Abstract
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.
Highlights
According to data from the United Nations, the world population is expected to grow to nine billion by 2050 [1]
Plant phenotyping is defined as the assessment of complex traits such as growth, development, tolerance, resistance, architecture, physiology, ecology, yield, and the basic measurement of individual quantitative parameters that form the basis for complex trait assessment [2]
Researchers affiliated with the United States National Science Foundation (NSF) have developed a form of CI called iPlant that incorporates artificial intelligence technologies to store and process plant data gathered from the various high-throughput phenotyping (HTP) platforms
Summary
According to data from the United Nations, the world population is expected to grow to nine billion by 2050 [1]. High-throughput phenotyping has been fostered by non-invasive imaging techniques, which have enabled the visualization of plant cell structures on a wider scale As these imaging technologies develop, images carry more useful extractable information that supports biological interpretations of plant growth [10,11]. High-throughput phenotyping techniques are currently being used to enable data acquisition in both laboratory and field settings They are being employed at the levels of data collection, data management, and analysis. Open-source devices and tools represent another fast developing application of AI technologies [30] In phenomics, these tools are addressing the challenges of expensive phenotyping equipment and proprietary or incompatible data formats. The current utilization of phenotyping technologies for field phenotyping, which is increasingly gaining ground over phenotyping in controlled environments, is discussed briefly, highlighting their cross applicability with artificial intelligence
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