Use case

Computer vision with deep learning for plant monitoring

Using computer vision and deep learning for plant monitoring enhances crop management by providing accurate, high-throughput, and noninvasive measurements. This approach addresses the growing demand for food and supports sustainable agriculture. Future advancements will focus on integrating diverse data from advanced, cost-effective monitoring technologies.


Growing challenges in agriculture due to the increasing global demand for food makes it imperative to employ efficient crop management techniques to enhance yield.

Plant cultivation plays an important role in preserving the sustainability of natural ecosystems and human food security. Achieving sustainable agricultural development demands comprehensive efforts from various angles.

Human beings need to create a good ecological environment, including water resources, forests, and soil, to ensure that plants grow in a healthy environment.


Measuring and analyzing observable plant characteristics (Phenotyping) is essential for accurate crop monitoring, involving the characterization and quantification of a plant's traits across different environments.

This field has rapidly advanced due to affordable digital imaging devices like RGB, multispectral, and hyperspectral cameras, enabling extensive data collection. Deep learning has further spurred the development of visual plant monitoring methods, known for their effectiveness in vision-based tasks like image classification, object detection, and semantic segmentation.

Notably, these tasks underpin various plant monitoring applications such as growth development, disease detection, fruit/leaf identification, and yield estimation.


  • Estimation of the top part of plant structure where it is unavoidable that some branches and leaves will block others from view. Limited datasets and need for augmentation.


  • Traditional plant monitoring methods, reliant on manual measurements and observations, are time-consuming, labor-intensive, and prone to human errors.
  • High-throughput monitoring can handle a large number of plants within a given timeframe.
  • Imaging technologies provide noninvasive, objective, and high-resolution measurements of plant traits.


  • Integrating heterogeneous data from reliable, automated, multifunctional, and high-throughput plant monitoring platforms.
  • Development of novel technologies, emphasizing developing low-cost, high-performance, high-throughput technologies.

The automated monitoring systems

Automated monitoring systems use image processing, feature extraction, and deep learning to detect product defects, ensure PPE compliance, and monitor worker-machine proximity. They also employ real-time processing for accurate product counting on assembly lines. Mastering these skills enables effective application across various scenarios. Read more

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