The pipelines of deep learning-based plant image processing.

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.

Code and data availability

This is a review article on deep learning-based plant image processing pipelines. The authors explicitly state no data or code are involved, and the supplied blocks contain only reference lists citing prior work (PlantDoc, Soybean-MVS, Plantseg, etc.), which are cited third-party resources rather than paper-specific, i

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