Affiliation:
1. The Bucharest University of Economic Studies , Romania
Abstract
Abstract
The only technology that is currently mature enough to help us quickly recover losses in agriculture is digital technology. Digital transformation is increasingly referred to as the fourth industrial revolution, aimed at merging the real world with the virtual one, bringing advantages even in traditional sectors such as agriculture. Artificial intelligence (AI) algorithms are used to analyze large amounts of data collected from various sources in the field, as well as from farmers or beneficiaries of European funds for agricultural sector development. Specifically, they can analyze information gathered from soil sensors, weather stations, satellite images, or European funding applications, among others. This analysis provides farmers with valuable information to make informed decisions regarding seed selection, optimal timing, planting techniques, and resource allocation decisions. At the same time, AI-powered systems can learn from historical data and identify patterns, enabling more accurate predictions and recommendations. Machine learning algorithms can anticipate managerial decisions, detect potential problems, and suggest appropriate interventions to improve financial performance for the successful implementation of projects funded by European funds.
Reference17 articles.
1. AFIR data base
2. Bergerman M., Billingsley J., Reid J., van Henten E. (2016) Robotics in Agriculture and Forestry. In: Siciliano B., Khatib O. (eds) Springer Handbook of Robotics. Springer, Cham, Springer-Verlag Berlin Heidelberg 2016, DOI https://doi.org/10.1007/978-3-319-32552-1_56
3. CECCAR, (2019), Profesia contabilă într-o economie bazată pe globalizare şi digitalizare (I), https://www.ceccarbusinessmagazine.ro/profesiacontabila-intr-o-economie-bazata-peglobalizare-sidigitalizare-i-a5481/
4. Ciro Potena, Daniele Nardi, Alberto Pretto - Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture, Springer International Publishing AG 2017, in W. Chen et al. (eds.), Intelligent Autonomous Systems 14, Advances in Intelligent Systems and Computing 531, DOI 10.1007/978-3-319-48036-7_9
5. Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C. et al. - Fleets of robots for environmentally-safe pest control in agriculture, Precision Agric (2017), 18: 574, https://doi.org/10.1007/s11119-016-9476-3