Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Abstract

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Development of Reference Process Model and Reference Architecture for Pharmaceutical Cold Chain

“…It also helps them to ensure cold chain accreditation, from packing to last-mile distribution. Functional Analysis (FA) and Developing a Curriculum (DACUM) methods [16,17] are used as existing competence developing techniques, and these techniques can only be implemented with the help of subject experts. FA is designed to identify the specific and detailed content of each component of the competence model based on the worker's experiences in that position.…”

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

“…3 ). These data are invaluable for identifying key genes, proteins, and metabolic pathways involved in tolerance (Esposito et al 2019 ; Lakshmi et al 2021 ; Jung et al 2021 ; Tripodi et al 2022 ; Khan et al 2022 ; Yan and Wang 2023 ; Raza et al 2023 ).…”

“…Integrating omics data with AI enables the development of predictive models that can forecast the plant performance under metal(loid) stress, facilitating the identification of promising candidates for further breeding to design elite/superior cultivars (Fig. 3 ) (Esposito et al 2019 ; Lakshmi et al 2021 ; Jung et al 2021 ; Tripodi et al 2022 ; Khan et al 2022 ; Yan and Wang 2023 ).…”

MIPDB: A maize image-phenotype database with multi-angle and multi-time characteristics

“…Artificial intelligence (AI) is changing data interaction at the same time by turning massive amounts of data into insightful predictions and analyses(7,8). In particular, the advent of deep learning based on picture recognition has ushered in a new era of big data and AI-powered cross-disciplinary research on plant phenotypes (9,10).…”