Browsing by Subject "sähkönjohtavuus"

Automatic measurement of temporally varying soil moisture content could result in benefits both in agriculture and other applications. Mainly due to the ease of automation only electrical methods are suitable for measuring the variability of temporal moisture content. Capacitance technology is one of the most suitable methods for this kind of purpose. When determining soil moisture content by dielectricity means one will face two main properties: soil permittivity and soil electrical conductivity. Moisture measurement is based mainly on the changes of soil permittivity. Instead, changes in the soil electrical conductivity typically hamper the accuracy of moisture measurements. The measurement accuracy can be improved, however, by increasing the measurement frequency. The aim of this study was to determine the impact of sensor’s physical geometry and measuring frequency to the reliability of measurement. Combination of two measurement frequencies and an additional measurement of soil resistance were also determined for improving the accuracy. This study was carried out as part of the SAFETOOL project which aims were to monitor and to develop measurement tools for measuring and monitoring the status of the environment in field conditions. The sampling volume of capacitive sensors was examined in both field and laboratory conditions. To stabilize the weather moisture measurements were done only in laboratory conditions. The study was divided into two parts: pre-study and main-study. During the main-study five different capacitive geometries and the measurement of soil resistance were examined. Capacitance was measured using three frequencies: 5,5, 70 and 95 MHz. A commercial EC-5 capacitance sensor (Decagon Devices, Pullman, WA, USA) was used as control. Measurements were carried out in containers of volume about 3 litres filled with fine sand. Moisture measurements were conducted with volumetric water contents of 5 %, 15 %, 25 % and 35 % while the electrical conductivity varied between 2 and 24. Changes in the physical geometry did not result in remarkable differences of accuracy. The measurement volumes were also similar within the variable geometries. The impact of changes in the electrical conductivity, or salinity, however, was significant. Measuring the resistance of the soil lead typically to better results. However, this could be due to relatively high soil electrical conductivity. While working within the framework of normal field conditions and the right calibration one might expect to get reliable moisture measurements with a capacitance moisture sensor.

Mastitis is economically the most important disease and the second most important welfare issue after lameness in dairy production worldwide. Mastitis diagnosis consists of recognizing the causative pathogen and simultaneous changes in milk parameters, such as somatic cell count. Currently, 27 % of Finnish farms use automatic milking system (AMS) and more than 50 % of all milk is harvested by a milking robot. Large amounts of data are available from AMS, and they can be used to recognize and control mastitis on farms. The aim of this work was to study how different AMS data patterns describe mammary gland infection, and how they can be used in mastitis diagnosis. The most conventional parameter for diagnosing mastitis is somatic cell count (SCC) which describes the number of somatic cells per milliliter of milk. During mastitis, SCC increases, but a significant day-to-day variation is characteristic. SCC is measured in official Dairy Herd Improvement (DHI) programs, and SCC is also counted by sensors in AMS. The most common in-line measured parameter at AMS is electrical conductivity (EC). EC is measured quarter-specifically which makes it good for comparison among different quarters but there are some uncertainties associated with EC. In addition, L-lactate dehydrogenase (LDH) is an enzyme that indicates infection in different tissues and is also detectable with a sensor in some AMS. It’s less mastitis-specific than SCC, but because it has less daily variation, combined with SCC it’s currently an interesting tool for recognizing mastitis in AMS. Descriptive study was conducted using AMS data from 24 cows over 7 months from a Canadian research herd. The data were fragmented and only a few mastitis cases were included. However, the results describe the characteristics of different AMS parameters. Results showed that LDH is high especially in 1st lactation cows until 35 days after calving. As expected, LDH of mastitic cows was substantially higher compared to cows that were healthy or had non-udder illness. Interestingly, the daily variation of LDH in individual cows appears to be greater than expected.