راهبرد نوین طبقه‌بندی کود پتاسیم بر مبنای میزان pH با استفاده از تصویربرداری فراطیفی مرئی-فروسرخ نزدیک و یادگیری ماشینی

مقالات آماده انتشار

نوع مقاله : مقاله پژوهشی

نویسندگان

محمدحسین نرگسی ¹

کامران خیرعلی پور ¹

فاطمه ولی زاده کاخکی ²

زهرا مرادی ³

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

² دکتری محیط‌زیست گرایش آلودگی‌های محیطی، ایلام، ایران

³ گروه شیمی، دانشکده علوم پایه، دانشگاه ایلام، ایلام، ایران

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

چکیده

در حوزه کشاورزی، علاوه بر کودهای آلی، انواع مختلفی از کودهای شیمیایی به‌ منظور افزایش حاصلخیزی خاک و ارتقای عملکرد گیاهان مورد استفاده قرار می‌گیرند. پتاسیم به‌عنوان یکی از عناصر ضروری برای رشد گیاه، نقش کلیدی در فرآیندهایی نظیر فتوسنتز، سنتز کلروفیل و افزایش مقاومت گیاه در برابر تنش‌های محیطی ایفا می‌کند. یکی از شاخص‌های مهم در بررسی کیفیت کودهای پتاس، میزان اسیدیته آن‌هاست که تأثیر مستقیمی بر قابلیت جذب عناصر غذایی و اثربخشی کود دارد. در این پژوهش، با هدف توسعه روشی غیرمخرب و دقیق برای ارزیابی خصوصیات شیمیایی کود پتاسیم، از تصویربرداری فراطیفی در بازه طول موجی 400 تا 950 نانومتر استفاده شد. تصاویر چهار نمونه کود پتاسیم با سطوح متفاوت pH ثبت گردید و پس از پردازش اولیه، طول موج‌های مؤثر شامل 453.32، 669.95، 798.11، 821.26، 901.47 و 932.06 نانومتر انتخاب شدند. از کانال‌های تصویری متناظر با این طول موج‌ها، مجموعه‌ای از ویژگی‌های طیفی و آماری استخراج شد. به‌منظور طبقه‌بندی سطوح مختلف pH، با استفاده از یادگیری ماشینی، از شبکه عصبی مصنوعی بهره گرفته شد. نتایج نشان داد مدل بهینه در حالت استفاده از تمامی ویژگی‌ها دارای ساختار 42-6-4 و در حالت استفاده از ویژگی‌های منتخب، دارای ساختار 7-4-4 است. دقت طبقه‌بندی این دو مدل به‌ترتیب برابر با 98.6 و 97.2 درصد به‌دست آمد. یافته‌های این مطالعه بیانگر آن است که ترکیب تصویربرداری فراطیفی و الگوریتم‌های یادگیری ماشینی، روشی کارآمد، سریع، غیرمخرب و مقرون ‌به‌‌صرفه برای پایش کیفیت شیمیایی کودهای پتاس ارائه می‌دهد و می‌تواند جایگزین مناسبی برای روش‌های آزمایشگاهی مرسوم باشد.

کلیدواژه‌ها

پردازش تصویر

تصویربرداری فراطیفی

شبکه عصبی مصنوعی

کود شیمیایی

یادگیری ماشینی

موضوعات

• کشاورزی دیجیتال، هوشمند و دقیق

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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