外文摘要：Precision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher resolution meteorological and soil data obtained through in situ sensing. The integration of machine learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. These data allow ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in Southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.
外文关键词：Soil moisture;Internet of Things (IoT);artificial intelligence (AI);Precision Agriculture (PA);frost;grapevine diseases;precision viticulture (PV)
作者：Pero, Chiara;Bakshi, Sambit;Nappi, Michele;Tortora, Genoveffa
作者单位：Natl Inst Technol;Univ Salerno
期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
期刊影响因子：0.0
出版年份：2024
出版刊次：14
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台