapid Soil Testing (RST), among others. For an extreme case, soil laboratory work is sometimes conducted. However, such a process is considered taking time and expensive to realize. Nowadays, the use of smartphones is getting common. Smartphones can capture images, in this case soil images, in no time. However, recognizing soil characteristics based on images needs more processes. Various artificial intelligence (AI) methods exist and could be used for the purpose, including artificial neural networks, convolutional neural networks, random forest, and gradient boosting, among others. This paper tries to experiment how the soil images captured using smartphone could be used to predict soil characteristics. Various image preprocessing methods are chosen to produce images which could be modelled using various AI methods. The results show that random forest performed the best compared to other methods with overall lowest mean squared error. Predicting pH values based on soil images produced better accuracies compared to NPK values. Image preprocessing does not influence largely on the prediction accuracies. For some cases, modelling of images without preprocessing even resulted in better accuracies.References
1. J. Harwood, R. Heifner, K. Coble, J, Perry, and A. Somwaru, Managing Risk in Farming: Concepts, Research, and Analysis, Agricultural Economic Report No. 774. , Market and Trade Economics Division and Resource Economic Division, Economic Research Service, U.S. Department of Agriculture, 1999.
2. B. Tedesse, Y. Tilahun, T. Bekele, and G. Mekonen, Assessment of Challenges of Crop Production and Marketing in Bench-Sheco, Kaffa, Sheka, and West-Omo Zone of Southwest Ethiopia. Heliyon 7, 2021.
3. MI. Abdi-Soojeede, Crop Production Challenges Faced by Farmers in Somalia: A Case Study of Afgoye District Farmers. Wavelet Agricultural Science 9, vol 9, no 7, pp. 1032-1046, 2018.
4. VKH. Manegar, Problems Faced by Farmers in Karnataka. International Journal of Research and Analytical Reviews, vol 2, issue 4, pp. 46-56, 2015.
5. Z. Rozaki, Food security challenges and opportunities in Indonesia post COVID-19. Advances in Food Security and Sustainability, vol 6, pp. 199-168, 2021.
6. M. H. Shahapur, and R. T. Pote, A Study on Problems Faced by Farmers of Yadgir District of Karnataka State. International Journal of Creative Research and Thought, vol 9, issue 5, pp. 838-843, 2021.
7. D. Kahan, Managing Risk in Farming. Food and Agriculture Organization of The United Nations, Rome, 2008.
8. S. Sundaramoorthy, A Study on Problems and Prospects of Farmers with Reference to Tirunelveli District. International Journal of Economics, vol 9, issue 2, pp. 22-25, 2021.
9. UTASS and Rose Regeneration Challenges Facing Farmers, A Report into Upland Farming and Farming Families in Teesdale, 2012
10. J. F. Montanez, Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation., Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 90-98, 2021.
11. H. Pallevada, S. P. Potu, T. V. K. Munnangi, B. C. Rayapudi, S. R. Gadde, and M. Chinta, Real-time Soil Nutrient detection and Analysis, 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, vol, pp. 1035-1038, 2021.
12. J. C. Puno, E. Sybongco, E. Dadios, I. Valenzuela, J. Cuello, Determination of Soil Nutrients and pH level using Image Processing and Artificial Neural Network. 9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-6, 2017.
13. R. G. Regalado, J. C. D. Cruz, Soil pH and Nutrient (Nitrogen, Phosphorus and Potassium) Analyzer using Colorimetry. IEEE Region 10 Conference (TENCON), pp. 2387-2391, 2016.
14. R. Sudha, S. Aarti, K. Nanthini, Determination of Soil Ph and Nutrient Using Image Processing, International Journal of Computer Trends and Technology (IJCTT) – Special Issue, pp. 58-61, 2017.
15. B. H. Tan, W. H. You, S. H. Tian, T. F. Xiao, M. C. Wang, B. T. Zheng, L. N. Luo. Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy. Sensors, 22, 8013, 2022.
16. N. R. Zani, A. H. Alasiry, A. Wijayanto. Design and Development of Soil Nutrients Level Detection System based on Soil Colour and pH for Crop Recommendations using Fuzzy Algorithms. The Indonesian Green Technology Journal, pp. 38-45, 2022.
17.
18. M. Amri, R. Sumiharto. Sistem Pengukuran Nitrogen, Fosfor, Kalium Dengan Local Binary Pattern Dan Analisis Regresi. Indonesian Journal of Electronics and Instrumentation Systems (IJEIS), Vol. 9, No. 2, pp. 107~118, 2019.
