Deep learning-empowered crop breeding: intelligent, efficient and promising

Wang, Xiaoding; Zeng, Haitao; Lin, Limei; Huang, Yanze; Lin, Hui; Que, Youxiong

doi:10.3389/fpls.2023.1260089

Cited by 11 publications

(2 citation statements)

References 117 publications

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“…It has become an industry consensus to simultaneously enhance sugarcane and land productivity under the conditions of high-level mechanization of the production process by breeding varieties suitable for mechanized operations, ensuring that agronomic practices match agricultural machinery operation standards, providing mechanical equipment and technical support for high-yield production, and improving soil structure and soil productivity under the conditions of mechanical operations ( Zhang et al., 2021 ). With the rapid integration of artificial intelligence and big data ( Wang X. et al., 2023 ), research into the mechanization of the entire sugarcane production process is on the rise. We sincerely invite colleagues from home and abroad to work together to speed up the selection and breeding of sugarcane varieties that are suitable for mechanization, resistant to smut, and have a strong ratooning ability.…”

Section: Prospectsmentioning

confidence: 99%

Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects

Que,

Wu,

Zhang

et al. 2024

Front. Plant Sci.

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The sugar industry, which relates to people’s livelihood, is strategic and fundamental in the development of agricultural economy. In China, sugar derived from sugarcane accounts for approximately 85% of total sugar production. Mechanization is the “flower” of sugarcane industry. As the saying goes “when there are blooming flowers, there will be sweet honey.” However, due to limitations in land resources, technology, equipment, organization, and management, mechanization throughout the sugarcane production process has not yet brought about the economic benefits that a mechanized system should provide and has not reached an ideal yield through the integration of agricultural machinery and agronomic practice. This paper briefly describes how to initiate the mechanization of Chinese sugarcane production to promote the sound, healthy, and rapid development of the sugarcane industry, and how to ultimately achieve the transformation of sugarcane breeding in China and the modernization of the sugarcane industry from three perspectives, namely, requirements of mechanized production for sugarcane varieties, breeding strategies for selecting new sugarcane varieties suitable for mechanized production, and screening for sugarcane varieties that are suitable for mechanization and diversification in variety distribution or arrangement in China. We also highlight the current challenges surrounding this topic and look forward to its bright prospects.

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Section: Prospectsmentioning

confidence: 99%

Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects

Que,

Wu,

Zhang

et al. 2024

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…DeepSEA [100] pioneered a CNN-based framework integrating chromatin profiles to predict variant effects on expression and miRNA binding. Graph networks that represent relationships between phenotype-associated variants have also proven effective [101].…”

Section: Genetic Variant Interpretationmentioning

confidence: 99%

AI and the next medical revolution: deep learning’s uncharted healthcare promise

L B,

Kotei

et al. 2024

Eng. Res. Express

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Deep learning has shown tremendous potential for transforming healthcare by enabling more accurate diagnoses, improved treatment planning and better patient outcome predictions. In this comprehensive survey, we provide a detailed overview of the state-of-the-art deep learning techniques and their applications across the healthcare ecosystem. We first introduce the fundamentals of deep learning and discuss its key advantages compared to traditional machine learning approaches. We then present an in-depth review of major applications of deep learning in medical imaging, electronic health record analysis, genomics, medical robotics and other domains. For each application, we summarize key advancements, outline the technical details of state-of-the-art methods, discuss challenges and limitations and highlight promising directions for future work. We examine cross-cutting challenges in deploying deep learning in clinical settings, including interpretability, bias and data scarcity. We conclude by proposing a roadmap to accelerate the translation and adoption of high-impact healthcare applications of deep learning. Overall, this survey provides a comprehensive reference for researchers and practitioners working at the intersection of deep learning and healthcare.

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Advancing plant biology through deep learning-powered natural language processing

Peng,

Rajjou

2024

Plant Cell Rep

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Deep learning-empowered crop breeding: intelligent, efficient and promising

Cited by 11 publications

References 117 publications

Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects

Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects

AI and the next medical revolution: deep learning’s uncharted healthcare promise

Advancing plant biology through deep learning-powered natural language processing

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