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

Articles in Press

Document Type : Research Article

Authors

• M. H. Nargesi ¹
• K. Kheiralipour ¹
• F. Valizadeh Kakhki ²
• Z. Moradi ³

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

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

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

https://doi.org/10.22067/jam.2025.93983.1394

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

Main Subjects

• Image Processing

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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