Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g. step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer’s mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Index Terms—Lameness, Internet of Things (IoT), Data Analytics, Smart Agriculture, Machine Learning, Micro services, Fog Computing. I

Influence of claw health on fertility and milk production in dairy cows

Lameness and claw lesions are frequent health problems observed among dairy cows and are a common reason for culling, reduced milk production and reproductive inefficiency. The present study investigated the association between claw health, reproduction and milk production of dairy cows at the Aland Islands in order to gain an understanding of the current situation. The study included data from 17 farms during the years 2013 and 2014 regarding claw health, reproduction, milk production and housing system. Five of the herds kept the cows in free stalls and 12 herds kept the cows in tie-stalls. The recorded claw lesions were divided into infectious diseases and laminitis related diseases. Dermatitis, digital dermatitis, heel horn erosion, interdigital hyperplasia, wart growth, and interdigital phlegmone were considered as infectious diseases. Sole hemorrhage, sole ulcer, double sole, white line fissure, toe abscess, white line abscess and chronic laminitis, on the other hand, were considered as laminitis related diseases. Each disease was given a severity score depending on its clinical severity. The higher the total score a cow got the worse were the claw health. In the statistical analyses all trimming sessions for each cow were compared so that only the most severe score was used. This resulted in each cow having a maximum score for infectious diseases (maximum infectious point, MIP), a maximum score for laminitis related diseases (maximum laminitis point, MLP) and a maximum score for these two added together (maximum total point, MTP). Five parameters were studied in order to evaluate the effect of claw health on reproduction and milk production; number of services (inseminations), interval from first service to last service (IFLS), interval from calving to last service (ICLS), calving interval (CI) and mean ECM production. Fifty per cent of the total number of trimmed cows during 2013 had no recorded claw lesions; the same number in 2014 was 52 %. No significant correlations between year of production and MIP, MLP and MTP could be observed. Laminitis related diseases were more common on all farms compared to infectious diseases and a difference in the prevalence of both laminitis related diseases and infectious diseases could be observed between farms. There were significantly more infectious diseases on farms with free stall systems compared to farms with tie-stalls during both years (P=0.000 and P=0.016). A significant difference in the occurrence of laminitis related diseases between the claw trimmers was found. There were no significant differences between tie-stalls and free stalls herds concerning number of services, IFLS, ICLS, CI and mean ECM production. Average ECM production was not affected by an increase in MTP, MIP or MLP and there were no tendencies for more services, longer ICLS or longer CI with increased MTP. There was however a tendency towards a positive correlation between IFLS and MTP. The anecdotal evidence that dairy cows on the Aland Islands have better claw health than their counterparts in Sweden can neither be proved nor rejected based on the results from this study. The present study was an epidemiologic study, to draw conclusions about cause - effect relationships are therefore impossible. However, the results from this study can hopefully be a base in the future works towards a better claw health status at the Aland Island.Hältor och skador i klövarna är hälsoproblem som ofta uppmärksammas hos dagens mjölkkor. Studier i ämnet rapporterar om försämrad mjölkproduktion, försämrad reproduktiv förmåga samt ett ökat antal utslaktningar i samband med klövproblem. För att få en uppfattning om den nuvarande situationen på Åland undersökte den här studien sambandet mellan klövhälsa, mjölkproduktion och reproduktion hos 17 gårdar. Data hämtades från åren 2013 och 2014. Fem av gårdarna hade lösdrift med liggbås och 12 av gårdarna hade uppbundna kor. För att underlätta de statistiska analyserna delades de registrerade klövsjukdomarna upp i två grupper; smittsamma klövsjukdomar och fångrelaterade klövsjukdomar. Dermatit (lindrig), digital dermatit, klövröta, limax, vårta och klövspaltsinflammation ansågs vara smittsamma sjukdomar medan sulblödning, klövsulesår, dubbelsula, hålvägg/separation vita linjen, tåböld, sår/böld i vita linjen och fångbrytning ansågs vara fångrelaterade sjukdomar. Varje klövsjukdom tilldelades en poäng beroende på hur kliniskt allvarlig den var. Ju högre poäng en ko fick, desto sämre var klövhälsan. Poängen för varje klövverkningstillfälle jämfördes så att endast den högsta poängen för varje ko användes i de statistiska analyserna. Detta resulterade i att alla kor fick en maxpoäng för smittsamma sjukdomar (MIP) och en maxpoäng för fångrelaterade sjukdomar (MLP). Dessa två adderades så att alla kor också fick en total maxpoäng (MTP). För att undersöka om klövhälsan hade någon effekt på reproduktion och mjölkproduktion undersöktes fem parametrar; antal insemineringar, intervallet från första inseminering till den sista (IFLS), intervallet från kalvning till den sista insemineringen (ICLS), kalvningsintervall (CI) och den medelsnittliga ECM produktionen. I studien var 50 % av alla kor friska och utan några rapporterade klövskador år 2013 och 2014 var siffran 52 %. Studien kunde inte finna någon signifikant korrelation mellan produktionsår och MIP, MLP och MTP. Fångrelaterade klövsjukdomar var generellt mer vanligt förekommande på gårdarna än smittsamma sjukdomar och det fanns en stor skillnad i förekomst av både smittsamma och fångrelaterade klövsjukdomar mellan gårdarna. Korna på gårdarna med lösdrift hade signifikant mer smittsamma klövsjukdomar båda åren (P=0.000 och P=0.016) jämfört med de gårdarna där korna stod uppbundna. Det fanns en signifikant skillnad i förekomst av fångrelaterade sjukdomar mellan de tre klövvårdarna som deltog i studien. Det fanns ingen skillnad i antal insemineringar, IFLS, ICLS, CI och medelsnittlig ECM produktion mellan gårdar med lösdrift och uppbundet. Den medelsnittliga ECM produktionen påverkades inte av ökade MIP, MLP eller MTP. Inte heller fanns det någon tendens för ett ökat antal insemineringar, längre ICLS eller längre CI då MTP ökade. Dock fanns det en tendens till en liten positiv korrelation mellan IFLS och MTP. Uppgifter om att mjölkkor på Åland har bättre klövhälsa än deras motsvarigheter i Sverige kan varken bevisas eller förkastas på baserat på resultaten från denna studie. Detta var en epidemiologisk studie så det är inte möjligt att dra slutsatser om orsak och verkan. Dock kan studien förhoppningsvis fungera som stöd i det fortsatta arbetet för en förbättrad klövhälsa på Åland

