Wang Xiaoding, Zeng Haitao, Lin Limei, Huang Yanze, Lin Hui, Que Youxiong
Fujian Provincial Key Lab of Network Security & Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China.
School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China.
Front Plant Sci. 2023 Oct 3;14:1260089. doi: 10.3389/fpls.2023.1260089. eCollection 2023.
Crop breeding is one of the main approaches to increase crop yield and improve crop quality. However, the breeding process faces challenges such as complex data, difficulties in data acquisition, and low prediction accuracy, resulting in low breeding efficiency and long cycle. Deep learning-based crop breeding is a strategy that applies deep learning techniques to improve and optimize the breeding process, leading to accelerated crop improvement, enhanced breeding efficiency, and the development of higher-yielding, more adaptive, and disease-resistant varieties for agricultural production. This perspective briefly discusses the mechanisms, key applications, and impact of deep learning in crop breeding. We also highlight the current challenges associated with this topic and provide insights into its future application prospects.
作物育种是提高作物产量和改善作物品质的主要途径之一。然而，育种过程面临着诸如数据复杂、数据获取困难以及预测准确率低等挑战，导致育种效率低下且周期长。基于深度学习的作物育种是一种应用深度学习技术来改进和优化育种过程的策略，可加速作物改良、提高育种效率，并培育出更高产、适应性更强和抗病的农业生产品种。本文简要讨论了深度学习在作物育种中的机制、关键应用及影响。我们还强调了与该主题相关的当前挑战，并对其未来应用前景提供见解。
