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

Effectiveness of TMC AI Applications in Case Studies

DTFH61-16-D00030Traffic incident detection is a crucial task in traffic management centers (TMCs) that typically manage large highway networks with limited staff. An effective automatic incident-detection approach could benefit TMCs by helping to report abnormal events in a timely and accurate manner and optimize operating resources. During the past decades, researchers have made significant progress in developing such automatic approaches. Nevertheless, the majority of the developed approaches have shown limited success in the field, largely because of concerns about their often-costly false alarms (e.g., misdispatching response teams to a nonexistent incident). Fortunately, recent advances in artificial intelligence (AI) are expected to provide opportunities for improving conventional TMC operations. This project aimed to propose an AI-based incident-detection framework that can leverage large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. Researchers investigated the generic algorithmic problems in designing a detection approach and emphasized the architecture of the AI-based detection framework by including learning and evolving capabilities. The proposed framework was assessed with a fully controlled experiment in simulation that consisted of numerous traffic and incident scenarios. The results indicated that the proposed AI-based framework achieved higher detection rates, lower false alarm rates, and shorter time to detect the incidents in the studied scenarios than conventional approaches. Some extensions of the proposed framework are also discussed

Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks

10.1109/72.950145IEEE Transactions on Neural Networks1251173-1187ITNN

Developing Algorithms to Detect Incidents on Freeways From Loop Detector and Vehicle Re-Identification Data

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident scenarios and generating the input data. Two incident scenarios are generated by closing either one lane or two lanes of a four-lane highway. The third scenario involves bottleneck blocking two lanes of the freeway with an incident occurring in the upstream of the bottleneck. The highway network is five miles long and simulated in VISSIM. Traffic parameters like occupancy, speed, flow and number of vehicles passing through the loop detector are collected to assess the traffic condition between the sensors or detectors. The proposed performance test inspects whether the algorithms thus tested were able to detect any occurrences and incidences within the first minutes in different scenarios and compares their respective detection to identify the best performing algorithm in all the contingencies. The results indicate that the implementation of this new approach not only reduces the dilemma of selecting thresholds but also checks algorithm performance in different incident scenarios so that the response time for clearing such incidences is as short as possible. Likewise, making use of Re identification data and travel time makes the incident detection more trivial and self-evident and thus outperformed the algorithms using traditional data like occupancy speed and volume in uncontested traffic conditions. Further different SVM models were trained and tested inspecting the effects of change in location of incident concerning detectors. However, using data from loop detector performed well when the incident happened at the upstream detector while using that from re-identification encountered delays in overall detection time for the same