A Novel Strategy for Classifying Potassium Fertilizer Based on pH Level Using Visible-Near-Infrared Hyperspectral Imaging and Machine Learning

ماشین های کشاورزی

Articles in Press, Accepted Manuscript, Available Online from 27 September 2025   

Document Type: Research Article

DOI: 10.22067/jam.2025.93983.1394

Authors

M. H. Nargesi^{* 1}; K. Kheiralipour¹; F. Valizadeh Kakhki²; Z. Moradi³

¹Biosystems Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran

²Ph.D. in Environmental Science, Specialization in Environmental Pollutions, Ilam, Iran

³Department of Chemistry, Faculty of Science, Ilam University, Ilam, Iran

Abstract

Introduction
Paying attention to the technical aspects of production plays a crucial role in increasing yield and ensuring sustainable agriculture. Organic fertilizers, such as poultry manure, contribute to plant growth by providing essential nutrients and improving soil quality. However, they alone cannot fully meet the nutritional needs of plants. The combination of organic and chemical fertilizers is an effective approach to enhancing soil fertility and boosting crop performance, ultimately leading to sustainable agricultural development. Integrated nutrient management also helps reduce the use of chemical fertilizers while minimizing their harmful effects on the environment. Potassium is an essential element in plant nutrition, playing a key role in processes such as photosynthesis, growth, chlorophyll production, and transpiration regulation. Additionally, under stress conditions, potassium enhances water uptake and regulates osmotic pressure, helping to maintain plant health. Potassium fertilizers are classified into two categories: chloride-based and chloride-free. Potassium sulfate, due to its lack of chloride, is a suitable option for chloride-sensitive crops such as tea, potatoes, and sugar beets. Meanwhile, hyperspectral imaging has emerged as an innovative technique with broad applications in detecting chemical parameters, assessing quality, and analyzing the purity of agricultural and food products. This study utilizes hyperspectral image processing technology to determine the pH level of potassium sulfate.
Materials and Methods
The present study was conducted in the Image Processing Laboratory at the Ilam University, Iran. To determine the pH level of potassium sulfate, four different levels of 2.5, 2.6, 2.8, and 2.9 were considered. The pH measurement was performed in the laboratory using a flame photometer. The required images were obtained through hyperspectral imaging using the line-scan method. For each pH level, three samples were obtained and six hyperspectral images were captured for each sample, resulting in 18 images per pH level and a total of 72 hyperspectral images for each pH level. MATLAB software was used for the analysis and processing of these images. The image processing stage included wavelength selection, feature extraction, and feature selection. Finally, the selected features were classified using an artificial neural network.
Results and Discussion
Principal Component Analysis performed on the hyperspectral image channels of potassium sulfate revealed significant variations in the principal component values across different pH levels. This finding indicates that pH conditions exert a considerable influence on the spectral response of the samples. Based on the prominent peaks obtained from the analysis, the most relevant channels were identified, and their corresponding wavelengths were determined as the optimal spectral bands. The selected channels for the four pH levels were 65, 327, 334, 482, 510, 607, and 644, with their corresponding effective wavelengths being 453.32, 669.95, 675.74, 798.11, 821.26, 901.47, and 932.06 nm, respectively. To extract discriminative spectral information, six features were computed from each of the selected wavelengths. Consequently, a total of 42 features were obtained, which were subsequently employed in the classification process of different pH levels. The confusion matrices of the classification model based on the artificial neural network were obtained to evaluate the model's accuracy. The classification accuracy for detecting the pH level of potassium sulfate was 98.6% with effective features and 97.2% without them.
Conclusion
The results of this study demonstrated the high potential of hyperspectral imaging technology combined with the artificial neural network classification method, using strategies with and without effective feature selection, in detecting the pH level of potassium sulfate. The proposed method offers several advantages over laboratory-based approaches, such as being non-destructive, having high speed, and being cost-effective. It is suggested to explore other methods for classifying hyperspectral images for determining the pH level of potassium sulfate. The proposed method in this study could also be applied in the future to identify various chemical elements in potassium sulfate.

