Big Data–Driven Agricultural Knowledge Engineering for Climate-Resilient Farming

Abstract

Climate variability and extreme weather events are growing to become a threat to the agricultural productivity, food security, and the livelihoods of farmers especially in regions that are sensitive to climate. The current paper provides an agricultural knowledge engineering framework based on Big Data to aid in climate-intensive agriculture with the help of smart data collection, analytics, and decision-support. The various information sources of IoT based field sensors, weather stations, satellite remote sensing, and past soil, crop and climate databases are gathered harmoniously, pre-processed and combined to form one unified agricultural data ecosystem. Developed data cleaning, normalization and feature extraction algorithms are used to guarantee the reliability of data and multi-source interoperability allows knowledge to be constantly enriched. In order to have smart modelling, both conventional statistical and deep learning models are used, such as linear and multi-linear regression, recurrent neural networks, and long short-term memory models. Such models allow predicting the trends in crop yield, climate risks, and the state of stress correctly, which contributes to making informed and timely decisions. Predictive analytics can tell potential weaknesses in varying climatic conditions, whereas prescriptive analytics will suggest adaptive measures, including optimized irrigation timetable, crop choice, and risk-sensitive farming oversight measures. Experimental findings indicate that it has enhanced reality forecasting, knowledge interpretation, and strength than traditional forms of data-driven approaches. The suggested framework puts a lot of emphasis on the importance of big data analytics and knowledge engineering in facilitating sustainable, climate-resilient agricultural systems and provides a basis of the next-generation smart farming applications.