Feasibility of Body Weight Estimation of Kalkoohi Camels Using Digital Image Processing

محورهای موضوعی : Camel

م. خجسته کی ¹ ( Department of Animal Science, Qom Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Qom, Iran )
م. کلانتر نیستانکی ² ( Education Center, Agricultural Research, Education and Extension Organization (AREEO), Qom, Iran )
ز. رودباری ³ ( Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran )
ح. صادقی پناه ⁴ ( Department of Animal Science, Animal Science Research Institute of Iran (ASRI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran )
ه. جواهری ⁵ ( Animal Science Research Institute of Iran (ASRI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran )
ع.ر. آقاشاهی ⁶ ( Animal Science Research Institute of Iran (ASRI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran )

تاریخ دریافت : 1398/03/30 تاریخ پذیرش : 1398/06/09 تاریخ انتشار : 1399/03/12

کلید واژه: image processing, Kalkoohi camels, weight estimation,

چکیده مقاله :

The aim of this study was to investigate the possibility of estimating the weight of Kalkoohi camels using digital image processing. For this purpose, Kalkoohi camels were weighed monthly on a private farm for one year. On the day of weighing, digital images were taken from all camels from their lateral side. These digital images were processed in MATLAB software environment and the required numerical features of each image including different morphological features were extracted. Among all extracted features, major axis length, minor axis length, the number of non-zero elements (NNZ) and equivalent diameter had a significant and high correlation with the weight of camels (p <0.01) and were considered as effective features in developing neural network. The multi-layer artificial neural network, which was trained by back propagation algorithm, was used to estimate the weight of camels based on their digital images. The accuracy of the final model in estimating the weight of Kalkoohi camels based on their image features was 99%. The correlation coefficient between the estimated weights by artificial neural network model and the actual weights of the camels was 98%, and the deviation of estimated weights from the measured weight of camels was 2.21 kg. The results of this study revealed that digital image processing technology has a good potential to estimate the weight of Kalkoohi camels, and this method could be a good alternative to weigh camels using a scale.

چکیده انگلیسی:

هدف از این مطالعه بررسی امکان برآورد وزن شترهای کلکوهی با استفاده از پردازش تصاویر دیجیتال بود. برای این منظور، شترهای کلکوهی ماهانه در یک مزرعه خصوصی به مدت یک سال وزن می­شدند. در روز وزن­کشی، تصاویر دیجیتال از تمام شترها و از نمای جانبی آن­ها گرفته شد. این تصاویر دیجیتال در محیط نرم­افزار MATLAB پردازش شده و ویژگی­های عددی مورد نیاز هر تصویر از جمله ویژگی­های مختلف مورفولوژیکی تصاویر استخراج شد. از بین تمام ویژگی­های استخراج شده، برخی مانند طول محور اصلی، طول محور فرعی، تعداد عناصر غیر صفر (NNZ) و قطر معادل با وزن شترها ارتباط معنی­دار و بالایی داشتند (P<0.01) و از این نظر به­ عنوان ویژگی­ های مؤثر در توسعه شبکه عصبی در نظر گرفته شدند. برای برآورد وزن شترها بر اساس تصاویر دیجیتالی آن­ها از شبکه عصبی مصنوعی چند لایه که با الگوریتم پس انتشار خطا آموزش داده شده بود، استفاده شد. دقت مدل نهایی در برآورد وزن شترهای کلکوهی براساس ویژگی­ های تصویر آن­ها 99 درصد بود. ضریب همبستگی بین وزن تخمین­زده­ شده با مدل شبکه عصبی مصنوعی و وزن واقعی شترها 98 درصد و انحراف وزن تخمین­زده شده از وزن واقعی شترها 2.21 کیلوگرم بود. نتایج این تحقیق نشان داد که فناوری پردازش دیجیتال ظرفیت خوبی برای تخمین وزن شترهای کلکوهی دارد و این روش می­تواند جایگزین مناسبی برای وزن­کشی شترها با استفاده از باسکول باشد.

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