Open AccessApplication of Convolutional Neural Network In LAWN Measurement
Author(s) -
John S. Wilkins,
Manh B. Nguyen,
Budi Rahmani
Publication year - 2021
Publication title -
signal and image processing : an international journal
Language(s) - English
Resource type - Journals
eISSN - 2229-3922
pISSN - 0976-710X
DOI - 10.5121/sipij.2021.12101
Subject(s) - lawn , convolutional neural network , python (programming language) , artificial intelligence , computer science , deep learning , artificial neural network , machine learning , measure (data warehouse) , pattern recognition (psychology) , data mining , botany , biology , operating system
Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, deep learning method, specifically Convolutional neural network, was applied to measure the lawn area. We used Keras and TensorFlow in Python to develop a model that was trained on the dataset of houses then tuned the parameters with GridSearchCV in ScikitLearn (a machine learning library in Python) to estimate the lawn area. Convolutional neural network or shortly CNN shows high accuracy (94 -97%). We may conclude that deep learning method, especially CNN, could be a good method with a high state-of-art accuracy.