# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 3 Related Work  
Due to the major impact a disease or pest can have on the yield of a crop, numerous systems have been proposed and tested in literature in an attempt to halt the spread of the pathogens [15]. Since preventing a disease from appearing is impossible due to the uncontrollable conditions in the environment, the focus has been placed on early detection of a disease before it manifests into an epidemic. In an attempt to counteract this growing problem, systems that both utilize drones and do not utilize drones have been developed [16].  
[PERSON] et al. [17] were able to determine that if a plant was infected with downy mildew, infected leaves would fluoresce violet-blue under an Ultra-Violet (UV) light. Tests performed showed that they were able to design a portable sensor that was able to successfully detect the presence of downy mildew in a plant in one day after the point of infection. The sensor was completely non-invasive causing no harm to the plant while providing results immediately. Systems that have focused on utilizing drones have had great success in determining the symptoms of diseases. [PERSON] et al. [18] used a drone equipped with a camera to take multi-spectral images. The images could then be used to determine several vegetation parameters of the field. The use of the images was able to determine vines showing symptoms of esca disease that human scouts in the field missed. In addition, previous seasons' weather conditions were used in estimating the likelihood of a disease occurring. While able to detect early stages of the disease, images produced by the drone were not able to confirm if a disease was present in the plant. Another drone-based system for agricultural use is presented in [19]. They designed a system composed of two sensors, a stereo vision and thermal imaging camera which are attached to a drone. Images taken by the drone were processed using a deep neural network that was able to classify poor crops from healthy ones. When tested in a vineyard, the system was correctly able to detect diseases such as downy mildew, powdery mildew, and acid rot, but only when the occurrence was above a threshold in the monitoring area. Testing also showed that the system was unable to function if the lighting conditions were not optimal for the sensors.  
There are also systems that use sensors for smart farming and viticulture. Several aspects of sustainable IoT devices, including the wireless communication, sensing, and systems are presented in [20]. In [21], a low-cost wireless system for agrochemical dosage reduction in precision farming was introduced, while in [22] a low-cost wireless monitoring and decision support system for water saving in agriculture was proposed. A comprehensive review of computer vision, image processing, and machine learning techniques in viticulture is presented in [23]. In [24], a wireless underground sensor networks (WUSA) for autonomous precision agriculture (PA) is proposed. The system gathers soil information, from a WUSN in real-time to automatically control the center pivot (CP) irrigation system for precision irrigation.  
In comparison with the works in the literature, the novelty of this work lies in the framework and the system implementation. The introduced approach uses commercially available components, design a real-time monitoring system for vineyards, properly program and configure the different components and finally examine the feasibility of the system through real experimentation.