Towards Agricultural Technology: A Literature Review on the Application of Artificial Intelligence, and Geospatial Machine Learning in Digital Agriculture

Due to the increasing population of the world, the limitation of water resources and fertile agricultural lands, and the changing essence of nature and climate patterns such as precipitation systems and temperature cycles, it seems that in the near future, all people will face severe food crises. Traditional agriculture, regarding its reliance on traditional knowledge and lack of access to up-to-date databases, cannot provide practical solutions for the future growth of food demand. These days, international food andagriculture organizations across the world have focused on Digital Agriculture as the future food systems, and Artificial Intelligence and Machine Learning have become critical areas in modern agricultural technology. This research, by reviewing and considering the recent international research in the related field and using analytical methods, tries to investigate the role of Artificial Intelligence and Machine Learning in the field of Digital Agriculture. Studies demonstrate that the application of Digital Agriculture,based on digital-spatial technologies, can be effective in various agricultural areas such as resource management and environmental conditions improvement. Indeed, Digital Agriculture can develop efficient and practical solutions to strengthen the food industry. Besides, Digital Agriculture, with its ability to investigate and predict potential threats, can help to reduce the risks of unintentional decisions due to lack of access to new knowledge and up-to-date information by traditional farmers. Due to the dynamic nature of agriculture, as a complex dynamic system, various variable parameters (such as geological and geographical location, climatic conditions and environmental pollution, etc.) can affect the process of agricultural activities. Artificial Intelligence and Machine Learning by using the power of Simulating Human Intelligence and the ability to enhance their learning capacity and improve their analysis based on the sophisticated computational algorithms can enhance the sustainable development of Digital Agriculture.

Keywords:

Agricultural Technology , Digital Agriculture , Artificial Intelligence , Geospatial- Machine Learning