Integrating personalised context aware systems with farming practices towards sustainable development for rural agriculture in Shaanxi Province, China.

Abstract

This study investigates how personalized context-aware systems can be integrated into farming practices underpinned by ubiquitous computing devices and adaptive learning models. One of the most compelling aspects of personalized context-aware systems is their ability to combine traditional agricultural knowledge with modern technological advancements. This study contributes to the field by demonstrating how to integrate personalized context-aware systems with adaptive learning models and IoT technology can significantly enhance agricultural productivity and sustainability. This research problem is to determine on how the application of sophisticated technologies, namely the Internet of Things (IoT), machine learning and geographic information system (GIS) with conventional farming techniques boost yields within rural farming activities. The primary objectives of this research are as follows: i) To identify the agricultural practices and environmental impacts in Shaanxi Province, China, ii) To determine agriculture practices towards sustainable rural agriculture in Shaanxi Province, China, iii) To analyse the machine learning models for sustainable rural agriculture in Shaanxi Province, China, and iv) To formulate the framework for sustainable rural agriculture in Shaanxi Province, China. This work employs Shaanxi Province as the study area because of its rich and diverse agriculture practices and severe environmental issues. Both quantitative research using predictive models and quantitative geographical spatial analysis were used in combination with semi-structured interviews and questionnaires with farmers in Shaanxi Province, China. The main purpose is to formulate a framework for the efficient utilization of technology in the implementation of sustainable agriculture in perceived rural farming societies. These findings can prove that using IoT, machine learning, and GIS enhances resource availability and sustainability, produces high yields, and increases food production. The study also realizes socio-economic barriers to these technologies, including education level, financial resources, and so on. Therefore, this research enhances the existing body of knowledge by presenting a concept that delineates a scalable model of sustainable rural agriculture for other provinces in China and comparable conditions across the globe. The likely policy effects include policies of subsidies for the adoption of technology, enhancement of infrastructure and farmer training programs. These measures ensure that rural communities can transform into new sustainable and efficient agriculture practices for sustainable use of natural resource capital with a view of achieving sustainable economic benefits.