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The integration of Smart Sensor Networks (SSNs) with Artificial Intelligence (AI) has revolutionized precision agriculture, offering unprecedented capabilities for microclimate monitoring and agroecological forecasting. This chapter explores the application of these technologies in optimizing crop management, enhancing soil health, and improving overall farm productivity. By leveraging real-time data from sensor networks, AI-driven predictive models enable farmers to make data-driven decisions regarding crop rotation, fertilization, irrigation, and pest management. Furthermore, the combination of remote sensing technologies with AI enhances forecasting accuracy, providing insights into environmental variables and enabling proactive agricultural practices. The chapter discusses key advancements in cloud-based data analytics for large-scale agroecological forecasting, addressing the challenges and potential solutions in scaling these technologies for widespread agricultural use. Additionally, the role of AI in modeling microclimate interactions and their effects on crop health is analyzed, highlighting the potential for adaptive farming systems that respond dynamically to changing environmental conditions. As the agricultural sector faces increasing pressure to meet global food demands while minimizing environmental impact, the integration of SSNs and AI offers a sustainable path forward for smart, efficient farming. The future of agroecological systems hinges on these technologies, which promise to transform agricultural practices into more resilient, resource-efficient, and environmentally friendly processes.
Agriculture is undergoing a significant transformation due to advancements in technology [1]. Among the most promising innovations are Smart Sensor Networks (SSNs) and Artificial Intelligence (AI), which together have the potential to revolutionize how agricultural systems monitor, manage, and optimize environmental variables [2]. Traditional farming practices often rely on general knowledge and human intuition, but the incorporation of SSNs and AI shifts this approach to data-driven decision-making [3]. By deploying sensors across agricultural fields, real-time data is gathered on a variety of microclimate factors, including soil moisture, temperature, humidity, and light intensity [4]. These sensors provide a detailed, localized view of environmental conditions that influence crop health, allowing farmers to make timely, informed decisions. When coupled with AI, the wealth of sensor data is analyzed to uncover patterns and insights that guide operational decisions, from irrigation and fertilization to pest management and crop rotation strategies. This integration of SSNs and AI enhances the precision of agricultural practices, optimizing resource use and improving yield while reducing the environmental impact [5].
The application of AI in agroecological forecasting has emerged as one of the most significant contributions to modern agriculture [6]. In traditional forecasting, predictions are often based on historical weather data and broad assumptions about crop growth [7]. However, with the integration of SSNs and AI, these predictions are refined through real-time data analytics, which allows for more accurate forecasting of microclimate conditions [8]. AI-driven models can process large volumes of environmental data, including those from sensors, satellite imagery, and weather forecasts, to predict upcoming weather patterns and their potential impact on crops [9]. This capability is critical for enabling farmers to anticipate adverse conditions, such as droughts, heatwaves, or frosts, and take preventive measures ahead of time. Real-time forecasting also supports adaptive management practices, where farmers can dynamically adjust their strategies based on evolving conditions, rather than relying on fixed schedules or historical trends [10].
