AI-Driven Construction and Application of Gardens: Optimizing Design and Sustainability with Machine Learning

Abstract

Artificial intelligence (AI) integration into environ mental analysis has revolutionized various fields. Including the construction and application of gardens, by enabling precise classification and decision-making for sustainable practices. This paper presents a strong AI-driven framework uses convolutional neural network (CNN) and pretrained models like VGG16 and InceptionV3 to classify eight distinct environmental classes. The CNN achieved superior performance Among the tested models and reaching an impressive 98% accuracy with optimized batch sizes. This demonstrate its effectiveness for precise environmental condition classification. This work highlights the crucial role of AI in advancing the construction and application of gardens. It offers insights into optimizing garden design through accurate environmental data analysis. The diverse dataset used ensures the framework’s adaptability to real-world applications, making it a valuable resource for sustainable development and eco-friendly design strategies. This paper not only contributes to the field of AI-driven environmental analysis but also provides a foundation for future innovations in garden management and sustainability, paving the way for intelligent solutions in the evolving landscape of ecological design.