Review: Application of Artificial Intelligence in Phenomics

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.

Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris

“…In the last years, thermal and (multior hyper-) reflectance imaging sensors have been broadly used for monitoring stress in crop fields. Furthermore, sustainable agriculture is increasingly relying on AI (such as classifying algorithms) coupled with computer vision, to solve farming issues and to promote the automation of decision-making process (Tian et al, 2020;Nabwire et al, 2021). However, these methods require basic research to define informative parameters that efficiently report the health state and fitness of a particular crop.…”

Core Collections: Is There Any Value for Cotton Breeding?

“…While core collections for drought tolerance have been assembled, gains in limited water and rainfed cotton productivity have largely been incremental and reflective of improvements in yield potential under non-stressed conditions. Thus, future genetic gain in abiotic stress resistance will require a combination of traditional plant breeding and new breeding methods such as genomic selection, as well as the integration of panomics ( Weckwerth et al, 2020 ), novel field-based phenomics ( White et al, 2012 ; Zhao et al, 2019 ), and the application of machine learning and artificial intelligence to breeding for complex plant traits ( Niazian and Niedbała, 2020 ; Nabwire et al, 2021 ). However, these methods are currently prohibitively expensive and/or will require a core collection of diverse germplasm to efficiently assess whether they will be effective in cotton breeding.…”

An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms

“…Machine learning enables systems to automatically learn and improve based on their experiences. With the emergence of large spectral libraries, we must seize the opportunity to use big data analytics to aid in the use and processing of spectral data, which goes beyond commercial software or packaged machine learning methods [ 47 ]. Deep learning-based model is different from traditional neural networks, which have been utilized in NIR spectra processing, as it is made up of multiple processing layers and deeper architectures to learn data representation [ 45 ].…”