Abstract
The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation. In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data. The scanned 3D points of the training site were sliced row and row with a fixed 3D window. Points within the window were compressed into deep images, which were used to train the Faster R-CNN (region-based convolutional neural network) model to learn the ability of detecting maize stem. Three sites of different planting densities were used to test the method. Each site was also sliced into many 3D windows, and the testing deep images were generated. The detected stem in the testing images can be mapped into 3D points, which were used as seed points for the regional growth algorithm to grow individual maize from bottom to up. The results showed that the method combing deep leaning and regional growth algorithms was promising in individual maize segmentation, and the values of r, p, and F of the three testing sites with different planting density were all over 0.9. Moreover, the height of the truly segmented maize was highly correlated to the manually measured height (R2> 0.9). This work shows the possibility of using deep leaning to solve the individual maize segmentation problem from Lidar data.
Highlights
During the 20th century alone, the human population has grown from 1.65 billion to 6 billion according to the United Nations1
This study proposed an indirectly way of 3D object detection and segmentation, which used 2D Faster R-Convolution neural network (CNN) to detect object in 2D images compressed from 3D points
We demonstrated the combination of deep learning and regional growth methods to segment individual maize with terrestrial light detection and ranging (Lidar) scanned 3D points
Summary
During the 20th century alone, the human population has grown from 1.65 billion to 6 billion according to the United Nations. By the middle of the 21st century, the global population will reach up to 9–10 billion (Cohen, 2003; Godfray et al, 2010). To meet the challenge, developing new methods of crop breeding to increase crop yields is a promising option (Ray et al, 2013). Traditional crop breeding, such as hybrid breeding, relies on the breeding experience of breeders, which has the disadvantage of long cycle, low efficiency and great uncertainty. The major reason is the lack of precise and high throughput phenotype data to assist gene discovery, identification, and selection (Rahaman et al, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