Analysing the Movement and Behaviour of Housed Dairy Cows

Cows in modern dairy systems are at risk of comprised health and welfare, and monitoring changes in behaviour can help identify early-warning signs. This thesis uses a local positioning system to detect changes in group-level behaviour. The proximity interaction network structure and consistency of a herd housed in a closed barn on a commercial farm in Essex is explored. Next, the network structure, alongside group-level space-use patterns, on the commercial farm in Essex are compared to those of a second dairy cow herd housed in an open barn (RVC Research farm). In the subsequent chapters, the relationship between barn temperature and bunching behaviour, a potentially maladaptive response to warmer than average temperatures, was investigated in both herds, through various bunching metrics: range size, inter-cow distance and nearest neighbour distance. The herd on the commercial farm in Essex was highly connected and temporally unstable, with inter-individual variation in interactions in the non-feeding zone, and social differentiation across functional zones. No social assortment by parity, days in milk or lameness state was detected. The herd on the RVC Research farm were less connected than the herd on the commercial farm in Essex. Inter-individual variation in proximity interactions was found in the feeding zoneof the RVC Research farm, alongside social differentiation across functional zones. Cows showed preferences for specific areas of the non-feeding zones, more so on the commercial farm in Essex than on the RVC Research farm. Cows increased their bunching behaviour ≥ 20°C in terms of all bunching metrics on the commercial farm in Essex. This pattern was observed for nearest neighbour distance on the RVC Research farm ≥ 15.91°C. This thesis demonstrates the use of precision livestock farming to monitor changes in group-level behaviour to improve the health and welfare of livestock

Validating a Proposed Data Mining Approach (SLDM) for Motion Wearable Sensors to Detect the Early Signs of Lameness in Sheep

