Livestock classification and counting in quadcopter aerial images using Mask R-CNN

Beibei Xu,Wensheng Wang,Greg Falzon,Paul Kwan,Leifeng Guo,Zhiguo Sun,Chunlei Li

doi:10.1080/01431161.2020.1734245

Abstract

ABSTRACT Quadcopters equipped with machine learning vision systems are bound to become an essential technique for precision agriculture applications in pastures in the near future. This paper presents a low-cost approach for livestock counting jointly with classification and semantic segmentation which provide the potential of biometrics and welfare monitoring in animals in real time. The method used in the paper adopts the state-of-the-art deep-learning technique known as Mask R-CNN for feature extraction and training in the images captured by quadcopters. Key parameters such as IoU (Intersection over Union) threshold, the quantity of the training data and the effect the proposed system performs on various densities have been evaluated to optimize the model. A real pasture surveillance dataset is used to evaluate the proposed method and experimental results show that our proposed system can accurately classify the livestock with an accuracy of 96% and estimate the number of cattle and sheep to within 92% of the visual ground truth, presenting competitive advantages of the approach feasible for monitoring the livestock.

Highlights

In order to meet the growing population demand for meat and improve meat quality, livestock monitoring including behaviours and health has become a hot research topic among livestock management
The performance evaluation of livestock classification and counting follows the same training and testing settings described in Section 2.2 and Section 3.1, and the IoU threshold is set as 0.4
Even though the precision and the recall as a function of IoU thresholds, respectively, are previously presented in Section 3.3, we need to combine these two metrics to evaluate the performance of livestock classification

Summary

Introduction

In order to meet the growing population demand for meat and improve meat quality, livestock monitoring including behaviours and health has become a hot research topic among livestock management. Death or loss due to hunting or unintentional factors such as drown in the river or landslides, and dangerous infectious diseases carried from invasive species, pose great threats to livestock management. With accurate knowledge of species and quantity of livestock, the farmers or the farm managers can efficiently monitor the animals to avoid animals’ loss or invasion by other species causing vandalization of crops such as hares and wild boars (Priyadharshini et al 2018). The farmers or the farm managers need to consciously control the number of livestock to adapt to the carrying capacity of the grazed pastures, or the pastures will be overgrazed which leads to soil degradation and environmental damage (Evju et al 2006; Oesterheld, Sala, and McNaughton 1992)

Objectives

Methods

Results

Discussion

Conclusion