CowLog - cross-platform application for coding behaviours from video

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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