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This is a Literature Review study.

September 10, 2025Open Access

The pipelines of deep learning-based plant image processing

KHKaiyue HongNanjing Forestry University YZYun ZhouSouthwest University HHHan HanNanjing Forestry University

Puntos clave

Deep learning techniques significantly enhance the accuracy of plant species identification and disease detection.
The latest image processing advancements include methods like high-resolution imaging and UAV photography for improved data acquisition.
Methods for feature extraction, such as colour histograms and texture analysis, play a critical role in health assessment of plants.
Emerging trends highlight the need for further research and practical applications in plant science using deep learning technologies.

Resumen

Abstract 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.

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