CowLog - cross-platform application for coding behaviours from video

201

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.Karjojen keskikoko kasvaa jatkuvasti ja automaatio lypsyssä ja ruokinnassa lisääntyy. Maailmassa oli vuonna 2006 käytössä yli 6000 lypsyrobottia ja Suomessakin noin 200. Tilakoon kasvun seurauksena karjanhoitajan yksittäisen eläimen tarkkailemiseen käyttämä aika lyhenee ja mahdollisuus havaita eläinten terveysongelmat heikkenee. Tästä johtuen automaattisia menetelmiä tarvitaan tuotannon lisäksi myös lehmien terveyden seurantaan. Lypsykarjan ontuminen on yksi maailman suurimmista lehmien terveys- ja hyvinvointiongelmista. Jalkaongelmat aiheuttavat lehmille kipua ja heikentävät niiden hyvinvointia sekä heikentävät niiden maitotuotosta. Tuotoksen heikkeneminen, lehmien ennenaikainen poisto ja jalkavikojen hoito aiheuttavat merkittäviä taloudellisia menetyksiä karjan kasvattajille. Ontumisen aiheuttamia taloudellisia ja hyvinvointivaikutuksia voidaan pienentää merkittävästi, jos ongelma havaitaan ja hoidetaan aikaisessa vaiheessa. Jalkavikojen automaattinen mittaaminen tilatasolla mahdollistaa ontumisen nykyistä tarkemman seurannan ja säästää viljelijän työaikaa avustamalla eläinten tarkkailussa. Tutkimukset osoittavat, että suuri osa ontuvista lehmistä jää tiloilla kokonaan huomaamatta. Ongelman tunnistaminen mahdollistaa sen hoitamisen ja parantaa samalla eläinten hyvinvointia ja tilan taloudellista tulosta. Väitöstyö tehtiin Helsingin yliopiston Agroteknologian laitoksella ja mittaukset Suitian opetus- ja tutkimustilalla. Tutkimuksessa kehitettiin ensin nelivaakajärjestelmä, jolla punnitaan lehmän jokaisen jalan paino erikseen lypsyn aikana. Järjestelmä koostui neljästä leikkausvoima-anturista joiden päälle oli asennettu vaakasillat, vahvistimesta ja tietokoneesta sekä seurantaohjelmistosta. Järjestelmällä kerätyn yli 10 000 mittauksen ja säännöllisten eläinlääkärin tarkastusten perusteella kehitettiin jalkaviat havaitseva neuroverkkomalli. Valvontajärjestelmä havaitsi kaikki tutkimuksen aikaiset jalkaongelmat. Hälytyksen herkkyyttä voidaan säätää yhtä parametria muuttamalla. Kun valvonta säädetään varmasti havaitsemaan kaikki ongelmat, myös väärien hälytysten määrä lisääntyy. Tämä on useimmiten hyväksyttävää, koska hälytyksen tarkoitus on kiinnittää viljelijän huomio mahdollisesti sairaaseen eläimeen ja viljelijä tekee päätöksen hoitotarpeesta itse

CowLog : open-source software for coding behaviors from digital video

We have developed CowLog, which is open-source software for recording behaviors from digital video and is easy to use and modify. CowLog tracks the time code from digital video files. The program is suitable for coding any digital video, but the authors have used it in animal research. The program has two main windows: a coding window, which is a graphical user interface used for choosing video files and defining output files that also has buttons for scoring behaviors, and a video window, which displays the video used for coding. The windows can be used in separate displays. The user types the key codes for the predefined behavioral categories, and CowLog transcribes their timing from the video time code to a data file. CowLog comes with an additional feature, an R package called Animal, for elementary analyses of the data files. With the analysis package, the user can calculate the frequencies, bout durations, and total durations of the coded behaviors and produce summary plots from the data.Peer reviewe

Simple online algorithm for detecting cow’s ECG beat-to-beat interval using a microcontroller

This paper describes an online algorithm for detecting cow’s beat-to-beat interval on a small embedded microcontroller. The target device is an ECG implant which only provides limited calculation power and insufficient storage memory for long term complete ECG data logging. No common computationally efficient method for detecting the human R-wave was found successful for cattle ECG data with the used measurement configuration. Our algorithm detects a cow’s S-wave, which is the most distinguishable part of the QRS-complex. The offset and amplitude adaptive algorithm utilizes only arithmetic operations and logic conditions.Peer reviewe

Recycled manure solids as a bedding material: Udder health, cleanliness and integument alterations of dairy cows in mattress stalls

Interest in using recycled manure solids (RMS) as a bedding material for dairy cattle is increasing, but suitable information for Nordic housing conditions is scarce. The aim of our study was to investigate the effect of RMS bedding on dairy cow welfare compared to peat bedding commonly used in Finland. We conducted a 2 × 3-month cross-over study with two groups of 24 lactating dairy cows, where first three months one group was housed with RMS-bedding and the other group with peat-bedding and then vice versa for the next three months. We followed integument alterations, cleanliness of the animals and udder health fortnightly. With RMS bedding cows had less severe integument alterations in tarsal joints (p=0.0031) and their udders were cleaner (p=0.0109) compared to peat. Somatic cell count did not differ between bedding materials, but link between RMS bedding and the small number of cases of clinical mastitis cannot be ruled out. Based on this study, RMS could provide an economical and renewable bedding source for Nordic dairy farms, with no major effects on animal welfare