19. M. J. Aitkenhead, M. Coull, W. Towers, G. Hudson, H. I. J. Black. Prediction of soil characteristics and colour using data from the National Soils Inventory of Scotland. Geoderma 200–201, pp. 99–107, 2013.
20. M. Mouazen, R. Karoui, J. Deckers, J. De Baerdemaeker, H. Ramon, Potential of visible and near-infrared spectroscopy to derive colour groups utilising the Munsell soil colour chart. Biosystems Engineering, vol 97, Issue 2, pp. 131-143, 2007.
21. R. A. V. Rossel, B. Minasny, P. Roudier, A. B. McBratney, Colour space models for soil science. Geoderma 133, pp. 320-337, 2006.
22. S. S. Nodi, M. Paul, N. Robinson, L. Wang, S. U. Rehman, Determination of Munsell Soil Colour Using Smartphones. Sensors, 23, 3181, 2023.
23. H. Robbani, E. T, Trisnawati, R. Noviyanti, A. Rivaldi, F. P. Cahyani, F. Utaminingrum, Aplikasi Mobile Scotect: Aplikasi Deteksi Warna Tanah Dengan Teknologi Citra Digital Pada Android. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), Vol. 3, No. 1, pp. 19-26, 2016.
24. C. Shorten, T. M. Khoshgoftaar, A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60, 2019.
25. K. M. Koo, E. Y. Cha, Image recognition performance enhancements using image normalization. Human-centric Computing and Information Sciences 7, Article 33, 2017.
26. K. G. Dhal, A. Das, S. Ray, et al. Histogram Equalization Variants as Optimization Problems: A Review. Arch Computation Methods Eng 28, pp. 1471–1496, 2021.
27. M. Shah, Y. Xiao, N. Subbanna, S. Francis, Evaluating intensity normalization on MRIs of human brain with multiple sclerosis Mohak Shah a Yiming Xiao a Nagesh Subbanna a Simon Francis b c Douglas L. Arnold b c D. Louis Collins b Tal Arbel a Medical Image Analysis, Volume 15, Issue 2, April 2011, Pages 267-282 https://doi.org/10.1016/j.media.2010.12.003
28. L. Kalyani, K. B. Prakash, Soil Color as a Measurement for Estimation of Fertility using Deep Learning Techniques. International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 305-310, 2022.
29. R. C. Gonzalez and R. E. Woods., Digital Image Processing. Essex: Pearson Education, Fourth Edition, 2018.
30. P. Cheeseman and J. Stutz, Bayesian Classification (AutoClass): Theory and Results. Advances in Knowledge Discovery and Data Mining, pp. 153-180, 1996.
31. C. Fraley and A. E. Raftery, MCLUST: Software for Model-Based Cluster and Discriminant Analysis. Technical Report 342, Statistics Dept, University of Washington, 1998.
32. G. J. McLachlan, D. Peel, K. E. Basford, P. Adams, The EMMIX Software for the Fitting of Mixtures of Normal and t-Components. J. Stat. Software, vol. 4, 1999.
33. Munsell Color. Munsell Soil Color Book, SKU: MC-M50215B, MPN: M50215B, USA.
34. Y. Agusta and D. L. Dowe, MML Clustering of Continuous-Valued Data Using Gaussian and t Distributions. In Lecture Notes in Artificial Intelligence, vol. 2557, pp. 143-154, 2002.
35. Y. Agusta and D. L. Dowe, Unsupervised Learning of Gamma Mixture Models Using Minimum Message Length. 3rd IASTED International Conference on Artificial Intelligence and Applications, pp.457-462, 2003.
36. Y. Agusta and D. L. Dowe, Unsupervised Learning of Correlated Multivariate Gaussian Mixture Models Using MML. Lecture Notes in Artificial Intelligence AI2003, vol. 2903, pp. 477-489, 2003.
37. Y. Agusta, Implementing Minimum Message Length to the Modelling of Denpasar City Inflation Rate. 2023 Eighth International Conference on Informatics and Computing (ICIC), pp. 1-6, 2023.
38. C. S. Wallace and D. L. Dowe, Intrinsic classification by MML – the Snob program. In Proc. 7th Aust. Joint Conf. on AI, pp. 37-44, 1994.
39. CNNF. Chollet, Deep Learning with Python. New York: Manning Publication. 2018.
40. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press. 2016.
41. Random Forest S. Dharumarajan and R. Hedge, Digital mapping of soil texture classes using Random Forest classification algorithm. Soil Use and Management. British Society of Soil Science, Wiley, 2020.
