Multi-modal AI for Ultra-Precision Agriculture

Ubiquitous sensors can collect high volumes of morphological, physiological, abiotic, and biotic stress traits for monitoring, forecasting, and prediction at a scalable micro-plot level. These trait assessments necessitate a comprehensive analysis of multiple data sources for making optimum inferences. However, the collected data is usually temporally and spatially heterogeneous, and originates from various sensors, platforms (proximal-, uncrewed aerial-, and satellite-based sensing systems), and is of varying fidelities. Synergizing the inter-modal and cross-modal information of such multi-modal data using multi-modal AI becomes imperative. These requirements have spurred the development of comprehensive sensing and actuation approaches underpinned by accompanying hardware and software technologies. This chapter explores the cutting-edge developments in sensor technologies, data fusion strategies, and algorithms for agricultural applications, as these are integral to the new ultra-precision agriculture ecosystem. Advancements in cyber-physical systems have brought complementary improvement in real-time monitoring of crop-soil conditions, weather patterns, and how they impact productivity. So, this chapter includes pipelines and infrastructures for seamless data integration, communication, and storage, along with deep learning-based models focusing on crop modeling. In conclusion, practical examples that showcase the synergy between multi-modal AI, phenotyping for precise resource allocation, and implementing sustainable practices are provided.