Analysis of the Role of Smart Agriculture in Supporting Digital Transformation in the Agricultural Sector
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Keywords: Smart Agriculture, Digital Transformation, Internet of Things, Artificial Intelligence, Agricultural SectorAbstract
Digital transformation in the agricultural sector has become a strategic necessity in addressing global challenges such as climate change, resource constraints, and the demand for increased productivity and efficiency. Smart Agriculture emerges as a technology-based approach that integrates the Internet of Things (IoT), smart sensors, artificial intelligence, and data analysis to support modern agricultural management. This study aims to analyze the role of Smart Agriculture in supporting digital transformation in the agricultural sector and to identify the benefits and challenges of its implementation. The study employs a literature review method with a qualitative approach through the process of identifying, selecting, analyzing, and synthesizing various relevant scientific literature. The results of the analysis show that the application of Smart Agriculture can improve operational efficiency and lead to savings in resource use (water and fertilizer), enhance the accuracy of decision-making through real-time data, and support increased productivity and the quality of agricultural yields. In addition, the integration of technologies such as IoT and AI contributes to monitoring land conditions, predicting crop yields, and the early detection of pests and plant diseases.
However, challenges have been identified, including limited digital infrastructure, low levels of technological literacy among farmers, and high implementation costs. Smart Agriculture has proven to play a significant role in supporting the digital transformation of the agricultural sector, although its success is heavily influenced by the readiness of the technology ecosystem and human resources. Further research is recommended to examine implementation through field case studies and to develop technology adoption models that are better adapted to local conditions.
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