نوع مقاله : مقاله پژوهشی

نویسنده

دانشگاه شهید مدنی آذربایجان

چکیده

نیاز به اطلاعات دقیق و با کیفیت خاک به‌دلیل کاربرد در برنامه­ریزی­های کشاورزی در حال افزایش است. هدف این تحقیق برآورد رنگ و بافت خاک با استفاده از اطلاعات تصاویر ماهواره­ای به‌عنوان داده­های ورودی رگرسیون بردار پشتیبان و رگرسیون درختی می­باشد. در پژوهش حاضر، شاخص­های ماهواره­ای NDVI، PDI،SAVI ، MPDI،MSAVI ،TCI ، TVX، VHI، NVWSI، RVWSI،MVWSI  وVCI  استفاده شد. آزمون دانکن در سطح احتمال پنج درصد حاکی از وجود تغییرات معنی­دار زمانی بین شاخص­ها است. تفاوت معنی­دار بین میانگین شاخص­ها از نظر تنوع بافت خاکی براساس آزمون دانکن در سطح احتمال پنج درصد وجود نداشت. آماره­های خطای RMSE،RRMSE ، AMAPE و MSE در مورد شن از رگرسیون درختی به رگرسیون بردار پشتیبان به ترتیب 43/15، 33/13، 41/16 و 7/28 درصد کاهش داشتند. تعیین بافت خاک با مثلث بافت خاک در دوره صحت­سنجی حاکی از تطابق نوع بافت خاک مشاهداتی و رگرسیون بردار پشتیبان بود. با در نظر گرفتن اجزاء بافت خاک و مولفه­های رنگ خاک آماره RPD از رگرسیون درختی به رگرسیون بردار پشتیبان به میزان 43/12 درصد افزایش داشت که بیانگر کارایی رگرسیون بردار پشتیبان در برابر رگرسیون درختی است. درصد کاهش آماره­های RMSE،RRMSE    و MSE از رگرسیون خطی چندگانه به رگرسیون بردار پشتیبان در هیو به ترتیب 88/76، 4/77 و 6/94 و در رگرسیون درختی به ترتیب 15/72، 58/72 و 92/92 بیانگر عملکرد بهتر دو مدل رگرسیونی نسبت به رگرسیون خطی چندگانه بود. براساس تحلیل از جهات گوناگون رگرسیون بردار پشتیبان نسبت به رگرسیون درختی عملکرد بهتری در تعیین رنگ و بافت خاک داشت.

کلیدواژه‌ها

عنوان مقاله [English]

Soil Texture and Color Identification Using Artificial Intelligence Algorithm and Satellite Images

نویسنده [English]

  • Laleh Parviz

Azarbaijan Shahid Madani University

چکیده [English]

The demand for quality and low-cost soil information is growing because of necessity in land use planning and precision agriculture. The aim of this study is soil texture and color estimation using satellite images information as input variables of support vector regression and tree regression. NDVI, PDI, SAVI, MPDI, MSAVI, TCI, TVX, VHI, NVWSI, RVWSI, MVWSI, VCI are satellite indices which related to the region in Azarshahr (East Azarbaijan). Duncan's test at the 5% probability level indicates significant time differences between the indices. There was no significant difference among the average of indices in terms of soil texture diversity. The error criteria RMSE, RRMSE, MAPE and MSE decreasing regard to sand from tree regression to support vector regression was 15.43, 13.33, 16.41 and 28.7%, respectively. Determination of soil texture with soil texture triangle in the validation period indicated the agreement of soil texture between observation and support vector regression. Considering soil texture and color components, RPD statistic increased from tree regression to support vector regression by 12.43%, which indicates the efficiency of support vector regression against tree regression. RMSE, RRMSE and MSE decreasing from multiple linear regression to support vector regression in hue were 76.88, 77.4 and 94.6%, respectively and for tree regression were 72.15, 72.58 and 92.92%, respectively, which is indicative of better performance of two regression models relative to simple regression. Based on various aspects of analysis, support vector regression had better performance for soil color and texture determination than tree regression.

کلیدواژه‌ها [English]

  • Satellite indices
  • Regression
  • Error Criteria
  • RPD
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