Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping
Pasquale Tripodi
A * , Nicola Nicastro A and Catello Pane A
+ Author Affiliations
- Author Affiliations
A CREA Research Centre for Vegetable and Ornamental Crops, via dei Cavalleggeri 25, 84098 Pontecagnano Faiano, Salerno, Italy.
Crop & Pasture Science - https://doi.org/10.1071/CP21387
Submitted: 8 June 2021 Accepted: 11 October 2021 Published online: 1 February 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
In the upcoming years, global changes in agricultural and environmental systems will require innovative approaches in crop research to ensure more efficient use of natural resources and food security. Cutting-edge technologies for precision agriculture are fundamental to improve in a non-invasive manner, the efficiency of detection of environmental parameters, and to assess complex traits in plants with high accuracy. The application of sensing devices and the implementation of strategies of artificial intelligence for the acquisition and management of high-dimensional data will play a key role to address the needs of next-generation agriculture and boosting breeding in crops. To that end, closing the gap with the knowledge from the other ‘omics’ sciences is the primary objective to relieve the bottleneck that still hinders the potential of thousands of accessions existing for each crop. Although it is an emerging discipline, phenomics does not rely only on technological advances but embraces several other scientific fields including biology, statistics and bioinformatics. Therefore, establishing synergies among research groups and transnational efforts able to facilitate access to new computational methodologies and related information to the community, are needed. In this review, we illustrate the main concepts of plant phenotyping along with sensing devices and mechanisms underpinning imaging analysis in both controlled environments and open fields. We then describe the role of artificial intelligence and machine learning for data analysis and their implication for next-generation breeding, highlighting the ongoing efforts toward big-data management.
Keywords: artificial intelligence, big data, machine learning, next-generation breeding, phenomics, precision agriculture, sensing technologies, spectral imaging.
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