利用机器学习评估 NDVI 和气候数据在玉米收成预测中的效率
IF 3.7
1区 地球科学
Q1 GEOGRAPHY, PHYSICAL
引用次数: 0
摘要
准确预测玉米收获期对农业市场非常重要，因为它能确保粮食生产的可持续性，以满足全球日益增长的粮食需求。这...本文章由计算机程序翻译，如有差异，请以英文原文为准。
Evaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning
Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. Th...
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来源期刊
CiteScore
6.50
自引率
3.90%
发文量
88
审稿时长
3 months
期刊介绍：
The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that:
Progress visions for Digital Earth frameworks, policies, and standards;
Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery;
Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared;
Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level;
Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning;
Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and
Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
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