[Show abstract][Hide abstract] ABSTRACT: Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.
[Show abstract][Hide abstract] ABSTRACT: Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.
[Show abstract][Hide abstract] ABSTRACT: The length of the rice panicle determines the number of grains it can hold, and consequently rice yield; it is therefore one of the most important traits assessed in yield-related research. However, the conventional method of measuring panicle length is still a manual process that is inconsistent, subjective and slow. In this study, a novel prototype, dubbed ''Smart-PL'', was developed for the automatic measurement of rice panicle length based on dual-camera imaging. Cameras with a long-focus lens and a short-focus lens were utilized to capture both a detailed image and a complete image of the rice panicle, respectively. Specific image processing algorithms were exploited, to analyze the neck image for neck identification and the whole-panicle image for path extraction. Subsequently, co-registration was used to identify the neck location in the whole-panicle image, and a resampling method was used to search for the path points between the panicle neck and the tip. Finally, the panicle length was calculated as the sum of the distances between each adjacent path point. To evaluate the accuracy of this prototype, six batches of rice panicles were tested. The results showed that the mean absolute percentage error (MAPE) for the system was about 1.23%, and the automatic measurements had a good agreement with manual measurements, regardless of panicle type. To evaluate the efficiency of this prototype, 3108 panicle samples were tested under continuous-measurement conditions, and the measuring efficiency was approximately 900 panicles per hour, 6 times over manual method. In conclusion, the system automatically extracts panicle length while providing three advantages over the manual method: objectiveness, high efficiency and high consistency.
Computers and Electronics in Agriculture 10/2013; 98:158-165. DOI:10.1016/j.compag.2013.08.006 · 1.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Biomass is an important component of the plant phenomics, and the existing methods for biomass estimation for individual plants are either destructive or lack accuracy. In this study, a hyperspectral imaging system was developed for the accurate prediction of the above-ground biomass of individual rice plants in the visible and near-infrared spectral region. First, the structure of the system and the influence of various parameters on the camera acquisition speed were established. Then the system was used to image 152 rice plants, which selected from the rice mini-core collection, in two stages, the tillering to elongation (T-E) stage and the booting to heading (B-H) stage. Several variables were extracted from the images. Following, linear stepwise regression analysis and 5-fold cross-validation were used to select effective variables for model construction and test the stability of the model, respectively. For the T-E stage, the R(2) value was 0.940 for the fresh weight (FW) and 0.935 for the dry weight (DW). For the B-H stage, the R(2) value was 0.891 for the FW and 0.783 for the DW. Moreover, estimations of the biomass using visible light images were also calculated. These comparisons showed that hyperspectral imaging performed better than the visible light imaging. Therefore, this study provides not only a stable hyperspectral imaging platform but also an accurate and nondestructive method for the prediction of biomass for individual rice plants.
The Review of scientific instruments 09/2013; 84(9):095107. DOI:10.1063/1.4818918 · 1.61 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Threshing is an essential pretreatment in rice yield-related traits evaluation and rice thresher is an important study of agricultural machinery automation. However, traditional threshers aim at threshing filled grains with simple function, which are inapplicable to high-precision and automatic yield-related traits evaluation of rice. And the conventional threshing method for the traits evaluation is still manual. To improve it, we developed a whole-feeding and automatic rice thresher for single plant. The thresher adopted a hierarchical multi-roller rolling method to thresh filled and unfilled grains respectively. A fish scale sieve plate was designed to separate the grains from the straw and the whole system was controlled by Programmable Logic Controller (PLC) automatically. To evaluate the threshing system, two batches of rice plants were tested, and the results showed that the thresher had the advantages of high precision, low breakage and no residue. Thus, the thresher provides strong support for high-precision and automatic yield-related traits evaluation of rice.
[Show abstract][Hide abstract] ABSTRACT: Tiller number is highly correlated with grain yield in wheat. Traditional observation of wheat tiller number is still manual. Previously, our group developed a high-throughput system for measuring automatically rice tillers (H-SMART) based on X-ray computed tomography (CT), providing high accuracy for measuring rice tillers. However, the time-consuming reconstruction, which is necessary to generate tomographic images, limits the throughput improvement of system as well as the CT potential for the real-time applications. In order to accelerate the reconstruction process, we present an adaptive minimum enclosing rectangle (AMER) method to reduce the number of reconstructed pixels from the full field of view (FOV) and apply parallel processing using Graphics Processing Unit (GPU). The reconstruction time and speedup with different methods were discussed. Compared to the AMER method, GPU technique improved reconstruction with a higher speedup of approximately 200 times. And the speedup with AMER method was determined by two factors: area ratio of AMER and FOV, and the longest distance between the vertices of the AMER and the rotation center. Besides reconstruction, tiller identification could also be accelerated by AMER. Moreover, the tiller measurement accuracy did not decrease. With the combination of AMER and GPU, the entire tiller inspection time for a pot-grown plant was reduced from about 11870 ms to less than 200 ms. In sum, the optimized method met the requirement of real-time imaging and expanded CT application in plant phenomics and agriculture photonics.
Computers and Electronics in Agriculture 07/2012; 85:123–133. DOI:10.1016/j.compag.2012.04.004 · 1.76 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.