Inthe field of agriculture, the plants are closely observed in order to get the maximum yield. This includes observing various plant phenotypes such as flowers, leaves, stem length etc. These phenotypes indicate the growth of the plants under observation. Hence, appropriate care can be taken according to the observed growth and condition of the plant. This phenotype data is also useful for plant breeding and other related research programs. Counting leaves is one of the important phenotypes that gives a clear idea of the plant’s health and its current development stage. The traditional manual observation of these phenotypes can be a very slow and tedious. In order to automate this, we can use Image processing and Deep Learning techniques. This blog post aims at explaining the approach to solve one such scenario where we use Deep Learning and Image processing to count the leaves given the plant images in order to reduce the tedious task of manual observation.
Case Study Overview
The approach to this problem can be broken down into following steps:

  1. Problem Description
  2. Data Preprocessing
  3. Segmentation Model
  4. Regression Models based on Segmentation model
  5. Transfer Learning Models
  6. Results Analysis
  7. Model Quantization
  8. Streamlit App to demonstrate the Best Model
  9. Future Work/Improvements
  10. Conclusion

#deep-learning #segmentation #image-processing #keras #tensorflow

Let Me Count The Leaves for You — A Deep Learning Case Study
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