Lameness can be described as painful erratic movements, which relate to a locomotor system and result in the animal deviating from its normal gait or posture. Lameness is considered one of the major health and welfare concerns for the sheep industry in the UK that leads to a substantial economic problem and causes a reduction in overall farm productivity. According to a report in 2013 by ADAS entitled ‘Economic Impact of Health and Welfare Issues in Beef, Cattle and Sheep in England’, each lame ewe costs £89.80 due to the decline in body condition, lambing percentage, growth rate, and reduced fertility. Thus, early lameness detection eliminates the negative impact of lameness and increase the chance of favourable outcome from treatment. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal behaviours or movements which relate to lameness. The aim of this thesis was to evaluate the feasibility and accessibility of a proposed data mining approach (SLDM) to detect the early signs of lameness in sheep via analysing the retrieved data from a mounted wearable motion sensor within a sheep’s neck collar through investigating the most cost effective factors that contribute to lameness detection within the whole data mining process including; sensor sampling rate, segmentation methods, window size, extracted features, feature selection methods, and applicable classification algorithm. Three classes are recognised for sheep while their walking throughout the data collection process (sound, mild, and severe lameness classes). The sheep data were collected using three different sensor applications (Sheep Tracker, SensoDuino, SensorLog) which collect sheep data movements at different sampling rates 10, 5, and 4 Hz. Various sensing data were retrieved in X,Y, and Z dimensions; however, only accelerometer, gyroscope, and orientation readings are considered in the current study. Four sheep datasets are aggregated each of which includes 31, 10, 18, and 7 sheep. The conducted work in this thesis evaluates the performance of ensemble classifiers (Bagging, Boosting, or RusBoosting) using three different validation methods (5-fold, 0.3 hold-out, and proposed one ‘Single Sheep Splitting’) in comparison to three sampling rates (10, 5, 4 Hz), two segmentation approaches (FNSW and FOSW), three feature selection methods (ReliefF, GA, and RF) and three window sizes (10, 7, 5 sec.). Promising results of lameness prediction accuracies are achieved over most of the combinations (3 sampling rates, two segmentation methods, 3 window sizes, 183 extracted features, 3 feature selection methods, 3 ensemble classification models, and 3 model validation methods). However, the highest accuracy is revealed by using the `Bagging ensemble classifier 88.92% with F-score of 87.7%, 91.1%, 88.2% for sound walking, mildly walking, and severely walking classes, respectively. The results are obtained using 5-fold cross-validation over a 10 sec.window for sheep data collected at 10 Hz sampling rate using only the accelerometer hardware sensor reading and calculated orientation readings. The number of features selected is 46 optimised by GA using CHAID tree as a fitness function. Conversely, the lowest prediction accuracy of 56.25% with F-score (63.4% sound walking, 51.9% mildly walking, 48.8% severely walking) is recorded when RusBoosting ensemble is applied using 5-fold cross-validation over a 10 sec.window for dataset collected at the 4 Hz. sampling rate. So, the major research findings recommend that 10 Hz sampling rate is adequate for collect sheep movements, while the best segmentation method is FOSW as 20% of data-points are shared between two successive windows. Whereas, the preferable number of data-points (sheep movements) to be pre-processed is around 100, which is obtained when a 10 sec.window size or 7 sec.window size is applied. Additionally, the 20 features selected by RF out of 183 features could reveal good accuracy results compared to the whole set of extracted features. Although that GA feature selection method has slower execution time than RF, competitive prediction accuracy could be achieved when the selected features by GA were fed to the classifier. Finally, the acceleration sensor data alone are capable of making the decision about the lame sheep. So no extra hardware sensors like Gyroscope is required for decision making; moreover, the orientation sensor features could be directly derived from Acc which contribute most to lameness detection. Since the most cost effective factors are identified in this research, the practice in the meanwhile could be applicable for farmers, stakeholders, and manufacturers as no available sensor to detect the lame sheep developed yet. Therefore, the multidisciplinary nature of the conducted research opens diverse paths towards applying further research studies to develop various data mining approaches and practical sensor kits to detect the early signs of sheep’s lameness for better farm productivity and sheep industry prosperity in the UK