תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםO Geffen - Precision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.) - The Volcani Center, 68 Hamaccabim Road, P.O.B 15159 Rishon Lezion7505101, Israe; Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B 653 Be'er Sheva8410501, Israel; Animal Science Institute, Agricultural Research Organization (A.R.O.) - The Volcani Center, 68 Hamaccabim Road, P.O.B 7505101 Rishon Lezion7505101, Israel.
Y Yitzhaky - Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B 653 Be'er Sheva8410501, Israel.
Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens' relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the 'gold standard'). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages.
O Geffen - Precision Livestock Farming (PLF) Lab, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.) - The Volcani Center, 68 Hamaccabim Road, P.O.B 15159 Rishon Lezion7505101, Israe; Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B 653 Be'er Sheva8410501, Israel; Animal Science Institute, Agricultural Research Organization (A.R.O.) - The Volcani Center, 68 Hamaccabim Road, P.O.B 7505101 Rishon Lezion7505101, Israel.
Y Yitzhaky - Electro Optical Engineering Department, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B 653 Be'er Sheva8410501, Israel.
Manually counting hens in battery cages on large commercial poultry farms is a challenging task: time-consuming and often inaccurate. Therefore, the aim of this study was to develop a machine vision system that automatically counts the number of hens in battery cages. Automatically counting hens can help a regulatory agency or inspecting officer to estimate the number of living birds in a cage and, thus animal density, to ensure that they conform to government regulations or quality certification requirements. The test hen house was 87 m long, containing 37 battery cages stacked in 6-story high rows on both sides of the structure. Each cage housed 18 to 30 hens, for a total of approximately 11 000 laying hens. A feeder moves along the cages. A camera was installed on an arm connected to the feeder, which was specifically developed for this purpose. A wide-angle lens was used in order to frame an entire cage in the field of view. Detection and tracking algorithms were designed to detect hens in cages; the recorded videos were first processed using a convolutional neural network (CNN) object detection algorithm called Faster R-CNN, with an input of multi-angular view shifted images. After the initial detection, the hens' relative location along the feeder was tracked and saved using a tracking algorithm. Information was added with every additional frame, as the camera arm moved along the cages. The algorithm count was compared with that made by a human observer (the 'gold standard'). A validation dataset of about 2000 images achieved 89.6% accuracy at cage level, with a mean absolute error of 2.5 hens per cage. These results indicate that the model developed in this study is practicable for obtaining fairly good estimates of the number of laying hens in battery cages.
