Wireless MEMS sensors for precision farming

Bowen

Michie

et al. 2021

Animal

Self Cite

Current techniques for measuring feed intake in housed cattle are both expensive and time-consuming making them unsuitable for use on commercial farms. Estimates of individual animal intake are required for assessing production efficiency. The aim of this study was to predict individual animal intake using parameters that can be easily obtained on commercial farms including feeding behaviour, liveweight and age. In total, 80 steers were used, and each steer was allocated to one of two diets (40 per diet) which consisted of (g/kg; DM) forage to concentrate ratios of either 494:506 ( MIXED ) or 80:920 ( CONC ). Individual daily fresh weight intakes ( FWI ; kg/day) were recorded for each animal using 32 electronic feeders over a 56-day period, and individual DM intakes ( DMI ; kg/day) subsequently calculated. Individual feeding behaviour variables were calculated for each day of the measurement period from the electronic feeders and included: total number of visits to the feeder, total time spent at the feeder ( TOTFEEDTIME ), total time where feed was consumed ( TIMEWITHFEED ) and average length of time during each visit to the feeder. These feeding behaviour variables were chosen due to ease of obtaining from accelerometers. Four modelling techniques to predict individual animal intake were examined, based on (i) individual animal TOTFEEDTIME relative expressed as a proportion of the dietary group ( GRP ) and total GRP intake, (ii) multiple linear regression ( REG ) (iii) random forests ( RF ) and (iv) support vector regressor ( SVR ). Each model was used to predict CONC and MIXED diets separately, giving eight prediction models, (i) GRP_CONC, (ii) GRP_MIXED, (iii) REG_CONC, (iv) REG_MIXED, (v) RF_CONC, (vi) RF_MIXED, (vii) SVR_CONC and (viii) SVR_MIXED. Each model was tested on FWI and DMI. Model performance was assessed using repeated measures correlations ( R 2 _RM) to capture the repeated nature of daily intakes compared with standard R 2 , RMSE and mean absolute error ( MAE ). REG, RF and SVR models predicted FWI with R 2 _RM = 0.1–0.36, RMSE = 1.51–2.96 kg and MAE = 1.19–2.49 kg, and DMI with R 2 _RM = 0.13–0.19, RMSE = 1.15–1.61 kg and MAE = 0.9–1.28 kg. The GRP models predicted FWI with R 2 _RM = 0.42–0.49, RMSE = 2.76–3.88 kg and MAE = 2.46–3.47 kg, and DMI with R 2 _RM = 0.32–0.44, RMSE = 0.32–0.44 kg, MAE = 1.55–2.22 kg. Whilst more simplistic GRP models showed higher R 2 _RM than regression and machine learning techniques, these models had larger errors, likely due to individual feeding patterns not b...

Section: Discussionmentioning

confidence: 99%

Predicting feed intake using modelling based on feeding behaviour in finishing beef steers

Bowen

Michie

et al. 2021

Animal

Self Cite

“…Alternatively, data can be pre-processed into summaries that describe the dominant signal characteristics over a period of time such that this information can be subsequently processed. In this manner substantial savings in radio transmission power consumption are obtainable and battery lifetimes of 5–10 years are common (Michie et al ., 2017). Information generated in this way can then be processed on a central farm computer or in the cloud to represent the measurement data in a manner that is meaningful to the herdsman to support their daily decision making.…”

Section: Oestrus Detection In Dairy Cattlementioning

confidence: 99%

“…It is well known that cattle in heat (oestrus) become restless (Kiddy, 1977; Van Vliet and Van Eerdenburg, 1996) hence machine learning or statistical approaches can be used to identify outlier behaviour that aligns with the onset of heat (Eradus et al , 1996; Martiskainen et al ., 2009). Measurement of this change in activity is readily achieved using MEMs accelerometers (Pastell et al ., 2009; Robert et al ., 2009; Michie et al ., 2017), however, it is not possible to transmit all of the unprocessed accelerometer data which may operate in three-axes with a sample frequency of 10 Hz or more. This is because the low power wireless transmission channels do not have sufficient bandwidth.…”

Section: Oestrus Detection In Dairy Cattlementioning

confidence: 99%

“…The steady rhythm allows rumination to be identified from the motion of the neck muscles hence facilitating the use of accelerometers to detect and time this behaviour. The process of extracting the rumination information is not the subject of the present paper but for illustration the accelerometer output shown in online Supplementary Figure S1 shows an example of a rumination sequence measured using an accelerometer collar and verified by comparison with a pressure sensitive halter monitoring jaw action (Michie et al ., 2017). Although the Signal-to-Noise Ratio (SNR) of the accelerometer signal is lower than the pressure data, the same features (periodic motion followed by a cessation of activity) are apparent.…”

Section: Automated Measurement Of Animal Welfarementioning

confidence: 99%

See 1 more Smart Citation

The Internet of Things enhancing animal welfare and farm operational efficiency

Michie

Andonović

Journal of Dairy Research

et al. 2020

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

“…Despite these trends, the overall milk production in the UK has remained relatively constant or increased (13,319 million litres in 2008 versus 14,829 million litres in 2015) [ 2 ]. Operational scale and genetic gain are responsible in large part for this improved efficiency, but the implementation of different agritech sensing systems has given farmers greater insights into cattle fertility and health which, in turn, has increased milk yields [ 3 , 4 , 5 , 6 ]. This is underlined by the growth in sales of collars and pedometers for the detection of oestrus (or “heat”) to optimise herd reproductive efficiency [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of the Rumination in Cattle Using Support Vector Machines with Motion-Sensitive Bolus Sensors

Hamilton

Tachtatzis

et al. 2019

Sensors

The reticuloruminal function is central to the digestive efficiency in ruminants. For cattle, collar- and ear tag-based accelerometer monitors have been developed to assess the time spent ruminating on an individual animal. Cattle that are ill feed less and so ruminate less, thus, the estimation of the time spent ruminating provides insights into the health of individual animals. pH boluses directly provide information on the reticuloruminal function within the rumen and extended (three hours or more) periods during which the ruminal pH value remains below 5.6 is an indicator that dysfunction and poor welfare are likely. Accelerometers, incorporated into the pH boluses, have been used to indicate changes in behaviour patterns (high/low activity), utilised to detect the onset of oestrus. The paper demonstrates for the first time that by processing the reticuloruminal motion, it is possible to recover rumination periods. Reticuloruminal motion energy and the time between reticuloruminal contractions are used as inputs to a Support Vector Machine (SVM) to identify rumination periods with an overall accuracy of 86.1%, corroborated by neck mounted rumination collars.