Rice Seed Image-to-Image Translation Using Generative Adversarial Networks to Improve Weedy Rice Image Classification

Authors

นายอรรถกร เพชรสด, ผศ.ดร.ธนาสัย สุคนธ์พันธุ์

Published

Lecture Notes in Computer Science (Machine Learning and Knowledge Extraction)

Abstract

Rice is a staple food for more than half of the world’s population. Furthermore, rice is the main export crop of Thailand which produces 21% world’s market share. However, weedy rice is a major counterproductive plant that reduces rice productivity by more than 80% in Thailand. Previous research attempted to develop image classification models to recognize types of rice using images captured in closed environments, which is not practical for farmers with typical mobile phone cameras. This research develops a specific Generative Adversarial Network (GAN) architecture to translate an input image from a typical mobile phone cameras into the closed environment setting. Our GAN architecture can translate mobile phone images and achieves 90.06% weedy rice recognition accuracy, as compared to 58.10% without the translation.

(2021). Rice Seed Image-to-Image Translation Using Generative Adversarial Networks to Improve Weedy Rice Image Classification . Lecture Notes in Computer Science (Machine Learning and Knowledge Extraction), 2021(12844), 137-151.