###### Abstract  
Precisely measuring the work area of agriculture farm machinery is important for performing the authentication of machinery usage, better allocation of resources, measuring the effect of machinery usage on the yield, usage billing and driver's behaviour. The manual measurement, which is a common practice is an error-prone and time-consuming process. The irregular fields make it even more difficult to calculate the work area. An automatic solution that uses smart technology and algorithms to precisely calculate the work area is crucial for the advancement of agriculture. In this work, we have developed a smart system that utilizes the Internet of Things (IoT), Global Positioning System (GPS) and Artificial Intelligence (AI) that records the movement of agriculture machinery and use it to measure the precise work area of its usage. The system couples the nearest neighbourhood algorithms with Contact-based mechanisms to find the precise work area for different shaped fields and activities. The system was able to record the movement of machinery and calculate its work area, regardless of how many times the machinery runs through a particular field. Our evaluation shows that the system was able to precisely find the work boundaries and calculate the area with a maximum of 9% error for irregular shapes.  
area estimation; agriculture machinery; artificial intelligence; GPS Article Determining the Precise Work Area of Agriculture Machinery Using Internet of Things and Artificial Intelligence  
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to give us the accurate location of a GPS module. The GPS coordinates include the latitudes and longitudes. The GPS can also be applied towards the agriculture domain to find the work area of agriculture machinery. Automatically finding the work is interesting, but poses several challenges. First, the machinery is often used on the fields that are very irregular in shape. Second, the field may have been covered sparsely. Third, the machinery could be used around the perimeters only to trick the system. Finally, the GPS data itself could have outliers.  
To overcome these issues and precisely calculate the machinery work area, we have developed a mechanism in this paper. We use a smart IoT device to record the movement of agricultural machinery. After filtering the data for outliers, we apply different convent-hull and concave-hull Artificial Intelligence (AI) schemes on the data. The AI algorithms detect the boundary points of work area. On the boundary points, algorithms are applied to calculate the precise work area of agriculture machinery.  
The AI algorithms are good at calculating area of regular and irregular shapes when the work is done uniformly across the field. However, its accuracy reduces when the field is not uniformly covered or the machinery is used around the perimeters only. To overcome this issue, we use the AI in combination with a Contact-based mechanism. The Contact-based scheme uses the distance covered by machinery and width of the machinery to calculate the area, thus helping detect anomalies in the field work.  
The rest of the paper is structured as follows. Section 2 describes the related work in the agriculture domain that utilizes IoT and other technologies to boost agriculture production. Section 3 describes the use of GPS and AI in agriculture. Section 4 discusses the IoT-cloud platform for data collection, storage and processing. Section 5 formulates a methodology to detect the boundary of the work area of machinery. Determining the boundary leads to the shape of the field and work area estimation. Section 6 outlines the methods used for area calculation. Section 7 shows the accuracy and performance evaluations of the schemes for different field shapes. Concluding remarks and future work is presented in Section 8.