Intelligent renewal method of productive landscape based on the inheritance of Inner Mongolia grassland food culture

Abstract: Productive landscape refers to a sustainable landscape system formed by combining material output and spatial creation based on agricultural, forestry, animal husbandry, fishing and other production activities. China has a vast territory, significant climate differences between the north and south, and regional integration of food and culture. In Inner Mongolia, a typical representative of northern China, the productive landscape presents unique historical and regional characteristics: on the one hand, the nomadic tradition has shaped the landscape form centered on grassland animal husbandry and dairy product processing; On the other hand, the introduction of farming and gathering activities has enriched the types of dietary landscapes such as grains, fruits, and vegetables. As a carrier of productive landscapes, the inheritance of food culture carries important functions of food supply, national memory, and cultural continuity as a result of the interaction between human long-term food practice and natural environment. Diversified productive landscapes not only support the survival system of regional society, but also have irreplaceable value in ethnic cultural identity and intangible heritage protection. However, current research still relies mainly on qualitative records, with insufficient identification and quantitative analysis of its elements, which hinders the scientific protection and reuse of it. To solve this problem, this article adopts deep learning methods to automatically identify and classify the productive landscapes of typical grassland food culture inheritance background. Based on the ResNet50 model in the PyTorch framework, an image dataset covering landscape types such as pastures, farmland, forest gardens, and fishing grounds is constructed, and preprocessed through size standardization, normalization, and data augmentation. The model is trained with the support of transfer learning and its performance is validated through multiple metrics. The research results indicate that this method can efficiently identify the core elements of food culture in productive landscapes in complex natural environments, significantly improving classification accuracy and stability. Its application value lies in providing a reliable technical path for the digital archiving, dynamic monitoring, and scientific management of food in productive landscapes, aiming to promote the protection, rational utilization, and cultural value transformation of food landscapes, thereby supporting rural revitalization and regional sustainable development.

Keywords: PyTorch, productive landscapes, feature recognition, sustainable planning, cultural heritage

DOI: 10.54941/ahfe1006853

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