Real-time recognition of sows in video : A supervised approach

This paper proposes a supervised classification approach for the real-time pattern recognition of sows in an animal supervision system (asup). Our approach offers the possibility of the foreground subtraction in an asup’s image processing module where there is lack of statistical information regarding the background. A set of 7 farrowing sessions of sows, during day and night, have been captured (approximately 7 days/sow), which is used for this study. The frames of these recordings have been grabbed with a time shift of 20 second. A collection of 215 frames of 7 different sows with the same lighting condition have been marked and used as the training set. Based on small neighborhoods around a point, a number of image local features are defined, and their separability and performance metrics are compared. For the classification task, a feed-forward neural network (NN) is studied and a realistic configuration in terms of an acceptable level of accuracy and computation time is chosen. The results show that the dense neighborhood feature (d.3x3) is the smallest local set of features with an acceptable level of separability, while it has no negative effect on the complexity of NN. The results also confirm that a significant amount of the desired pattern is accurately detected, even in situations where a portion of the body of a sow is covered by the crate’s elements. The performance of the proposed feature set coupled with our chosen configuration reached the rate of 8.5 fps. The true positive rate (TPR) of the classifier is 84.6%, while the false negative rate (FNR) is only about 3%. A comparison between linear logistic regression and NN shows the highly non-linear nature of our proposed set of features.Peer reviewe

ICT monitoring and mathematical modelling of dairy cows performances in hot climate conditions: a study case in Po valley (Italy)

Automatic Milking Systems (AMS) measure and record specific data about milk production and cow behaviour, providing farmers with useful real-time information for each animal. At the same time, indoor climatic conditions in terms of temperature and humidity within a dairy livestock barn represent a well-known crucial issue in farm building design and management, since these parameters can remarkably influence cows behaviour, milk yield and animal welfare.The goal of the study is to develop and test an innovative procedure for the comprehensive analysis of AMS-generated multi-variable time-series, with a focus on the analysis of the relationship between milk production and indoor climatic conditions. The specific purpose of the study is to develop and test a mathematical computer procedure using AMS-generated data and environmental parameters, designed to provide a forecasting model based on the integration of milking data and temperature and humidity levels surveyed from local sensor grids, designed to model milk production scenarios and, specifically, yield trends depending on the expected environmental conditions.For this purpose, a typical Italian farm with AMS has been adopted as a study case and internal climatic data of the barn have been analysed to understand the influence of high values of the Temperature Humidity Index (THI) on milk production in time. Then the correlation between yield variations and THI has been computed and characterized. Finally, external climatic data have been used to forecast the milk production in summertime. Once the model was validated, tests has led to predict milk yield with a relative error smaller than 2%.This study represents a step of a research aimed to define integrated systems for cow monitoring and to develop guidelines for the optimization of barn layouts

Validation of a tail-mounted triaxial accelerometer for measuring foals' lying and motor behavior

Foals' locomotory and lying-down behavior can be an indicator of their health and development. However, measurement tools have not been well described with previously reported attachment sites used on limbs of adult horses unsafe for longer-term data collection in foals. In this study, a tail-mounted three-dimensional accelerometer was validated for monitoring foals lying, standing, and walking behavior. Eleven foals were recruited: four hospitalized and seven at private breeding stables. Accelerometers were attached to the dorsal aspect of the base of each foal's tail and their behavior was video recorded. Hospitalized foals had continuous video monitoring inside their stalls, and the breeding stable's foals were monitored outside at pasture for 1-5 periods (mean 42 minutes per period), depending how long they were at the facility. Acceleration was measured using 100 Hz frequency and mean, maximum, and minimum acceleration were recorded in 5 second epochs for x-, y-, and z-axes. Lying, standing, and walking behavior was monitored from videos of all foals, and the start and end time of each behavior was compared with the corresponding data from the accelerometer. Naive Bayes classifier was developed by using dynamic body acceleration and craniocaudal movement of the tail (tilt along z-axis), to predict a foal's lying behavior. The model was validated; the classifier achieved high accuracy in precision and in classifying foals' lying behavior (specificity, 0.92; sensitivity, 0.89; precision, 0.98; accuracy, 0.92). The overall accuracy for classifying walking and standing was also good, but the precision was poor (0.46 and 0.24, respectively). When standing and walking behavior was combined to a single "standing or walking" class, the precision improved (specificity, 0.62; sensitivity, 0.92; precision, 0.89; accuracy, 0.92). In conclusion, tail-mounted three-dimensional accelerometer can be used for monitoring foals' lying behavior. In addition, information regarding standing and walking can be gained with this method. (C) 2020 The Authors. Published by Elsevier Inc.Peer reviewe