Hyperspectral estimation of phosphorus content for apple leaves based on the random forest model

Abstract:【Objective】Qixia in Shandong provence, China is a widespread area designated for apple dis-tributing and planting, and it is honored as the township of apples due to its high yield. Apple growth is de-pendent on key nutrients such as N, P, K and so on, therefore, nutrient diagnosis and growth monitoringplay a crucial role in precision agriculture. Among the key nutrients, phosphorus(P) is one of the neces-sary nutrient elements to sustain the growth of the crop, it participates directly and indirectly in many dif-ferent ways in the plant metabolism process, photosynthesis and the energy transfer process. The increas-ing use of fertilizer helps to raise the yield of the farm crop, but often generates a lot of fertilizer waste be-cause of the inappropriate amount in use and the absurdity method of applying fertilizer. Much worse, overinputting of chemical fertilization is the main source of water pollution, and this leads to a worsening eco-logical environment. Thus, the accurate diagnosis of crop nutrition is significant to the increase of cropyield and environmental protection. In order to rapidly, accurately and nondestructively evaluate the P-content of apple trees, hyperspectral remote sensing technology has emerged in response to the needs ofthe times.【Methods】Traditional methods of obtaining P-content of apple leaves, i.e., sulfuric acid hydro-gen peroxide and boiled-molybdenum antimony colorimetric methods, require grinding leaves and apply-ing chemical titrations, which would be harmful to the apple trees and also very time-consuming. Hyperspectral remote sensing technology has the advantage of high spectral resolution, strong wavelength conti-nuity, and a large amount of spectral information, which is widely used in all kinds of plant diseases andinsect pests' prevention, chlorophyll content prediction, crop yield forecast and crop nutrient elementsmonitoring etc. The diagnostic data were from 100 new shoots of the prosperous long-term‘Red Fuji'ap-ple trees of 25 orchards in the Qixia area of Yantai city, which included P-content and hyperspectral re-flectance of the apple tree leaves. The apple leaves original spectral reflectivity data were captured by theASD Field Spec4 hyperspectral spectrometer, and the P-content were measured by the traditional chemi-cal analysis in the laboratory. There was a certain degree of differences in the correlation analysis of the P-content with spectral data, in order to increase the accuracy of the P-content estimation for the appletrees, the data was transformed through the first derivative of the original spectral reflectivity, vegetationindex and hyperspectral characteristic parameters, then the stepwise regression analysis method was usedto select the sensitive wavelengths and spectral parameters and establishe the P-content estimation mod-els. According to the results of the systematic analysis and diagnosis, the random forests model methodacts as a non-parametric regression technique, which analyzes the importance of each independent vari-able on the dependent variables and has a good adaptability for complex data.【Results】Hyperspectralcurves of the original reflectance for the apple leaves was distinctly differential. First, the apple leaf reflec-tance is low in the visible region(400-760 nm), because of the pigment absorption, where there is a greenpeak(550 nm) that is a strong reflection edge of chlorophyll and a red valley(667 nm) that is a strong ab-sorption of chlorophyll; Second, the range spectral reflectivity has risen sharply in 680-800 nm region,where the strong absorption is steep and approximately close to linear form; Third, the strong reflectionwave platform is presented in the near infrared region(800-1 300 nm), which is related to the strong infra-red reflectance of the cavernous cavity. Finally, the reflectivity curve fluctuates widely in 1 300-2 500 nm, mainly associated with the strong absorption of water and carbon dioxide. By the correlation analysis,the P-content negatively correlated with the original spectral reflectivity(350-2 500 nm range) as a whole,and the correlation coefficient reached to a remarkable level(R=-0.648 5), especially in the green light re-gion(507-590 nm), red light region(694-743 nm) and near-infrared spectrum(1 324-1 364 nm). In orderto improve the correlation coefficients, the correlation analysis were carried out with the first derivative oforiginal spectral reflectivity, vegetation index and hyperspectral characteristic parameters respectively.The remarkable wavelengths from the first derivative of the original spectral reflectivity were FDR523,FDR1879, FDR567, FDR1883, FDR701, FDR1980, FDR1876, and FDR2024. The remarkable wave-lengths from the vegetation index were DVI(556, 712), RVI(705, 937), DVI(677, 1 728), RVI(542, 1 094),DVI(FDR 567, FDR 1 980), NDVI(937, 549), and DVI(FDR 523, FDR 1 883). The remarkable wave-lengths from the hyperspectral characteristic parameters were Db, Dy, Dr, Rr, Rg, SDb, SDy, SDr, SDg,Rg/Rr,(Rg-Rr)/(Rg+Rr), SDr/SDb,(SDr-SDb)/(SDr+SDb), and(SDr-SDy)/(SDr+SDy). The effect of therandom forests model was good for estimation of the P-content of the apple trees, including the determina-tion coefficients between the measured value and estimated value which were greater than 0.82, the rootmean square errors were below 0.016 4, the relative error were close to 6.915, which demonstrated thatthe random forests model estimation had a higher accuracy. By this analysis, the random forest modelbased on remarkable wavelengths from the vegetation index had the best estimation, including the determi-nation coefficient R2=0.923 6, root mean square error RMSE=0.015 8 and relative error RE=6.915 0%.The leaving data is simulated to validate the model and the results compared with the measured value andestimated value is in good agreement. By the verification analysis, the random forest model based on re-markable wavelengths from the vegetation index had the best estimation, including the determination coefficient R2=0.868 5, root mean square error RMSE=0.011 8 and relative error RE=5.85%.【Conclusion】Compared with the traditional measurement method, hyperspectral remote sensing technology does increase the accuracy and timeliness of the P-content measured value using the random forest model. The study demonstrated the importance of the non-linear random forest model for estimation of the P-content which is negatively correlated with the hyperspectral data. The analysis results will provide a theoreticalguidance and technical support for diagnosis of the nutritional status of the orchards.