Keywords

Artificial neural network; Chemical fertilizer; Hyperspectral imaging; Image processing; Machine learning

References

1. Ahmad, Z., Anjum, S., Waraich, E. A., Ayub, M. A., & Ahmad, T. (2018). Growth, physiology, and biochemical activities of plant responses with foliar potassium application under drought stress–a review, Journal of Plant Nutrition 41, 1734-1743. https://doi.org/10.1080/01904167.2018.1459688
2. Ahmed, W., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2025). An overview of recent advancements in hyperspectral imaging in the egg and hatchery industry. Computers and Electronics in Agriculture, 230, 109847. https://doi.org/10.1016/j.compag.2024.109847
3. Allahvirdizadeh, N., & Deljou, M. N. (2014). Effect of humic acid on morph-physiological traits, nutrients uptake and postharvest vase life of pot marigold cut flower (Calendula officinalis Crysantha) in hydroponic system. Journal of Science Technology of Greenhouse Culture, 5(18), 133-143. (in Persian with English abstract). https://dorl.net/dor/20.1001.1.20089082.1393.5.2.12.6
4. Al-Taai, S. H. H. (2021). The effect of fertilizer uses on environmental pollution: A review. Review of International Geographical Education Online, 11(5), 3620-3529.
5. Arif Chaudhry, M. M., Bane, M., McAllister, T., Paliwal, J., & Narváez-Bravo, C. (2025). Identification and Classification of Multi-Species Biofilms on Polymeric Surfaces Using Hyperspectral Imaging. Journal of Food Safety, 45, e70008. https://doi.org/10.1111/jfs.70008
6. Aviara, N. A., Liberty, J. T., Olatunbosun, O. S., Shoyombo, H. A., & Oyeniyi, S. K. (2022). Potential application of hyperspectral imaging in food grain quality inspection, evaluation and control during bulk storage. Journal of Agriculture and Food Research, 8, 100288. https://doi.org/10.1016/j.jafr.2022.100288
7. Bagherpour, H., Fatehi, F., Shojaeian, A., & Bagherpour, R. (2025). Hyperparameter Optimization of ANN, SVM, and KNN Models for Classification of Hazelnuts Images Based on Shell Cracks and Feature Selection Method. Journal of Agricultural Machinery, 15(1), 129-144. https://doi.org/10.22067/jam.2024.87830.1244
8. Chen, J. H. (2006). The combined use of chemical and organic fertilizers and/or biofertilizer for crop growth and soil fertility. Paper presented at the international workshop on sustained management of the soil-rhizosphere system for efficient crop production and fertilizer use.
9. Ding, J., Zhang, R., Ahmed, S., Liu, Y., & Qin, W. (2019). Effect of sonication duration in the performance of polyvinyl alcohol/chitosan bilayer films and their effect on strawberry preservation. Molecules, 24(7), 1408. https://doi.org/10.3390/molecules24071408
10. Fernandez, L. C., Allende-Prieto, J., & Peon, E. (2019). Preliminary Assessment of Visible, Near-Infrared, and Short-Wavelength–Infrared Spectroscopy with a Portable Instrument for the Detection of Staphylococcus aureus Biofilms on Surfaces. Journal of Food Protection, 82(8), 1314-1319. https://doi.org/10.4315/0362-028X.JFP-18-567
11. Gowen, A. A., O’Donnell, C. P., Cullen, P. J., Downey, G., & Frias, J. M. (2007). Hyperspectral imaging–an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology, 18, 590-598. https://doi.org/10.1016/j.tifs.2007.06.001
12. Griffe, P., Metha, S., & Shankar, D. (2003). Organic production of medicinal, aromatic and dye yielding plants (MADPs): forward, preface and introduction. Food Agriculture Organization, 2, 52-63.
13. Hasan, M. M., Chaudhry, M. M. A., Erkinbaev, C., Paliwal, J., Suman, S. P., & Rodas- Gonzalez, A. (2022). Application of Vis-NIR and SWIR Spectroscopy for the Segregation of Bison Muscles Based on Their Color Stability. Meat Science, 188, 108774. https://doi.org/10.1016/j.meatsci.2022.108774
14. Hosainpour, A., Kheiralipour, K., Nadimi, M., & Paliwal, J. (2022). Quality assessment of dried white mulberry (Morus alba) using machine vision. Horticulturae, 8(11), 1011. https://doi.org/10.3390/horticulturae8111011
