The Use of Infrared Thermography fort he Monitoring of Udder Teat Stress Caused by Milking Machines

The aim of this study was to test infrared thermography (IRT) as a possible tool for scoring teat color changes after cluster removal; thus, indirectly, to classify the short-term stress of teats caused by milking machines. Thermographic images (n = 137) from three farms were collected and evaluated to calculate the average and maximum skin surface temperatures (SSTs) at the base, center, and tip of each teat (Tavg,B, Tavg,C, Tavg,T, Tmax,B, Tmax,C, and Tmax,T). Obtained results confirmed a significant relationship between the indicators Tavg, Tmax and the levels of teat color change (level one: pink-colored teat; level two: red-colored teat; level three: blue or purple-colored teat). Nevertheless, when a teat was considered to be stressed because its scoring fell in level 3 of the color-change scale used, sensitivity and specificity in the classification of the teat status ranged respectively between 45.6% and 54.3%, and 54.4% and 59.2%, for the indicators Tavg; and 56.5% and 60.9%, and 59.7% and 61.8%, for the indicators Tmax. When a teat was considered stressed because its scoring fell between the levels 2 and 3 of the scale adopted, sensitivity and specificity were between 49.0% and 55.8%, and 58.3% and 61.8%, for the indicators Tavg; and 55.8% and 59.9%, and 60.6% and 61.4%, for the indicators Tmax. As a consequence, the low values of sensitivity and specificity do not seem to justify the development of an ad hoc infrared device for the monitoring of udder teat stress. Nonetheless, this technology can be a viable solution for a preliminary evaluation of the mechanical stress of teats if a milking system would be equipped with an infrared sensor already in place for other purposes (e.g., the monitoring of udder health status)

Meccanica agraria e zootecnia: mungitura robotizzata, analisi dei consumi energetici

Negli ultimi trent\u2019anni le produzioni zootecniche sono state caratterizzate da un intenso sviluppo tecnologico al quale la Meccanica Agraria ha contribuito fortemente giocando un ruolo fondamentale nell\u2019incrementare la produttivit\ue0 del lavoro, garantire e migliorare il benessere animale, assicurare un\u2019intensificazione sostenibile dei processi produttivi. L\u2019automazione delle operazioni di stalla e in particolare della mungitura, che rappresenta la fase pi\uf9 onerosa in termini economici e fisici, e per la quale non pu\uf2 ancora ritenersi concluso il processo evolutivo, \ue8 probabilmente l\u2019ambito in cui il Meccanico Agrario che opera in contesti zootecnici \ue8 maggiormente coinvolto. Ad oggi nel mondo sono installati circa 45.000 sistemi automatici di mungitura o AMS (Automatic Milking System), di cui oltre 700 in Italia, e questo numero \ue8 destinato a crescere nei prossimi anni nel contesto di una generale tendenza all\u2019automazione della stalla da latte, aumentando il consumo energetico relativo alla mungitura robotizzata. L\u2019energia utilizzata da un AMS dipende da molti fattori, tra cui la generazione di appartenenza, le configurazioni e le impostazioni della macchina, nonch\ue9 le condizioni operative. Il lavoro qui presentato ha avuto come scopo quello di misurare e analizzare il consumo elettrico di due generazioni successive di AMS installati in aziende da latte del nord Italia, caratterizzate da contesti operativi diversi. La prova sperimentale \ue8 stata condotta su quattro AMS con differenti configurazioni (stallo singolo, unit\ue0 centrale con una o due unit\ue0 di mungitura). Il consumo elettrico (giornaliero, giornaliero per bovina munta, per mungitura, per 100 litri di latte) di ogni AMS (unit\ue0 di mungitura e compressore dell\u2019aria) \ue8 stato misurato utilizzando due multimetri collegati ai quadri elettrici rispettivamente dell\u2019AMS e del compressore dell\u2019aria. Il periodo di misura \ue8 stato di 24 h per ciascun sistema, utilizzando una frequenza di campionamento di 0,2 Hz. Il consumo elettrico degli AMS \ue8 risultato condizionato principalmente dalla gestione aziendale piuttosto che dalle caratteristiche e architetture delle singole macchine

On-line monitoring of milk electrical conductivity by fuzzy logic technology to characterise health status in dairy goats

Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology \u2013 which provided interesting results in cows \u2013 was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings

Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens

Conventional cage systems will be replaced by housing systems that allow hens to move freely. These systems may improve hens' welfare, but they lead to some disadvantages: disease, bone fractures, cannibalism, piling and lower egg production. New selection criteria for existing commercial strains should be identified considering individual data about laying performance and the behavior of hens. Many recording systems have been developed to collect these data. However, the management of double nest occupations remains critical for the correct egg-to-hen assignment. To limit such events, most systems adopt specific trap devices and additional mechanical components. Others, instead, only prevent these occurrences by narrowing the nest, without any detection and management. The aim of this study was to develop and test a nest usage "sensor", based on imaging analysis, that is able to automatically detect a double nest occupation. Results showed that the developed sensor correctly identified the double nest occupation occurrences. Therefore, the imaging analysis resulted in being a useful solution that could simplify the nest construction for this type of recording system, allowing the collection of more precise and accurate data, since double nest occupations would be managed and the normal laying behavior of hens would not be discouraged by the presence of the trap devices

Evaluation of the fourier frequency spectrum peaks of milk electrical conductivity signals as indexes to monitor the dairy goats’ health status by on-line sensors

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0\u201360 Days In Milking (DIM); 61\u2013120 DIM; 121\u2013180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS 7 HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland\u2019s health status. Results also explained how the milk EC signals\u2019 pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors

Improved fuzzy logic system to evaluate milk electrical conductivity signals from on-line sensors to monitor dairy goat mastitis

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0\u201360 Days In Milking (DIM); 61\u2013120 DIM; 121\u2013180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS 7 HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors

Evaluating an e-nose ability to detect biogas plant efficiency: a case study

The demand for online monitoring and control of biogas process is increasing, since better monitoring and control system can improve process plants stability and economy. A number of parameters in both the liquid and the gas phase have been suggested as process indicators (pH, alkalinity, VFA and H-2, redox potential, biogas production rate, biogas composition, FOS/TAC ratio, COD and/or VS reduction). The present study proposes the use of complex sensors as a possible solution to engineer a consistent control system. Tests were performed to analyze the biogas coming from a biogas plant in which conditions of pre-overloading and overloading were artificially inducted. The different inducted functioning conditions were recognised using a FOS/TAC tester, or the ratio volatile organic acids/alkaline buffer capacity. The FOS/TAC ratio has long been recognized extending as far as the imminent inversion of the digester biology to be detected at an early stage. Data coming from the e-nose were sorted and classified according to FOS/TAC ratio as a reference method. Not all the sensors of the e-nose were reactive to manure digestates, but four of them, sensitive to aromatic compounds, ammonia alkanes and methane, resulted crucial in the samples identification. Results confirmed that the e-nose can discriminate different digestion conditions, demonstrating the possibility to reduce the number of sensors in this innovative tool for biogas control systems. However, this instrument cannot be considered a complete alternative to traditional analysis systems, as, for example, the FOS/TAC titration, but a supporting tool for a quick analysis of the system

Heavy metals on honeybees indicate their concentration in the atmosphere : a proof of concept

The aim of this research was to evaluate as a proof of concept, a quantitative relationship between atmospheric particulate matter (PM10), atmospheric heavy metals (Pb, Ni and Cd) and Pb, Ni and Cd concentrations accumulated on bees reared in a beehive in the urban territory in Milan. For this purpose, a beehive called the Honey Factory, located in the Triennale museum area in Milano, was considered in the trial from May to October 2015. Every two days, bees found dead in the beehive were collected and the concentration of lead, cadmium and nickel on the bees bodies were evaluated through atomic absorption analysis. In the same period, data about atmospheric dust, Lead, Cadmium and Nickel, were daily downloaded by the ARPA website. The comparison between atmospheric and animal data has revealed a tight relation. Linear regressions for animals and atmosphere were calculated: when the concentration of atmospheric lead exceeded the value of 4 ng/m3, bees \u2018carried\u2019 about 0.7 mg/kg of lead. When the lead atmospheric concentration was higher than 15 ng/m3, lead on the bee\u2019s body was more than 0.9 mg/kg (y = 0.1006x + 0.573, R2 = 0.98). A similar relationship was detected for Nickel. This study showed that heavy metals accumulated on honeybees depend on the atmospheric concentrations measured during the month before animal sampling and that PM10 pollution level seems to contribute to Pb and Ni levels detected on the animals.Highlights Bees are environmental quality indicators. Pb, Ni, Cd (HM) were detected on bees and in the air. HM on bees depend on the atmospheric concentration