Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping

TLDR

Global agricultural changes demand innovative, non‑invasive precision agriculture technologies that use sensing devices and AI to manage high‑dimensional data and advance phenomics, integrating biology, statistics, and bioinformatics. The review aims to bridge plant phenomics with other omics, alleviate bottlenecks in crop accessions, and foster international collaboration by illustrating phenotyping concepts and imaging mechanisms. The review describes plant phenotyping concepts, sensing devices, imaging mechanisms in controlled and field settings, and the role of AI and machine learning in data analysis for next‑generation breeding and big‑data management.

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.