[Objective]Convenient and accurate diagnosis of crop nitrogen(N)status is the key to achieve precise crop fertilization and rational utilization of N resources.In recent years,the application of digital cameras and other tools in crop nutrition diagnosis has attracted wide attention.In this study,the smart phone cameras were used to obtain maize canopy images,and nitrogen nutrition diagnosis technology based on mobile phone cameras was established and improved.The reliability of traditional image mean method and histogram method for nitrogen nutrition diagnosis was compared to find out the best model for nitrogen nutrition diagnosis of summer maize.[Method]Based on the experiment of N fertilizer amount in the field,the canopy image of summer maize at jointing stage was obtained by smartphone camera.Six color indices,including G/R,G/B,NRI[R/(R+G+B)],NGI[G/(R+G+B)],NBI[B/(R+G+B)]and(G-R)/(R+G+B),were extracted from summer maize canopy images,and the histogram sensitive interval were established,respectively,to analyze their relationship with leaf N content and yield of maize.The determination coefficient(R2)and root mean square error(RMSE)were used to determine the relationship between the mean color index model and the histogram model.Mean absolute percentage error(MAPE)was used to simulate and estimate the stability and accuracy of leaf N content and yield in maize compared with different index models.Then,the N nutrition diagnosis model based on mobile phone camera acquisition of summer maize canopy images was established.[Result]N application significantly affected leaf N content,yield,canopy image hue and vegetation coverage of maize.The peak b of the histogram changes with the increase of leaf N content.Compared with the mean color index method in canopy images,the index histogram method was suitable for N diagnosis among different varieties.The color index(G-R)/(R+G+B)histogram could better reflect crop coverage and overall color information.The index histogram also showed a good correlation with leaf N content and yield.Based on the neural network model to validate the accuracy evaluation indicators of the dataset,the MAPE and RMSE values of leaf N content and yield in maize in the exponential histogram model were lower than those in the exponential mean model,and the R2 reached 0.753,which was greater than that in the exponential mean model.The validation results of the exponential histogram model showed a MAPE value of 5.80%and an RMSE value of 0.07,indicating high estimation accuracy and strong generalization ability.The results indicated that the color parameter index histogram of canopy images had higher accuracy and stronger robustness in estimating leaf N content and yield,and could effectively utilize the characteristics of maize leaf coverage,color,etc.,with good stability.[Conclusion]Therefore,the neural network model established by using smartphones to obtain digital images of maize canopy and combining them with the color index histogram method of canopy images has good application effects and improves estimation accuracy.As a new method,it has good potential in rapid and non-destructive diagnosis of maize N nutrition and precise fertilization.
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