Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

Year 2024, Volume: 7 Issue: 6, 646 - 656, 15.11.2024

Hatice Dilaver , Kamil Fatih Dilaver

https://doi.org/10.47115/bsagriculture.1536744

Abstract

The efficient and sustainable operation of the agricultural sector has become increasingly important in light of the transformations brought about by the third and fourth industrial revolutions. Population growth, increasing food demand, rising input costs, and environmental pressures necessitate innovative approaches not only to ensure food security but also to mitigate the effects of climate change. The European Union (EU) emphasizes the role of digital technologies in supporting agricultural productivity and resilience by promoting a bio-based economy. Strategies such as Farm to Fork (F2F) initiative aim to reduce pesticide and nutrient inputs, thus preserving biodiversity and supporting ecosystem health. Artificial intelligence (AI) and predictive analytics, along with connected sensors, offer opportunities to optimize water and nutrient usage and increase crop yields. By utilizing AI, combining remote sensing technologies, and monitoring changes in land use, it is possible to reduce environmental risks associated with agricultural practices. Although there are challenges such as high investment costs and data control for the integration of digital technologies, ongoing research and development efforts promise to overcome these obstacles. In conclusion, the integration of digital technologies into agriculture presents unique opportunities to address urgent issues and achieve sustainability goals. This review discusses the applicability of fundamental technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture (PA), and Machine Learning (ML) in making agriculture more efficient and sustainable, by enabling the perception, monitoring, collection, analysis, and extraction of meaningful insights from agricultural data.

Keywords

Artificial Intelligence, Machine learning, Internet of things, Agricultural remote sensing, Precision agriculture

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