Modern breeding technologies are capable of producing hundreds of new varieties daily, so fast, simple and effective methods for screening valuable candidate plant materials are urgently needed. Final yield is a significant agricultural trait in rice breeding. In the screening and evaluation of the rice varieties, measuring and evaluating rice yield is essential. Conventional means of measuring rice yield mainly depend on manual determination, which is tedious, labor-intensive, subjective and error-prone, especially when large-scale plants were to be investigated. This paper presented an in vivo, automatic and high-throughput method to estimate the yield of individual pot-grown rice plant using multi-angle RGB imaging and image analysis. In this work, we demonstrated a new idea of estimating rice yield from projected panicle area, projected area of leaf and stem and fractal dimension. 5-fold cross validation showed that the predictive error was 7.45%. The constructed model achieved promising results on rice plants grown both in-door and out-door. The presented work has the potential of accelerating yield estimation and would be a promising impetus for plant phenomics.

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Computer and Computing Technologies in Agriculture VIII

In order to shorten the revisit period, and improve the efficiency of imaging, currently, all the in orbit high resolution remote sensing satellites adopt side-view imaging technology. When satellites imaging with a side perspective, it would inevitably cause the degradation of image quality, like spatial resolution reduction, image deformation incensement, and the reduction of positioning accuracy. In addition, with the increase of the side perspective, the error of DEM data will be amplified in the process of ortho-rectification, which, thus, will bring bigger error for image mapping accuracy. To address this problem, embarked from the impact of side view and DEM precision on image point error, this paper used Quick bird image data of the same area but different side perspectives, and simulate the impacts of different DEM elevation error on different side view image. At last we put forward a method to determine the maximum side perspective with map precision constraints, and set up a conversion relationship among satellite side view, DEM error and image point error. Finally, the paper combines the errors of 1:50000 DEM, ASTERGDEM and the requirement of ortho-image drawing, discusses and gives the values of maximum side perspective in the mountains and plains.

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A Method to Determine the Maximum Side Perspective of Satellite with the Constraints of Mapping Accuracy

Based on the principle of Ortho-Rectification,a theoretical estimation formula of Ortho-Rectification with no GCPs is presentedWith this formula,the precision of the Ortho-Rectification image with the SRTM DEM data is estimatedThe result shows that Ortho-Rectification Images produced by HRG image with the support of SRTM DEM have high accuracyUsing this method the DOM of Hangzhou,Chongqing and Daqing region are produced,and their accuracy are evaluated with 1∶ 10 000 topographic mapsThe results prove that the theoretical estimation formula is correct and the precision of the ortho-rectification images meets 1∶ 50 000 mapping accuracy

Rao Yan-wei

Papers

The SPOT HRG images ortho-rectification experiment and precision analysis without GCPs