Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 21-36.doi: 10.12133/j.smartag.2020.2.3.202006-SA003

• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles     Next Articles

Current State and Challenges of Automatic Lameness Detection in Dairy Cattle

HAN Shuqing1(), ZHANG Jing1, CHENG Guodong1, PENG Yingqi2, ZHANG Jianhua1, WU Jianzhai1()   

  1. 1.Agricultural Information Institute of Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
    2.College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625014, China
  • Received:2020-06-08 Revised:2020-07-08 Online:2020-09-30 Published:2020-12-09
  • corresponding author: Jianzhai WU E-mail:hanshuqing@caas.cn;wujianzhai@caas.cn

Abstract:

Lameness in dairy cattle could cause significant economic losses to the dairy industry. Detection of lameness in a timely manner is critical to the high-quality development of dairy industry. The traditional method is visual locomotion scoring by dairy farmers, which is low efficiency, high cost and subjective. The demand for automated lameness detection is increasing. The review was conducted to find out the current state and challenges of automatic lameness detection technology development and to learn from the latest findings. The current automatic lameness detection systems were reviewed in this paper mainly rely on five technologies or combinations thereof, including machine vision, pressure distribution measuring system, wearable sensor system, behavior analysis and classification; the principle, function and features of these technologies were analyzed. Machine vision technique is to extract feature variables (e.g. back arch, head bob, abduction, stride length, walking speed, temperature, etc.) from video recordings of cattle movement by image processing. Pressure distribution measuring system contains an array of load cells to sense gait variables, when dairy cattle are walking by. By using accelerometer with high frequency data collection, the gait cycle parameters can be extracted and used for lameness detection. By using wearable devices, the number of lying/standing bouts and their duration, the total time spent lying, standing and ruminating per day can be recorded for individual cattle. The lameness can also be detected by behavior analysis. Currently, most of these studies were in the stage of sensor development or validation of algorithm. A few studies were in the stage of validation of performance and decision support with early warning system. The challenges to apply automatic lameness detection system in dairy farm includes the difficulties of acquiring high quality data of lameness features, lack of techniques to detect early lameness, identification errors caused by individual gait differences among dairy cattle, difficulties to function well in unstructured environment and difficulties to evaluate the benefits. To accelerate the development of automatic lameness detection systems, recommendations are proposed as follows: ①promoting lameness data sharing and data exchange among dairy farms; ②developing individual-based lameness classification model; ③developing multifunctional smart station which can detect lameness, measure body condition score, weighing, etc; ④evaluating the significance of automatic lameness detection to the dairy industry from the perspective of animal welfare, environment and food safety.

Key words: lameness detection, behavior analysis, precision livestock farming, machine vision, automated dection, pressure distribution, wearable sensor system

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