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طبقهبندی محصولات کشاورزی و برآورد سطح کشت در مقیاس حوضهای با رویکرد یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 56، شماره 11، بهمن 1404، صفحه 3129-3156 اصل مقاله (1.71 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2025.402830.670012 | ||
| نویسندگان | ||
| مسعود سلطانی1؛ بهاره بهمن آبادی* 1؛ علی مختاران2 | ||
| 1گروه علوم و مهندسی آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران | ||
| 2بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز، | ||
| چکیده | ||
| در مناطق خشک و نیمهخشک، تهیه نقشه دقیق نوع و پراکندگی محصولات زراعی و برآورد سطح زیرکشت آنها نقشی اساسی در برنامهریزی منابع آب و مدیریت بهینه اراضی دارد. در این پژوهش، ابتدا پایشهای میدانی در حوضه مارون-جراحی، انجام و مختصات مزارع محصولات منتخب شامل گندم، ذرت، کنجد و یونجه بهصورت دقیق برداشت شد و سپس این نقاط مرجع در محیط گوگل ارث انجین بهمنظور همسانسازی با دادههای سنجشازدور مورد استفاده قرار گرفتند. در ادامه، تصاویر سنتینل-۲ پس از تصحیح جوی و حذف ابر و تصاویر سنتینل-۱ پس از اعمال فیلتر اسپکل و تصحیح هندسی آماده شدند. مجموعهای از شاخصهای طیفی برگرفته از سنتینل-۲ و ویژگیهای شدت و نسبت پلاریزاسیون برگرفته از سنتینل- ۱ استخراج و با یکدیگر ترکیب گردید تا امکان تفکیک بهتر محصولات با رفتار فنولوژیک مشابه فراهم شود. نمونههای میدانی به نسبت ۷۰ درصد برای آموزش و ۳۰ درصد برای ارزیابی مدلها تقسیم شدند و سه الگوریتم جنگل تصادفی، SVM و XGBoost برای طبقهبندی نهایی بهکار گرفته شد. ارزیابی مدلها بر اساس ماتریس درهمریختگی، صحت کلی و ضریب کاپا انجام شد. نتایج نشان داد الگوریتم جنگل تصادفی با صحت کلی ۹۶ % و ضریب کاپا 93/0 و F1-Score، 97/0 عملکرد برتری نسبت به دو روش دیگر داشته و تفکیک گندم و سایر محصولات را با دقت بالا ممکن ساخته است؛ در حالیکه مدل XGBoost با صحت ۸۹ % در رتبه بعدی قرار گرفت و SVM عملکرد ضعیفتری نشان داد. مقایسه سطح زیرکشت برآورد شده با آمار رسمی نیز تطابق قابل توجه نتایج جنگل تصادفی را تأیید کرد. | ||
| کلیدواژهها | ||
| حوضه مارون-جراحی؛ سنتینل؛ جنگل تصادفی؛ svm؛ XGBoost | ||
| مراجع | ||
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