Strategic implementation of AI IoT and Big Data technology in improving upstream to downstream agricultural efficiency - Yohanes Emanuel (C-DISC, 2025)
This paper examines the role of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data in improving agricultural efficiency from upstream to downstream processes. These technologies enable large-scale data processing, advanced analysis, and automation to optimize agricultural activities, including land preparation, pest control, irrigation, and crop distribution. IoT facilitates real-time data collection through interconnected devices, enhancing monitoring and decision-making without direct human intervention. This study employs a qualitative literature review method by analyzing various relevant sources. The findings indicate that the integration of AI, IoT, and big data significantly enhances agricultural efficiency and productivity through the development of smart farming systems. These systems allow farmers to monitor soil conditions, weather patterns, irrigation levels, and crop health in real time. AI algorithms can predict crop yields, detect early signs of plant diseases, and recommend optimal planting schedules based on historical and real-time data. IoT sensors continuously transmit field data, enabling rapid responses to environmental changes, while big data analytics supports data-driven decision-making by aggregating and interpreting large volumes of information. Beyond increasing productivity, the integration of these technologies also promotes sustainable agricultural practices by optimizing resource use, reducing waste, and minimizing environmental impact. As global food demand continues to rise, the adoption of AI, IoT, and big data is essential to ensure sustainable and efficient agricultural development in the future.