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.

No abstract

BackgroundMaize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level.ResultsIn this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group.ConclusionThe results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.

No abstract

No abstract

No abstract

No abstract

No abstract