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.

Keywords: data science; deep learning; feature extraction; image recognition; machine learning.

Graphical abstract

Figure 1.

CNN and data training process flowchart. A. DL-based image processing flowchart; B. Data training process elowchart.

Figure 2.

A keyword network analysis of DL in plants. A keyword analysis of plant AI technologies reveals clear technological connections. Blue lines indicate image processing technologies, and green lines represent plant phenotyping and growth analysis. At its core, ‘DL’ links to ‘ML’, ‘image processing,’ and ‘computer vision.’ Technologies such as ‘remote sensing’ and ‘precision agriculture’. The relationships between terms like ‘plant growth’, ‘diseases’, ‘phenomics’, and ‘smart agriculture’ indicate the growing integration of AI and ML in improving plant practices.