Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

Plant Methods

Minghua Li¹  Xi Lu¹  Fuyu Ma¹  Yang Liu¹  Shuai Wen¹  Baoxia Ci¹  Ming Wen¹  Xiaokang Feng¹  Zhiqiang Dong¹ 

[1] School of Agriculture, Shihezi University, 832003, Shihezi, Xinjiang, People’s Republic of China;National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), 832003, Shihezi, People’s Republic of China;

关键词: Nitrate nitrogen;    Cotton;    Petiole;    Remote sensing;    Wavelet neural network;   

DOI  :  10.1186/s13007-021-00790-x

来源: Springer

【 摘 要 】

BackgroundEstimation of nitrate nitrogen (NO3−–N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3−–N contents in cotton petioles under drip irrigation is of great significance.MethodsIn this study, we discussed the use of hyperspectral data to estimate NO3−–N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3−–N contents were first investigated, after which a traditional regression model for petioles NO3−–N content was established. A wavelet neural network (WNN) model for estimating petiole NO3−–N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP).ResultsBetween the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3−–N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP.ConclusionsAn inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3−–N content estimation in cotton petioles under drip irrigation.

【 授权许可】

CC BY