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:

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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:

• Research paper comparing models: A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532088/
• Deep Learning for Plant Identification: Deep Learning for Plant Identification in Natural Environment: https://www.worldscientific.com/doi/full/10.1142/S1469026822500055
• Plant Species Identification Using Computer Vision Techniques: [Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review ON Springer link.springer.com]

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

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First posted at QUE.com