15. Ismail, A., Yim, D.-G., Kim, G., & Jo, C. (2023). Hyperspectral imaging coupled with multivariate analyses for efficient prediction of chemical, biological and physical properties of seafood products. Food Engineering Reviews, 15, 41-55. https://doi.org/10.1007/s12393-022-09327-x
16. Khan, M. H., Saleem, Z., Ahmad, M., Sohaib, A., Ayaz, H., & Mazzara, M. (2020). Hyperspectral imaging for color adulteration detection in red chili. Applied Sciences, 10, 5955. https://doi.org/10.3390/app10175955
17. Khazaee, Y., Kheiralipour, K., Hosainpour, A., Javadikia, H., & Paliwal, J. (2022). Development of a novel image analysis and classification algorithms to separate tubers from clods and stones. Potato Research, 65, 1-22. https://doi.org/10.1007/s11540-021-09528-7
18. Kheiralipour, K. (2024). The future of imaging technology. Nova Science Publishers, Hauppauge, New York, USA. ISBN 979-8-89530-078-7.
19. Kheiralipour, K. (2022). Sustainable production: Definitions, aspects, and elements. (1^st), Nova Science Publishers, Hauppauge, New York, USA. ISBN 979-8-88697-208-5.
20. Kheiralipour, K. (2012). Implementation and construction of a system for detecting fungal infection in pistachio kernel based on thermal imaging (TI) and image processing technology. Ph.D. Dissertation, University of Tehran, Karaj, Iran.
21. Kheiralipour, K., Ahmadi, H., Rajabipour, A., & Rafiee, S. (2018). Thermal imaging, principles, methods and applications. Ilam University Publication. Ilam, IR Iran.
22. Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., & Javan-Nikkhah. M. (2015a). Classifying healthy and fungal infected-pistachio kernel by thermal imaging technology. International Journal of Food Properties, 18(1), 93-99. https://doi.org/10.1080/10942912.2012.717155
23. Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., & Jayas, D. S. (2014). Detection of healthy and fungal-infected pistachios based on hyperspectral image processing. 8^th Iranian national congress of agricultural machinery engineering (biosystems) and mechanization, 29-31 January, Mashahd, Iran.
24. Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D. S., & Siliveru, K. (2015b). Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique. Quality Assurance and Safety of Crops & Foods, 8(1), 129-135. https://doi.org/10.3920/QAS2015.0606
25. Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S., Javan-Nikkhah, M., Jayas, D. S., Siliveru, K., & Malihipour, A. (2021). Processing the hyperspectral images for detecting infection of pistachio kernel by R5 and KK11 isolates of Aspergillus flavus fungus. Iranian Journal of Biosystems Engineering, 52(1), 13-25. https://doi.org/10.22059/ijbse.2020.299712.665293
26. Kheiralipour, K., Chelladurai, V., & Jayas, D. S. (2023a). Imaging Systems and Image Processing Techniques. In Image Processing: Advances in Applications and Research. Edited by Jayas, D.S. New York, USA: Nova Science Publishers.
27. Kheiralipour, K., & Marzbani, F. (2016). Pomegranate quality sorting by image processing and artificial neural network. 10^th Iranian national congress on agricultural machinery (biosystems) and mechanization, 29-31 August, Mashhad, Iran.
28. Kheiralipour, K., Sajadipour, F., & Nargesi, M. H. (2025). Applications of spectral imaging in Biosystems engineering in Iran, A review. Recent Progress in Science, Accepted manuscript. https://doi.org/10.70462/rps.2025.2.007
29. Kheiralipour, K., & Jayas, D. S. (2024). Current and future applications of hyperspectral imaging in agriculture, nature and food. Trends in Technical & Scientific Research, 7(2), 1-9.
30. Kheiralipour, K., & Jayas, D. S. (2023). Applications of near infrared hyperspectral imaging in agriculture, natural resources, and food in Iran. 15^th national and 1st international congress of mechanics of biosystems engineering and agricultural mechanization, Karaj, Iran.
