Hong Kaiyue, Zhou Yun, Han Han
Co-Innovation Center for Sustainable Forestry in Southern China, College of Life Sciences, Nanjing Forestry University, Nanjing, China.
Department of Botany and Plant Pathology, Center for Plant Biology, Purdue University, West Lafayette, IN, USA.
Quant Plant Biol. 2025 Jul 25;6:e23. doi: 10.1017/qpb.2025.10018. eCollection 2025.
Recent advancements in data science and artificial intelligence have significantly transformed plant sciences, particularly through the integration of image recognition and deep learning technologies. These innovations have profoundly impacted various aspects of plant research, including species identification, disease detection, cellular signaling analysis, and growth monitoring. This review summarizes the latest computational tools and methodologies used in these areas. We emphasize the importance of data acquisition and preprocessing, discussing techniques such as high-resolution imaging and unmanned aerial vehicle (UAV) photography, along with image enhancement methods like cropping and scaling. Additionally, we review feature extraction techniques like colour histograms and texture analysis, which are essential for plant identification and health assessment. Finally, we discuss emerging trends, challenges, and future directions, offering insights into the applications of these technologies in advancing plant science research and practical implementations.
数据科学和人工智能领域的最新进展显著改变了植物科学，特别是通过图像识别和深度学习技术的整合。这些创新对植物研究的各个方面产生了深远影响，包括物种识别、疾病检测、细胞信号分析和生长监测。本综述总结了这些领域中使用的最新计算工具和方法。我们强调数据采集和预处理的重要性，讨论高分辨率成像和无人机摄影等技术，以及裁剪和缩放等图像增强方法。此外，我们还综述了颜色直方图和纹理分析等特征提取技术，这些技术对于植物识别和健康评估至关重要。最后，我们讨论新兴趋势、挑战和未来方向，深入探讨这些技术在推进植物科学研究和实际应用中的应用。
