برآورد میزان اکسید پتاسیم در کود پتاس با استفاده از روش‌های پردازش تصویر فراطیفی و یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک بیوسیستم، دانشگاه ایلام، ایلام، ایران

2 گروه مهندسی مکانیک بیوسیستم، دانشگاه ایلام، ایلام، ایران.

چکیده

برای افزایش بهره‌وری کشاورزی، مدیریت حاصلخیزی خاک و تأمین عناصر مغذی از جمله پتاسیم بسیار مهم است. پتاسیم نقش حیاتی در رشد گیاه و فرآیندهای فیزیولوژیکی دارد؛ اما مصرف نامتعادل آن می‌تواند باعث کاهش کیفیت خاک یا اتلاف شود. روش‌های متداول اندازه‌گیری میزان اکسید پتاسیم پرهزینه و زمان‌بر هستند؛ بنابراین نیاز به روش‌های سریع، دقیق و مقرون ‌به ‌صرفه احساس می‌شود. هدف از این تحقیق، تشخیص میزان اکسید پتاسیم در کود پتاس بر اساس تصاویر فراطیفی است. پس از اکتساب تصاویر فراطیفی و پردازش آن‏ها، با استفاده از روش شبکه‏های عصبی مصنوعی و با دو رویکرد با و بدون انتخاب ویژگی طبقه‎بندی شدند. در رویکرد اول، تمامی ویژگی‌های استخراج‌شده از کانال‌های مؤثر تصاویر فراطیفی مستقیماً به عنوان ورودی مدل‌های طبقه‌بند به کار گرفته شدند؛ اما در رویکرد دوم، تنها ویژگی‌های منتخب وارد فرآیند طبقه‌بندی شدند. نتایج نشان داد که مدل شبکه عصبی مصنوعی بر اساس تمام ویژگی‏‎های استخراجی (9/92 درصد) بالاتر از ویژگی‌های منتخب (3/91 درصد) بود. روش پیشنهادی در تحقیق حاضر می‌تواند در آینده برای تشخیص سایر عناصر شیمیایی در کود پتاس مورد استفاده قرار گیرد. این روش، ابزاری کارآمد برای ارزیابی سریع و غیرمخرب ترکیب کودها ارائه می‌دهد. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of Potassium Oxide Content in Potash Fertilizer Using Hyperspectral Image Processing and Machine Learning Methods

نویسندگان [English]

  • Mohammad Hossein Nargesi 1
  • kamran kheiralipour 2
1 Department of Biosystems Mechanical Engineering, University of Ilam, Ilam, Iran
2 Department of Biosystems Mechanical Engineering, University of Ilam, Ilam, Iran.
چکیده [English]

 
 
To enhance agricultural productivity, managing soil fertility and ensuring the availability of essential nutrients such as potassium is of great importance. Potassium plays a vital role in plant growth and physiological processes. However, its unbalanced application can lead to soil degradation or nutrient loss. Conventional methods for measuring potassium oxide content are often expensive and time-consuming, highlighting the need for rapid, accurate, and cost-effective alternatives. This study aims to detect the amount of potassium oxide in potash fertilizer based on hyperspectral imaging. After acquiring and processing the hyperspectral images, artificial neural networks were employed for classification using two approaches: with and without feature selection. In the first approach, all extracted features from the effective hyperspectral bands were directly used as inputs to the classification models. In the second approach, only selected features were used for classification. The results showed that the artificial neural network model using all extracted features achieved a higher accuracy (92.9%) compared to the model based on selected features (91.3%). The proposed method in this study can potentially be used in the future to detect other chemical elements in potash fertilizer. This approach offers an efficient, rapid, and non-destructive tool for assessing fertilizer composition.

کلیدواژه‌ها [English]

  • Chemical fertilizer
  • hyperspectral imaging
  • image processing
  • machine learning
  • artificial neural network

EXTENDED ABSTRACT

 

Introduction

Soil fertility is a key factor in crop production and potassium is one of the most essential macronutrients for plant growth and development. It plays a crucial role in enzyme activity, photosynthesis, and plant resistance to diseases. Potassium fertilizers are classified into chlorine-containing and chlorine-free  types, with the latter being more beneficial for chlorine-sensitive crops. Potassium sulfate is produced through various methods, but laboratory-based determination is costly and time-consuming. Therefore, developing precise, fast, and cost-effective tools for measuring K₂SO₄ is essential. This study focuses on implementing an accurate, rapid, and economical method for estimating potassium sulfate content.

Materials and Methods

This study was conducted in the Image Processing Laboratory, Ilam University, Ilam, Iran. To determine the potassium oxide level, seven different concentrations were analyzed. The measurement of potassium oxide in the laboratory was performed using a flame photometer. The required images were captured through hyperspectral imaging using a line-scanning method. Six hyperspectral images were taken for each sample, resulting in 18 images per concentration and a total of 126 images. Image analysis and processing were carried out using MATLAB software, including wavelength selection, feature extraction, and selection of efficient features. Finally, the selected features were classified using artificial neural network method.

Results and Discussion

The results showed that the artificial neural network classifier achieved a classification accuracy of 91.3% using selected features from hyperspectral images of potash fertilizer and 92.9% using all extracted features. The proposed method offers high accuracy and reliability, along with advantages over laboratory-based techniques, such as being non-destructive, fast, simple, and cost-effective.

Conclusions

Hyperspectral imaging combined with artificial neural networks mehtod offers high accuracy and reliability while being non-destructive, fast, simple, and cost-effective compared to laboratory-based techniques in estimation of the potassium oxide level in potash fertilizers. The proposed approach can be utilized in the future for detecting other chemical elements in potash fertilizer. Additionally, it has the potential to be expanded for assessing other chemical fertilizers.

Author Contributions

M.H. Nargesi: Conceptualization, investigation, software, formal analysis, data curation, writing-original draft preparation, K. Kheiralipour: Methodology, software, resources, writing-review and editing. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data available on request from the authors.

Acknowledgements

The authors would like to thank Ilam University and Eyvan Chemical Industries Complex  Company  to support the present study.

Ethical considerations

The subject of plagiarism has been considered by the authors and this article is without problem.

Conflict of interest

The author declares no conflict of interest.

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تحقیقات آب و خاک ایران
دوره 56، شماره 6
شهریور 1404
صفحه 1539-1554

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