کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6539837 1421104 2018 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automated early yield prediction in vineyards from on-the-go image acquisition
ترجمه فارسی عنوان
خودکار پیش بینی عملکرد زودهنگام در تاکستان از دستیابی به تصویر
کلمات کلیدی
پیش بینی عملکرد اولیه انگور، تجزیه و تحلیل تصویر، حسگر غیر تهاجمی، دقت باغبانی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Early grapevine yield assessment provides information to viticulturists to help taking management decisions to achieve the desired grape quality and yield amount. In previous works, image analysis has been explored to this effect, but with systems performing either manually, on a single variety or close to harvest-time, when there are few rectifiable agronomic aspects. This study presents a solution based on image analysis for the non-invasive and in-field yield prediction in vines of several varieties, at phenological stages previous to veraison, around 100 days from harvest. To this end, an all-terrain vehicle (ATV) was modified with equipment to autonomously capture images of 30 vine segments of five different varieties at night-time. The images were analysed with a new image analysis algorithm based on mathematical morphology and pixel classification, which yielded overall average Recall and Precision values of 0.8764 and 0.9582, respectively. Finally, a model was calibrated to produce yield predictions from the number of detected berries in images with a Root-Mean-Square-Error per vine of 0.16 kg. This accuracy makes the proposed methodology ideal for early yield prediction as a very helpful tool for the grape and wine industry.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 144, January 2018, Pages 26-36
نویسندگان
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