Image Processing Failure and Deep Learning Success in Lawn Measurement
This work addresses lawn area measurement for agricultural or landscaping applications, but it is incremental as it applies standard methods to a specific task.
The paper compared image processing and deep learning methods for measuring lawn area, finding that Convolutional Neural Networks achieved 94-97% accuracy, while image processing methods like thresholding and edge detection had lower accuracy, and contouring performed poorly.
Lawn area measurement is an application of image processing and deep learning. Researchers have been used hierarchical networks, segmented images and many other methods to measure lawn area. Methods effectiveness and accuracy varies. In this project Image processing and deep learning methods has been compared to find the best way to measure the lawn area. Three Image processing methods using OpenCV has been compared to Convolutional Neural network which is one of the most famous and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional Neural Network or shortly CNN shows very high accuracy (94-97%) for this purpose. In image processing methods, Thresholding with 80-87% accuracy and Edge detection are effective methods to measure the lawn area but Contouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods especially CNN could be the best detective method comparing to image processing and other deep learning techniques.