Fangfang Wu

Microwave sintering of α/β-Si3N4

Abstract Dense silicon nitride from α- and β-Si 3 N 4 powders with 6 wt% (Y 2 O 3 + Al 2 O 3 ) additives were fabricated in a TE 103 single mode cavity microwave sintering system operating at 2.45 GHz. Packing powders were used. The sintering behaviour was investigated with special interest in the evolution of the α- to β-Si 3 N 4 phase transformation, densification and microstructure development. Densities of about 96.7% TD and 95% TD were achieved by microwave sintering of α- and β-Si 3 N 4 powders; the α to β conversion is more rapid than densification in the microwave heating of α-Si 3 N 4 .

A geometric method for wood-leaf separation using terrestrial and simulated Lidar data

Abstract Terrestrial light detection and ranging (lidar) can be used to record the three-dimensional structures of trees. Wood-leaf separation, which aims to classify lidar points into wood and leaf components, is an essential prerequisite for deriving individual tree characteristics. Previous research has tended to use intensity (including a multi-wavelength approach) and waveform information for wood-leaf separation, but use of the most fundamental information from a lidar point cloud, i.e., the x-, y-, and z- coordinates of each point, for this purpose has been poorly explored. In this study, we introduce a geometric method for wood-leaf separation using the x-, y-, and z- coordinates of each point. The separation results indicate that first-, second-, and third-order branches can be extracted from the raw point cloud by this new method, suggesting that it might provide a promising solution for wood-leaf separation.

An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China

ABSTRACT In recent decades, global biodiversity has gradually diminished due to the increasing pressure from anthropogenic activities and climatic change. Accurate estimations of spatially continuous three-dimensional (3D) vegetation structures and terrain information are prerequisites for biodiversity studies, which are usually unavailable in current ecosystem-wide studies. Although the airborne lidar technique has been successfully used for mapping 3D vegetation structures at landscape and regional scales, the relatively high cost of airborne lidar flight mission has significantly limited its applications. The unmanned aerial vehicle (UAV) provides an alternative platform for lidar data acquisition, which can largely lower the cost and provide denser lidar points compared with airborne lidar. In this study, we implemented a low-cost UAV-borne lidar system, including both a hardware system and a software system, to collect and process lidar data for biodiversity studies. The implemented UAV-borne lidar system was tested in three different ecosystems across China, including a needleleaf–broadleaf mixed forest, an evergreen broadleaf forest, and a mangrove forest. Various 3D vegetation structure parameters (e.g. canopy height model, canopy cover, leaf area index, aboveground biomass) were derived from the UAV-borne lidar data. The results show that the implemented UAV-borne lidar system can generate very high resolution 3D terrain and vegetation information. The developed UAV-based hardware and software systems provide a turn-key solution for the use of UAV-borne lidar data on biodiversity studies.

Microwave reaction sintering of α-β-sialon composite ceramics

Rapid heating and sintering velocity caused by internal and volumetric heating using microwave energy have potential in uniformly heating and sintering samples and obtaining higher density and finer microstructure. α-β-Sialon ceramics with nominal composition α:β = 20:80 can be sintered close to TD within 10 min of soaking time in a single mode cavity microwave sintering system operating at 2.45 GHz and higher density, finer grain size and good mechanical properties are obtained also.

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms

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.