Rapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristics

According to the mechanism of rice growth,if nitrogen deficiency occurs,not only rice leaf but also sheath shows special symptoms:sheaths become short,stems appear light green,older sheath become lemon-yellowish.Nitrogen nutrition status of rice could be identified based on the differences of color and shape of leaf and sheath under different levels of nitrogen nutrition.Machine vision technology can be used to non-destructively and rapidly identify rice nutrition status,but image acquisition via digital camera is susceptible to external conditions,and the images are of poor quality.In this research,static scanning technology was used to collect images of rice leaf and sheath.From those images,14 color and shape characteristic parameters of leaf and sheath were extracted by R,G,B mean value function and region props function in MATLAB.Based on the relationship between nitrogen content and the characteristics extracted from the images,the leaf R,leaf length,leaf area,leaf tip R,sheath G,and sheath length were chosen to identify nitrogen status of rice by using Support Vector Machine(SVM).The results showed that the overall identification accuracies of different nitrogen nutrition were 94%,98%,96%and 100%for the four growth stages,respectively.Different years of data were used for validation,identification accuracies were 88%,98%,90%and 100%,respectively.The results showed that additional sheath characteristics can effectively increase the identification accuracy of nitrogen nutrition status and the methodology developed in the study is capable of identifying nitrogen deficiency accurately in the rice.

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies,tree stem structure variables(SSVs)proved to be an essential kind of parameters,and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle.For this newly emerging task,satellite imagery such as WorldView-2 panchromatic images(WPIs)is used as a potential solution for co-prediction of tree-level multifarious SSVs,with static terrestrial laser scanning(TLS)assumed as a‘bridge’.The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters,and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models(termed as Model1s and Model2s).In the case of Picea abies,Pinus sylvestris,Populus tremul and Quercus robur in a boreal forest,tests showed that Model1s and Model2s for different tree species can be derived(e.g.the maximum R^(2)=0.574 for Q.robur).Overall,this study basically validated the algorithm proposed for co-prediction of multifarious SSVs,and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling,which is useful for large-scale investigations of forest understory,macroecosystem ecology,global vegetation dynamics and global carbon cycle.