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

ماشین های کشاورزی

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 05 مهر 1404   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.22067/jam.2025.93983.1394

نویسندگان

محمدحسین نرگسی^{* 1}؛ کامران خیرعلی پور¹؛ فاطمه ولی زاده کاخکی²؛ زهرا مرادی³

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

²دکتری محیط‌زیست گرایش آلودگی‌های محیطی، ایلام، ایران

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

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

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