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Computer Vision Model for Plants.

I have a small project to identify different plants and flowers in my garden so I'm looking for best computer vision model or even a combination of LLMs out there. I found several computer vision models perform well in plant identification. Here are some top contenders to consider, depending on your specific needs:

Image source: QUE.COM - Artificial Intelligence and Machine LearningQUE.COM - Artificial Intelligence and Machine Learning projects.

1. Convolutional Neural Networks (CNNs):

  • These are the current state-of-the-art for plant identification.
  • Popular pre-trained CNN options include:
    • ResNet: Known for its accuracy and efficiency, especially variants like ResNet-50 or the customized ResNet26 for plant images.
    • MobileNetV2: A lighter-weight option ideal for mobile applications due to its faster processing speed with good accuracy.

2. Other Models:

  • Bag-of-Words (BoW) model with Scale-Invariant Feature Transform (SIFT): This approach extracts keypoint features from the image and uses them for classification. While not the most advanced, it can be a good option for smaller datasets.

Choosing the best model depends on factors like:

  • Dataset size and variety: CNNs generally perform better with extensive training data.
  • Computational resources: ResNet might be more demanding than MobileNetV2.
  • Application requirements: If you need a mobile app, MobileNetV2's efficiency might be crucial.

Here are some additional resources to help you decide:

Use the comment below to post other ideas. I will share the website or app after my research and testing when it's completed.

More Computer Vision or Machine Learning projects at MachineLearn.com

First posted at QUE.com

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