31. Li, C., He, M., Cai, Z., Qi, H., Zhang, J., & Zhang, C. (2023). Hyperspectral imaging with machine learning approaches for assessing soluble solids content of tribute citru. Foods, 12, 247. https://doi.org/10.3390/foods12020247.
32. Lin, Y., Ma, J., Wang, Q., & Sun, D.-W. (2023). Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Critical Reviews in Food Science and Nutrition, 63, 1649-1669. https://doi.org/10.1080/10408398.2022.2131725
33. Min, D., Zhao, J., Bodner, G., Ali, M., Li, F., Zhang, X., & Rewald, B., (2023). Early decay detection in fruit by hyperspectral imaging–Principles and application potential. Food Control, 152, 109830. https://doi.org/10.1016/j.foodcont.2023.109830
34. Nargesi, M. H., Amiriparian, j., Bagherpour, H., & Kheiralipour, K. (2024b). Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method. Results in Chemistry, 9, 101644. https://doi.org/10.1016/j.rechem.2024.101644
35. Nargesi, M. H., & Kheiralipour, K. (2024). Visible feature engineering to detect adulteration in black and red peppers. Scientific Reports, 14, 25417. https://doi.org/10.1038/s41598-024-76617-1
36. Nargesi, M. H., Kheiralipour, K., & Jayas, D. S. (2024c). Classification of different wheat flour types using hyperspectral imaging and machine learning techniques. Infrared Physics & Technology, 142, 105520. https://doi.org/10.1016/j.infrared.2024.105520
37. Nargesi, M. H. (2024). Detection of fraud in black pepper, red pepper, and cinnamon powder using hyperspectral imaging and artificial neural network. Ph.D. Dissertation, University of Bu-Ali Sina. Pages 11-130.
38. Nargesi, M. H., Heidarbeigi, K., Moradi, Z., & Abdolahi, S. (2024a). Detection of chlorine in potassium chloride and potassium sulfate using chemical imaging and artificial neural network. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 326, Pages, 125253. https://doi.org/10.1016/j.saa.2024.125253
39. Naser, A. A. (2017). Study of response Trigonella foenum-graecum to spraying with high potassium (Miller) and high phosphor (Agroleaf). Tikrit Journal of Pure Science, 22(9), 6-10. https://doi.org/10.25130/tjps.v22i9.866
40. Nashmil, F., Mardani, A., Hosainpour, A., & Golanbari, B. (2025). Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks. Journal of Agricultural Machinery, 15(2), 263-274. (in Persian with English abstract). https://doi.org/10.22067/jam.2024.90273.1295
41. Noshirvani, N., & Mohebi, A. (2023). Investigation of chemical and microbial properties of date syrup during production stages (from raw material to final product). Iranian Journal of Food Science and Industry, 20(139). (in Persian). https://doi.org/10.22034/FSCT.20.139.109
42. Pahalvi, H. N., Rafiya, L., Rashid, S., Nisar, B., & Kamili, A. N. (2021). Chemical Fertilizers and Their Impact on Soil Health. In: Dar, G.H., Bhat, R.A., Mehmood, M.A., Hakeem, K.R. (eds) Microbiota and Biofertilizers, Vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-61010-4_1
43. Pandey, V., & Patra, D. (2015). Crop productivity, aroma profile and antioxidant activity in Pelargonium graveolens L’Hér. under integrated supply of various organic and chemical fertilizers. Industrial Crops Products, 67, 257-263. https://doi.org/10.1016/j.indcrop.2015.01.042
44. Park, J.-J., Cho, J.-S., Lee, G., Yun, D.-Y., Park, S.-K., Park, K.-J., & Lim, J.-H. (2023). Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging. Foods, 12, 3471. https://doi.org/10.3390/foods12183471
45. Patil, A., & Lad, K. (2021). Chili Plant Leaf Disease Detection Using SVM and KNN Classification. Springer Nature Singapore Pte Ltd. 2021 S. Rathore et al. (eds.), Rising Threats in Expert Applications and Solutions, Advances in Intelligent Systems and Computing 1187. https://doi.org/10.1007/978-981-15-6014-9_26
