Unified Retrieval-Augmented Intelligence for Agricultural and Environmental Research (URAIAER): A Memory-Centric Framework for Long-Term Agricultural Decision Support

Abstract

Modern agricultural research is flooded with various types of data - climate information, soil survey data, crop monitoring feeds, and the scholarly literature that connects it all. However, the majority of AI algorithms utilized in this field remain largely stateless - they analyze what they have in front of them but fail to remember anything, making it difficult to transfer knowledge gained during one harvest season to another one. This paper presents URAIAER (Unified Retrieval-Augmented Intelligence for Agricultural and Environmental Research) - a retrieval-augmented generation (RAG) approach based on three pillars - IoT-based environmental sensing, machine learning forecasting, and the vector memory of observations accumulated by the model over time. Environmental sensing is provided by the system via distributed sensor nodes, providing information about air quality, temperature, humidity, soil moisture, pH levels and other environmental factors that need to be taken into account. Data provided by these sensors is processed, embedded into vector space, and indexed in a vector database to enable future reasoning about previous observations. Thus, URAIAER combines real-time environmental monitoring with accumulated vector memory to retrieve external knowledge and use both in conjunction with domain-specific machine learning models to make predictions and recommendations. The retrieval model in URAIAER is derived from the Retrieval Augmented Generation (RAG) model proposed by Lewis et al. [1], while the memory system in URAIAER borrows concepts from the latest works on memory-augmented AI. The IoT-based sensing model in URAIAER uses established paradigms from smart agriculture and IoT literature [6], [7]. Pradhan and Unhelkar [12] have studied a similar sensing model, which illustrates the use of IoT to provide environmental monitoring and make sustainable decisions, which has influenced the design of the data acquisition model here.