AI-Powered Agriculture: A New Era of Smart Farming
Keywords:
Artificial Intelligence, digital agriculture, farm automation, precision farming, sustainable agricultureAbstract
Digital agriculture and Artificial Intelligence (AI) are developing as game-changing technologies in modern farming systems. The integration of digital technologies such as sensors, drones, mobile applications, satellite imaging, Internet of Things (IoT), robotics and AI-based decision support systems is improving agricultural productivity, resource use efficiency, and sustainability. AI technologies help farmers in various activities including precision farming, smart irrigation, pest and disease identification, weather forecasting, yield estimation and overall farm management. These technologies help farmers make better decisions, reduce production costs, minimize environmental impacts and improve crop yields. Digital agriculture also supports climate-resilient and sustainable farming practices. However, high costs, limited digital knowledge, poor internet access and lack of technical skills restrict its wider adoption especially in developing countries. Despite these challenges, technological advancements and government support are promoting the growth of smart agriculture worldwide.
References
FAO. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. Food and Agriculture Organization.
Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
Singh, A. K., Goyal, R. K., & Sharma, P. K. (2020). Digital agriculture for sustainable crop production. Indian Farming, 70(4), 12–16.
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture: Recent advances and future challenges. Biosystems Engineering, 164, 31–48.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80.
Zhang, Q., & Pierce, F. J. (2013). Agricultural automation: Fundamentals and practices. CRC Press.