42. Gradient Boosting Q. T. Bui, T. Y. Chou, T. V. Hoang, Y. M. Fang, C. Y. Mu, P. H. Huang, V. D. Pham, Q. H. Nguyen, D. T. N. Anh, V. M. Pham, M. E. Meadows, Gradient Boosting Machine and Object-Based CNN for Land Cover Classification. Remote Sens, 13 (14), 2709, 2021.
2. B. Tedesse, Y. Tilahun, T. Bekele, and G. Mekonen, Assessment of Challenges of Crop Production and Marketing in Bench-Sheco, Kaffa, Sheka, and West-Omo Zone of Southwest Ethiopia. Heliyon 7, 2021.
3. MI. Abdi-Soojeede, Crop Production Challenges Faced by Farmers in Somalia: A Case Study of Afgoye District Farmers. Wavelet Agricultural Science 9, vol 9, no 7, pp. 1032-1046, 2018.
4. VKH. Manegar, Problems Faced by Farmers in Karnataka. International Journal of Research and Analytical Reviews, vol 2, issue 4, pp. 46-56, 2015.
5. Z. Rozaki, Food security challenges and opportunities in Indonesia post COVID-19. Advances in Food Security and Sustainability, vol 6, pp. 199-168, 2021.
6. M. H. Shahapur, and R. T. Pote, A Study on Problems Faced by Farmers of Yadgir District of Karnataka State. International Journal of Creative Research and Thought, vol 9, issue 5, pp. 838-843, 2021.
7. D. Kahan, Managing Risk in Farming. Food and Agriculture Organization of The United Nations, Rome, 2008.
8. S. Sundaramoorthy, A Study on Problems and Prospects of Farmers with Reference to Tirunelveli District. International Journal of Economics, vol 9, issue 2, pp. 22-25, 2021.
9. UTASS and Rose Regeneration Challenges Facing Farmers, A Report into Upland Farming and Farming Families in Teesdale, 2012
10. J. F. Montanez, Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation., Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 1, pp. 90-98, 2021.
11. H. Pallevada, S. P. Potu, T. V. K. Munnangi, B. C. Rayapudi, S. R. Gadde, and M. Chinta, Real-time Soil Nutrient detection and Analysis, 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, vol, pp. 1035-1038, 2021.
12. J. C. Puno, E. Sybongco, E. Dadios, I. Valenzuela, J. Cuello, Determination of Soil Nutrients and pH level using Image Processing and Artificial Neural Network. 9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-6, 2017.
13. R. G. Regalado, J. C. D. Cruz, Soil pH and Nutrient (Nitrogen, Phosphorus and Potassium) Analyzer using Colorimetry. IEEE Region 10 Conference (TENCON), pp. 2387-2391, 2016.
14. R. Sudha, S. Aarti, K. Nanthini, Determination of Soil Ph and Nutrient Using Image Processing, International Journal of Computer Trends and Technology (IJCTT) – Special Issue, pp. 58-61, 2017.
15. B. H. Tan, W. H. You, S. H. Tian, T. F. Xiao, M. C. Wang, B. T. Zheng, L. N. Luo. Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy. Sensors, 22, 8013, 2022.
16. N. R. Zani, A. H. Alasiry, A. Wijayanto. Design and Development of Soil Nutrients Level Detection System based on Soil Colour and pH for Crop Recommendations using Fuzzy Algorithms. The Indonesian Green Technology Journal, pp. 38-45, 2022.
17.
18. M. Amri, R. Sumiharto. Sistem Pengukuran Nitrogen, Fosfor, Kalium Dengan Local Binary Pattern Dan Analisis Regresi. Indonesian Journal of Electronics and Instrumentation Systems (IJEIS), Vol. 9, No. 2, pp. 107~118, 2019.
19. M. J. Aitkenhead, M. Coull, W. Towers, G. Hudson, H. I. J. Black. Prediction of soil characteristics and colour using data from the National Soils Inventory of Scotland. Geoderma 200–201, pp. 99–107, 2013.
20. M. Mouazen, R. Karoui, J. Deckers, J. De Baerdemaeker, H. Ramon, Potential of visible and near-infrared spectroscopy to derive colour groups utilising the Munsell soil colour chart. Biosystems Engineering, vol 97, Issue 2, pp. 131-143, 2007.
21. R. A. V. Rossel, B. Minasny, P. Roudier, A. B. McBratney, Colour space models for soil science. Geoderma 133, pp. 320-337, 2006.
