The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems.
By Jinha Jung, Murilo M. Maeda, Anjin Chang, Mahendra Bhandari, Akash Ashapure, Juan Landivar-Bowles. Current Opinion in Biotechnology, 2020
339 citations recorded by the source corpus.
Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current and future food supply systems. Scientific and technological innovations are needed more than ever to secure enough food for a fast-growing global population. Scientific advances have led to a better understanding of how various components of the agricultural system interact, from the cell to the field level. Despite incredible advances in genetic tools over the past few decades, our ability to accurately assess crop status in the field, at scale, has been severely lacking until recently. Thanks to recent advances in remote sensing and Artificial Intelligence (AI), we can now quantify field scale phenotypic information accurately and integrate the big data into predictive and prescriptive management tools. This review focuses on the use of recent technological advances in remote sensing and AI to improve the resilience of agricultural systems, and we will present a unique opportunity for the development of prescriptive tools needed to address the next decade's agricultural and human nutrition challenges.
Subfields: Ecology, Genetics, Plant Science
View Paper: https://www.semanticscholar.org/paper/3a976e190a41f62e1fb86f824647a9ae55df482f
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Citations in the corpus
- Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [19,22,24,33].”