Transforming Urban Green Spaces: The Impact of Large Language Models on Smart and Sustainable Urban Plantations
摘要
The Green Revolution (GR) period (1960–2000) was an exceptional time of enhanced global food security. World food production and distribution increased incredibly for grains such as wheat, rice, and maize through rural agriculture intensification. It has been achieved due to synergetic combination of immense investment on research into crops, increased agricultural cultivation, mechanization, as well as mass use of synthetic fertilizers and pesticides, and genetically engineered varieties of high-yielding crops, the HYV. Even as the population had doubled, cereal production had trebled when the cultivated area had grown by only 30%. While the GR was followed by food prices that decreased sharply, mainly favoring consumers, many developing agrarian countries experienced ill effects that arise from an improvement in the quality of their ecosystems, associated with environmental degradation and loss of biodiversity. Integrating LLMs into the urbanization of green spaces is also critical for smart and sustainable urban agriculture. These models improve the processes involved with decision-making, offering insights and recommendations that are based on data about plant selection, urban planning, and resource management. The following sections describe the profound effects of LLMs on urban green spaces. LLMs support low-carbon plant options, which in turn further contribute to landscape architecture with sustainable orientation. They employ advanced databases and complex algorithms for suggesting ecologically suitable plant selections, thus solving the environmental problems. The reconciliation of LLMs in savvy metropolitan boards develops green space exercises through information investigation and IoT combination. These innovations enable continuous monitoring of environmental factors, enhancing resource allocation and maintenance strategies. LLMs make metropolitan maintainability appraisals much simpler to do by mechanizing the grouping of drives against set supportability rules. Such a methodology supports an all-encompassing comprehension of metropolitan undertakings and breaks storehouses in arranging, empowering cooperative endeavors. LMPs actually offer one fundamental benefit over the conventional organization of metropolitan green spaces as far as information precision and the translation of those requires human information.