Leveraging hybrid machine learning for sustainable development through recycled waste in landscape design

Traditional methods for landscape design using recycled waste materials, which rely primarily on manual selection and placement for integration and optimization, frequently encounter sustainability issues, which reduce the overall efficiency. To address these constraints, this study proposes a hybrid machine-learning technique that leverages AI-enhanced algorithms to improve the usage of recycled materials in landscape design. Based on total adaptive learning models, this strategy incorporates adaptive correction of material variations, followed by improvement utilizing AI-informed selection algorithms. AI technologies, particularly AI sensing techniques, are used to improve adaptive material integration, allowing for more incredible sustainable growth of landscape projects. The AI-enhanced technique allows for more precise utilization and analysis of recycled materials at the design level, considerably improving the sustainability of landscape projects. The experimental results show a significant increase in the effectiveness of landscape projects processed with these AI-enhanced techniques. The findings support the suggested method's efficacy, demonstrating its robust enhancing capabilities, efficiency, and practical applicability in sustainable landscape design. This advancement contributes significantly to improving the quality of landscape projects, particularly in sustainable development applications where precision and resource optimization are critical.