تقدم وتوقعات بحث تصنيف المزارع المغطاة بالأغشية باستخدام الاستشعار عن بُعد

  • role: First author第一作者
  • Affiliation:

    School of Surveying and Mapping Engineering, Jilin Jianzhu University, Changchun 130118, China

  • Email:jljggis@163.com
  • Introduction:E-mailjljggis@163.com
XIAO Yang1,  
  • Affiliation:

    School of Surveying and Mapping Engineering, Jilin Jianzhu University, Changchun 130118, China

CHANG Junyi1,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Surveying and Mapping Engineering, Jilin Jianzhu University, Changchun 130118, China

    Key Laboratory of Black Soil Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

  • Email:yangqian@iga.ac.cn
  • Introduction:E-mailyangqian@iga.ac.cn
YANG Qian12*,  
  • Affiliation:

    School of Surveying and Mapping Engineering, Jilin Jianzhu University, Changchun 130118, China

TAO Feng1,  
  • Affiliation:

    Key Laboratory of Black Soil Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

DU Jia2,  
  • Affiliation:

    Key Laboratory of Black Soil Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

LIU Huanjun2

ملخص

تتمتع تقنية زراعة الأغشية بقدرات على حفظ الحرارة والرطوبة، وإزالة الأعشاب الضارة وكبح الملح، وزيادة الإنتاجية، ولكن مشكلة تلوث الأغشية الزراعية الناتجة عنها تزداد حدة يوماً بعد يوم. يمكن لتقنية الاستشعار عن بُعد الحصول بدقة على التوزيع المكاني والزماني لمناطق الزراعة المغطاة بالأغشية على نطاق واسع، مما يوفر دعماً بيانياً لحماية التربة السوداء وتحسين البيئة الزراعية. من أجل مراجعة منهجية لحالة الأبحاث المتعلقة بتصنيف المزارع المغطاة بالأغشية عبر الاستشعار عن بُعد، استندت هذه الورقة إلى تحليل الأدبيات من قواعد بيانات الشبكة المعرفية الصينية (CNKI) وWeb of Science (WOS)، وبدأت بمراقبة المزارع المغطاة بالأغشية باستخدام تقنيات الاستشعار عن بُعد، واستعرضت حالة البحث في تحديد وتصنيف المزارع المغطاة بالأغشية من ناحية بيانات الاستشعار الشائعة، استخراج الميزات، تصنيف الصور، ودمج المعلومات. أظهرت النتائج أن بيانات الاستشعار ذات الدقة المتوسطة للصورة البصرية هي المصدر السائد؛ وتتفوق الخصائص الطيفية في استخراج المعلومات مقارنة بالخصائص النسيجية. وتُستخدم طريقة دعم المتجهات والغابات العشوائية على نطاق واسع ضمن طرق التعلم الآلي التقليدية، في حين أن طرق التعلم العميق تقدم دقة أعلى لكنها تعتمد على كمية كبيرة من البيانات. يساهم دمج المعلومات متعددة المصادر في تحسين دقة التصنيف بشكل فعّال. ولا تزال الأبحاث تواجه مشاكل مثل "الأجسام المختلفة ذات الطيف المتشابه"، والتداخل بين البكسلات، وارتفاع تكلفة الحوسبة للتعلم العميق. وينبغي في المستقبل تعزيز دمج البيانات متعددة المصادر، وتطوير نماذج خفيفة الوزن، ورصد كامل دورة حياة الأغشية الزراعية. وتلخص هذه الورقة القضايا والتحديات الحديثة في تحديد وتصنيف المزارع المغطاة بالأغشية باستشعار عن بُعد، وتتوقع الاتجاهات والتنمية المستقبلية، وتوفر مرجعية لتقدم وسائل المراقبة في هذا المجال.

مفهوم

زراعة الأغشية; تلوث الأغشية الزراعية; الاستشعار عن بُعد متعدد المصادر; استخراج الميزات; تصنيف الصور

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