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nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

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Journal of Integrative Agriculture 2023年第8期22卷 2536-2552页

作者： PEI Sheng-zhao ZENG Hua-liang DAI Yu-long BAI Wen-qiang FAN Jun-liang Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education Northwest A&F UniversityYangling712100P.R.China

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner.The nitrogen nutrition index(NNI)can quantitatively describe the nitrogen status of crops.Nevertheless,the NNI diagnosis for cotton with unmanned aerial vehicle(UAV)multispectral images has not been evaluated yet.This study aimed to evaluate the performance of three machine learning models,i.e.,support vector machine(SVM),back propagation neural network(BPNN),and extreme gradient boosting(XGB)for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images.The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI.The XGB model performed the best among the three models in predicting nitrogen weight.The prediction accuracy of nitrogen weight at the upper half-leaf level(R^(2)=0.89,RMSE=0.68 g m^(-2),RE=14.62%for calibration and R^(2)=0.83,RMSE=1.08 g m^(-2),RE=19.71%for validation)was much better than that at the all-leaf level(R^(2)=0.73,RMSE=2.20 g m^(-2),RE=26.70%for calibration and R^(2)=0.70,RMSE=2.48 g m^(-2),RE=31.49%for validation)and at the plant level(R^(2)=0.66,RMSE=4.46 g m^(-2),RE=30.96%for calibration and R^(2)=0.63,RMSE=3.69 g m^(-2),RE=24.81%for validation).Similarly,the XGB model(R^(2)=0.65,RMSE=0.09,RE=8.59%for calibration and R^(2)=0.63,RMSE=0.09,RE=8.87%for validation)also outperformed the SVM model(R^(2)=0.62,RMSE=0.10,RE=7.92%for calibration and R^(2)=0.60,RMSE=0.09,RE=8.03%for validation)and BPNN model(R^(2)=0.64,RMSE=0.09,RE=9.24%for calibration and R^(2)=0.62,RMSE=0.09,RE=8.38%for validation)in predicting NNI.The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields,which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

关键词： UAV nitrogen diagnosis leaf nitrogen weight nitrogen nutrition index cotton

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Leaf area index based nitrogen diagnosis in irrigated lowland rice

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Journal of Integrative Agriculture 2018年第1期17卷 111-121页

作者： LIU Xiao-jun CAO Qiang YUAN Zhao-feng LIU Xia WANG Xiao-ling TIAN Yong-chao CAO Wei-xing ZHU Yan National Engineering and Technology Center for Information Agriculture Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center forModern Crop Production/Nanjing Agricultural University Nanjing 210095 P.R. China

Leaf area index （LAI） is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen （N） status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters （e.g., tiller number （TN） and plant height （H）） were investigated simultaneously. Two N indicators of plant N accumulation （NA） and N nutrition index （NNI） were measured as well. A calibration equation （LAI=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=0.808-） was a relatively unbiased variable in the regression than the LAI （R^2=0.33^＊＊）. The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice （NA=29.778LAI-5.9397; NNI=0.7705RLAI＋0.2764）. Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H （LAI=-0.3375（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

关键词： leaf area index rice LAI-2000 nitrogen diagnosis plant characters

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Relationships Between Balance of nitrogen Supply-Demandand nitrogen Translocation and Senescenceof Different Position Leaves on Rice

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Agricultural Sciences in China 2003年第7期2卷 747-751页

作者： WANG Shao-hua, JI Zhi-jun, LIU Sheng-huang, DING Yan-feng and CAO Wei-xing(Key Laboratory of Crop Growth Regulation , Ministry of Agriculture/Nanjing Agricultural University ,Nanjing 210095, P.R.China) Key Laboratory of Crop Growth Regulation Ministry of Agriculture/Nanjing Agricultural University Nanjing 210095 P.R. China

By removing panicles and spikelets, the effects of nitrogen supply-demand balance(NSDB)on nitrogen translocation and senescence of leaves at different positions of the same stem in rice plant were studied. The results showed that under NSDBnitrogen translocation and senescence of the top 4 leaves was normal. nitrogen exported from the top leaves(LFT)especially the top 4 leaves decreased remarkably with increase of NSDB. thus, their MDA contents and differences in color(SPAD value)decreased, while the activities of SOD and CAT increased. Under NSDB>0(panicle removal), N accumulation of the top 4 leaves increased significantly, and the leaf senescence process was delayed. The results also indicated that the color of the 4th LFT was mainly affected by NSDB among the top 4 leaves and was lighter than the 3rd LFT when nitrogen supply was insufficient for the sink need.

关键词： Rice Leaf position nitrogen diagnosis Leaf senescence Sink regulation

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