46. Perramon, B., Bosch-Serra, A., Domingo, F., & Boixadera, J. (2016). Organic and mineral fertilization management improvements to a double-annual cropping system under humid Mediterranean conditions. European Journal of Agronomy, 76, 28-40. https://doi.org/10.1016/j.eja.2016.01.014
47. Qi, W., Wang, C., & Guo, X. (2017). Study on plant behavior perception based on computer vision: A review. Jiangsu Journal of Agricultural Sciences, 45, 20-26.
48. Saha, D., & Manickavasagan, A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28-44. https://doi.org/10.1016/j.crfs.2021.01.002
49. Salam, S., Kheiralipour, K., & Jian, F. (2022). Detection of unripe kernels and foreign materials in chickpea mixtures using image processing. Agriculture, 12(7), 995. https://doi.org/10.3390/agriculture12070995
50. Sakinejad, T. (2003). Study of effect of water deficit on the trend of uptake of N, P, K and Na at different growth stages considering the morphological and physiological traits of maize in Ahvaz climate. Ph.D. Dissertation on Crop Physiology, Science and Research Branch, Ahvaz, Iran.
51. Scharf, P. C., Kitchen, N. R., Sudduth, K. A., Davis, J. G., Hubbard, V. C., & Lory, J. A. (2005). Field‐scale variability in optimal nitrogen fertilizer rate for corn. Agronomy Journal, 97(2), 452-461. https://doi.org/10.2134/agronj2005.0452
52. Shabbir Dar, J., Akhtar Cheema, M., Ishaq Asif Rehmani, M., Khuhro, S., Rajput, S., Latif Virk, A., Hussain, S., Amjad Bashir, M., Suliman, M., Al-Zuaibr, M., Javed Ansari, M., & Hessini, K. (2021). Potassium fertilization improves growth, yield and seed quality of sunflower (Helianthus annuus) under drought stress at different growth stages. PLoS ONE, 16(9), e0256075. https://doi.org/10.1371/journal. pone.0256075
53. Sharma, S., Sirisomboon, P., Sumesh, K., Terdwongworakul, A., Phetpan, K., Kshetri, T. B., & Sangwanangkul, P. (2023). Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp. Postharvest Biology and Technology, 200, 112334. https://doi.org/10.1016/j.postharvbio.2023.112334
54. Siadat, S. A., & Moradi-Telavat, M. R. (2018). Practical Aspects of Organic Farming. Tehran: Agricultural Education and Extension Press, 500 p. (in Persian).
55. Soni, A., Dixit, Y., Reis, M. M., & Brightwell, G. (2022). Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants. Comprehensive Reviews in Food Science and Food Safety, 21, 3717-3745. https://doi.org/10.1111/1541-4337.12983
56. Sun, Y., Tong. Ch., He, Sh., Wang, K., & Chen, L. (2018). Identification of Nitrogen, Phosphorus, and Potassium Deficiencies Based on Temporal Dynamics of Leaf Morphology and Color. Sustainability, 10, 762. https://doi.org/10.3390/su10030762
57. Vadivambal, R., & Jayas, D. S. (2016) Bio-Imaging: Principles, Techniques, and Applications. CRC Press, Taylor and Francis Group, New York, NY, US.
58. Wu, X. Y., Zhu, S. P., Huang, H., & Xu, D. (2017). Quantitative identification of adulterated sichuan pepper powder by near-infrared spectroscopy coupled with chemometrics. Journal of Food Qualit, https://doi.org/10.1155/2017/5019816
59. Xue, X., Tian, H., Zhao, K., Yu, Y., Zhuo, C., Xiao, Z., & Wan, D. (2025). Non-Destructive Detection of pH Value During Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging Technology. Agronomy, 15, 285. https://doi.org/10.3390/agronomy15020285
60. Yang, F., Sun, J., Cheng, J., Fu, L., Wang, S., & Xu, M. (2023). Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning. Journal of Food Process Engineering, 46, https://doi.org/10.1111/jfpe.14304
61. Zhao, J., Ni, T., Li, J., Lu, Q., Fang, Z., Huang, Q., Zhang, R., Li, R., Shen, B., & Shen, Q. (2016). Effects of organic–inorganic compound fertilizer with reduced chemical fertilizer application on crop yields, soil biological activity and bacterial community structure in a rice–wheat cropping system. Applied Soil Ecology, 99, 1-12. https://doi.org/10.1016/j.apsoil.2015.11.006