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Multimodal AI to Improve Agriculture
IT Professional ( IF 2.6 ) Pub Date : 2021-06-24 , DOI: 10.1109/mitp.2020.2986122
Cynthia S. Parr 1 , Danielle G. Lemay 1 , Christopher L. Owen 1 , M. Jennifer Woodward-Greene 1 , Jiayang Sun 1
Affiliation  

Advances in natural language processing (NLP) and computer vision are now being applied to many agricultural problems. These techniques take advantage of nontraditional (or nonnumeric) data sources such as text in libraries and images from field operations. However, these techniques could be more powerful if combined with Artificial Intelligence (AI) and numeric sources of data in multimodal pipelines. We present several recent examples, where United States Department of Agriculture (USDA) Agricultural Research Service (ARS) researchers and collaborators are using AI methods with text and images to improve core scientific knowledge, the management of agricultural research, and agricultural practice. NLP enables automated indexing, clustering, and classification for agricultural research project management. We explore two case studies where combining techniques and data sources in new ways could accelerate progress in personalized nutrition and invasive pest detection. One challenge in applying these techniques is the difficulty in obtaining high-quality training data. Other challenges are a lack of machine learning (ML) techniques customized for use and ML skills or experience among researchers and other stakeholders. Initiatives are underway at USDA-ARS to address these challenges.

中文翻译:

多模式人工智能改善农业

自然语言处理 (NLP) 和计算机视觉的进步现在正被应用于许多农业问题。这些技术利用了非传统(或非数字)数据源,例如图书馆中的文本和来自现场操作的图像。但是,如果与人工智能 (AI) 和多模式管道中的数字数据源相结合,这些技术可能会更加强大。我们展示了几个最近的例子,其中美国农业部 (USDA) 农业研究服务 (ARS) 的研究人员和合作者正在使用人工智能方法和文本和图像来改进核心科学知识、农业研究管理和农业实践。NLP 支持农业研究项目管理的自动索引、聚类和分类。我们探索了两个案例研究,其中以新的方式结合技术和数据源可以加速个性化营养和入侵性害虫检测的进展。应用这些技术的一个挑战是难以获得高质量的训练数据。其他挑战是缺乏为使用而定制的机器学习 (ML) 技术以及研究人员和其他利益相关者的 ML 技能或经验。USDA-ARS 正在采取措施应对这些挑战。其他挑战是缺乏为使用而定制的机器学习 (ML) 技术以及研究人员和其他利益相关者的 ML 技能或经验。USDA-ARS 正在采取措施应对这些挑战。其他挑战是缺乏为使用而定制的机器学习 (ML) 技术以及研究人员和其他利益相关者的 ML 技能或经验。USDA-ARS 正在采取措施应对这些挑战。
更新日期:2021-06-25
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