Son yıllarda ucuzlayan bazı toprak nem sensörlerinin kullanımı Arduino mikroişlemci kontrol kartları sayesinde giderek artmaktadır. Buradan hareketle çalışma kapsamında, oldukça ekonomik olan 3 farklı toprak nem sensörünün son yıllarda sıklıkla kullanılan Arduino mikroişlemci kontrol kartıyla toprak nemini belirlemede kullanılabilirliği araştırılmıştır. Bu amaçla yapılan çalışmada 4 farklı tipte sensör ele alınmıştır. Bu sensörlerden biri kontrol sensörü (10HS, DECAGON) olarak kullanılmış, diğerleri de Arduino toprak nem sensörü, gravity analog toprak nem sensörü ve kapasitif toprak nem sensörü olarak bilinen ve sıklıkla kullanılan sensörlerden seçilmiştir. Bu sensörlerin, topraklı (orta bünye) ve topraksız (1/1 oranında torf+perlit) olmak üzere iki farklı yetiştirme ortamında toprak nemine karşı tepkileri belirlenmiştir. Araştırma kapsamında elde edilen sonuçlara göre performansı test edilen 3 farklı toprak nem sensörünün özellikle topraksız ortamda doğru okuma yapamadığı tespit edilmiştir. Ayrıca toprak nemini algılama konusunda da çok değişken sonuçlar elde edilmiştir. Oksitlenmeden dolayı ekonomik ömürlerinin çok kısa olduğu anlaşılmıştır. Elde edilen sonuçlara göre bu nem sensörlerinin kullanımı konusunda dikkatli olunması gerektiği söylenebilir.

Flexibility sensors are used to measure bending response of flexible materials which are employed in different technologies. They can be produced with easily available and low-cost materials. Its compactness, lightness and low power consumption makes this sensor ideal for manifold as well as any applications needs to monitor changes in shape or bending behavior. In this paper essential steps needed to design a custom-made, longer and cost effective flex sensor are discussed. It was found that selection of resistor, temperature dependency, and maximum possible length are major criteria to be considered. The best resistor yields the widest range was determined to be 100 Ω with maximum length of 75 cm. Another important finding of the study was the need of temperature compensation.

Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station device using temperature, relative humidity, UV, LDR Light, rain and soil moisture sensors to collect major environmental data. The measured data were wirelessly transmitted to the remote station for logging via the GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.

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Welfare and production efficiency of livestock, especially dairy cattle, in a barn are closely related with environmental factors such as temperature, humidity, etc. Therefore, the aim of this study is to design a low-cost automation device that is based on Temperature Humidity Index (THI). An Arduino microprocessor and associated sensors/electronics were used to design a prototype. The device collects, process and stores temperature, humidity and THI data in a minute interval for automation and long term management purposes. It is capable of estimating and storing theoretical daily reduction in milk production. Average actual daily milk production can also be entered to the system. The cost of the prototype was $ 238 that makes it affordable for low-income operations. Data was collected for a 6month-period to test the performance of the prototype. Totally 1.4 megabyte of capacity is required for data storage. That makes the system affordable and easy to manage the data. The device was installed on a post in the middle of barn. It is found that below the lower limits of mild heat stress category (THI<83) total of 80 Simmental milking cows were not influenced from heat stress as confirmed by literature.