22. S. S. Nodi, M. Paul, N. Robinson, L. Wang, S. U. Rehman, Determination of Munsell Soil Colour Using Smartphones. Sensors, 23, 3181, 2023.
23. H. Robbani, E. T, Trisnawati, R. Noviyanti, A. Rivaldi, F. P. Cahyani, F. Utaminingrum, Aplikasi Mobile Scotect: Aplikasi Deteksi Warna Tanah Dengan Teknologi Citra Digital Pada Android. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), Vol. 3, No. 1, pp. 19-26, 2016.
24. C. Shorten, T. M. Khoshgoftaar, A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60, 2019.
25. K. M. Koo, E. Y. Cha, Image recognition performance enhancements using image normalization. Human-centric Computing and Information Sciences 7, Article 33, 2017.
26. K. G. Dhal, A. Das, S. Ray, et al. Histogram Equalization Variants as Optimization Problems: A Review. Arch Computation Methods Eng 28, pp. 1471–1496, 2021.
27. M. Shah, Y. Xiao, N. Subbanna, S. Francis, Evaluating intensity normalization on MRIs of human brain with multiple sclerosis Mohak Shah a Yiming Xiao a Nagesh Subbanna a Simon Francis b c Douglas L. Arnold b c D. Louis Collins b Tal Arbel a Medical Image Analysis, Volume 15, Issue 2, April 2011, Pages 267-282 https://doi.org/10.1016/j.media.2010.12.003
28. L. Kalyani, K. B. Prakash, Soil Color as a Measurement for Estimation of Fertility using Deep Learning Techniques. International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 305-310, 2022.
29. R. C. Gonzalez and R. E. Woods., Digital Image Processing. Essex: Pearson Education, Fourth Edition, 2018.
30. P. Cheeseman and J. Stutz, Bayesian Classification (AutoClass): Theory and Results. Advances in Knowledge Discovery and Data Mining, pp. 153-180, 1996.
31. C. Fraley and A. E. Raftery, MCLUST: Software for Model-Based Cluster and Discriminant Analysis. Technical Report 342, Statistics Dept, University of Washington, 1998.
32. G. J. McLachlan, D. Peel, K. E. Basford, P. Adams, The EMMIX Software for the Fitting of Mixtures of Normal and t-Components. J. Stat. Software, vol. 4, 1999.
33. Munsell Color. Munsell Soil Color Book, SKU: MC-M50215B, MPN: M50215B, USA.
34. Y. Agusta and D. L. Dowe, MML Clustering of Continuous-Valued Data Using Gaussian and t Distributions. In Lecture Notes in Artificial Intelligence, vol. 2557, pp. 143-154, 2002.
35. Y. Agusta and D. L. Dowe, Unsupervised Learning of Gamma Mixture Models Using Minimum Message Length. 3rd IASTED International Conference on Artificial Intelligence and Applications, pp.457-462, 2003.
36. Y. Agusta and D. L. Dowe, Unsupervised Learning of Correlated Multivariate Gaussian Mixture Models Using MML. Lecture Notes in Artificial Intelligence AI2003, vol. 2903, pp. 477-489, 2003.
37. Y. Agusta, Implementing Minimum Message Length to the Modelling of Denpasar City Inflation Rate. 2023 Eighth International Conference on Informatics and Computing (ICIC), pp. 1-6, 2023.
38. C. S. Wallace and D. L. Dowe, Intrinsic classification by MML – the Snob program. In Proc. 7th Aust. Joint Conf. on AI, pp. 37-44, 1994.
39. CNNF. Chollet, Deep Learning with Python. New York: Manning Publication. 2018.
40. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press. 2016.
41. Random Forest S. Dharumarajan and R. Hedge, Digital mapping of soil texture classes using Random Forest classification algorithm. Soil Use and Management. British Society of Soil Science, Wiley, 2020.
42. Gradient Boosting Q. T. Bui, T. Y. Chou, T. V. Hoang, Y. M. Fang, C. Y. Mu, P. H. Huang, V. D. Pham, Q. H. Nguyen, D. T. N. Anh, V. M. Pham, M. E. Meadows, Gradient Boosting Machine and Object-Based CNN for Land Cover Classification. Remote Sens, 13 (14), 2709, 2021.
Published
2025-10-30
How to Cite
Agusta, Y., & Julyantari, N. K. S. (2025). Effect of Preprocessing on Modelling Soil Images Captured Using Smartphone. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2198
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Section
Research Articles
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