AI-Powered Agricultural Equipment: Enhancing Precision Farming Through Big Data and Cloud Computing

Abstract

Precision agriculture is becoming more important because it can improve farm yields and efficiency by using new technologies. These innovations are based on the collection of big data and outputs from agricultural equipment at the farm level. Agricultural equipment includes machines that help farmers with farming tasks, such as tractors, harvesters, and seeders. For example, GPS units mounted on tractors can collect georeferenced data every second while working in fields, such as field location and speed. Agricultural machinery systems are used to accomplish a certain farming task in a certain area within a predefined period of time. Big data obtained from agricultural machines are in the shape of time series inputs and provide a spatial-temporal understanding of farming areas. An increasing amount of research and advanced pilot projects are developing agriculture data analysis and sharing services, which will create business opportunities for information service providers and research on service method improvement. Business-oriented services are intended to be delivered by manufacturers or their partners. Farmers usually have the need or will to adopt machinery but tend not to be able to afford the dear price. The services to be provided are better suited for cooperative sharing. The business model will fit with domain horizontal partnerships, information cooperation and data open, for which analysis results should be retained within data providers. Thus cross-domain common efforts are required.

Outcomes of precision farming could be improved quality of crop production, reduced cost of production, and increased total yield. In addition, characteristics of modern agricultural development such as land increasing, multi-functional farm and one family managing many farms should also be taken into account. The results on the needs of precision farming indicated that simple decision farming, monitoring crop environments such as herbicidal status, temperature and moisture fields, and machine tracking. In addition, professional services, such as variable fertilization prescriptions and process recommendation, should also be developed. The future services from big data-acquiring agricultural machinery systems towards professional grower were also discussed. Cloud computing, high-speed internet, and social media have provided a carrier for data sharing across disciplines. Closed user group applications on precision farming were developed in cooperation with a tractor manufacturer. Precision agriculture has been made possible with the development of satellite imaging, GPS, GIS, and other related technologies which can measure field variability and therefore prescribe site-specific management as a means to enhance crop yield and reduce production cost.