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Internet of Things (IoT) is a concept that allows physical objects with computational and sensory support to connect with each other and access services across the Internet. The IoT idea was introduced to connect devices through the Internet and facilitate access to information for users. The wide range of potential applications of IoT also includes agriculture, where extensive use of IoT is expected in the future. The aim of this work was to present the IoT concept as a basis for monitoring and control systems used in farm production processes. IoT devices play a key role, with a focus on their realization by available microcontroller platforms and appropriate sensors such as Arduino products. Autonomous sensor devices gather data within monitoring systems and participate in the control process by sending signals to the actuators. Such an IoT based system provides users with the opportunity to remotely monitor conditions and production process. This system enables users to accomplish savings in inputs, achieve cost reduction and trace the production process on the farm.

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No abstract

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.