Detection Of Cnaphalocrocis Medinalis G(?)en(?)e And Nilaparvata Lugens (St(?)l) Damage In Rice Using Spectral Data

The spectral reflectance from the leaves and canopy of rice damaged by rice leaf folder (RLF) Cnaphalocrocis medinalis (Guenee) and brown planthopper (BPH) Nilaparvata lugens (Stal) were measured in plot and field at different growth stages (tillering, booting and flowering) using ASD Hand-held Spectroradiometer. The linear models for diagnosing leaf-roll rate and the number of BPH based on spectral reflectance and radial basis function (RBF) neural network method were established to supply some effective method for monitoring RLF and BPH in rice field. The main results were list as followings.(1) At tillering stage, the reflectance of rice leaf decreased at530-570nm and700-1050nm with the increase of RLF damage, whereas it increased at400-420nm and610-700nm. At booting stage, spectral reflectance of rice leaf decreased at426-475nm,496-630nm and690-1000nm and it increased at659-678nm with the increase of RLF damage. At flowering stage, the spectral reflectance of rice leaf increased at632-691nm and decreased at510-616nm and697-1000nm.The leaf-roll rates were simulated by combining different pieces of injured and healthy leaves at booting and flowering stage of rice on a black cloth in laboratory and reflectance from the combined rice leaves was measured. The results indicated that spectral reflectance decreased at530-570nm and700-1000nm and increased at450-500nm and610-700nm with the increase of leaf-roll rates.Canopy reflectance from a plot of rice decreased significantly at717-1000nm at booting, and it decreased at530-600nm and717-1000nm at flowering stage as the infestation scales of RLF increased.In rice field, canopy reflectance of rice decreased significantly at515-596nm and698-1000nm at tillering stage as the leaf-roll rates increased. At booting stage, spectral reflectance increased at664-684nm and decreased at714-1000nm. At flowering stage, spectral reflectance increased at623-692nm and decreased at725-1000nm as the leaf-roll rates increased. (2) Linear regression models based on spectral indices were established for detecting the damage degrees of rice at different grow stages. At tillering stage, The model based on the reflectance at741nm was better, and its accuracy was90%for100times of prediction. At booting stage, the model based on NDVI was better which had76%accuracy for86times of prediction. At flowering stage, the model based on Rgreen/Rred was the better which accuracy was69%for80times of prediction.Linear regression models for diagnosing the leaf-roll rates of combined leaves of rice were built. For the tillering stage leaves, the model based on Syellow was better, and its diagnostic accuracy was86%for14times prediction. For the booting stage leaves, the model based on Sred was better, and the diagnostic accuracy was79%for14times prediction.Linear Regression models based on spectral indices were built using multiple stepwise regression (MSR) method to detect infestation scales (Y,0-5) in plot of rice. At booting stage, the model Y=14.47-0.60DVI542.500-142.98RVSI-6254.44FD540-16.76(Sred-Sblue) was better and it root mean square error (RMSE) was0.216. At flowering stage, the model Y=8.7356-44.7232DVI730-670+40.3448TCARI+539.1316FD715+44.3701SD695was better with a smaller RMSE of0.3035.In rice field, diagnostic methods for leaf-roll rates were established using the RBF neural network method based on18spectral indices and reflectance at400-1000nm after reducing dimensions by MSR, factor analysis, and partial least squares (PLS) method. Factors or spectral indices entered into the RBF neural network as the input vectors, and the leaf-roll rates entered into RBF neural network as the target vectors. After31,23,27times prediction at tillering, booting and flowering stage, the predicting correction rate by the built network based on these factors from MSR was61%,65%and63%, respectively, based on the principal factors was68%,69%and63%, based on PLS factors was74%,78%and70%, and based on18spectral indices was81%,83%and78%, respectively.(3) When rice was infested by BPH, reflectance from leaves increased at570-690nm and decreased at700-1000nm at booting stage of rice, and at flowering stage, reflectance decreased significantly at710-1000nm regions. At canopy-level of rice, reflectance decreased at720-1000nm and increased at484-515nm and570-707nm at joint-booting stage of rice. At booting stage, reflectance decreased at525-565nm and700-1000nm, and at flowering stage, it decreased significantly at725-1000nm.Linear regression models for diagnosing the number of BPH infested rice were established based on the reflectance indices from rice leaves. At the booting stage (the number of BPH was0-106.25), the better models were Y=381.928-135.891CHl Index and Y=-71.254-2842.01NPQI with RMSE17.3and17.33, respectively. At the flowering stage (the number of BPH was0-17.5), the model based on DMRnir-green (Y=33.162-199.721DMRnir-green) was better with RMSE3.37. The multiple stepwise regression models based on all these25spectral indices were built, and the Y=174.831-79.361Chl Index-1655.217NPQI was better to detect the number of BPH at booting stage of rice, and its RMSE was13.71. The model Y=-39.903+491.616D MRnir-green-791.617DMRnir-red+120.714G(Lich) was better to detect the number of BPH at flowering stage with RMSE of2.87.At canopy-level, linear regression models for diagnosing the number of BPH (7) were built. The model based on MRnir was better to diagnose the number of BPH on joint-booting stage and booting stage of rice and their RMSE were18.22and19.28, respectively. At flowering stage of rice, the model based on DVI935-670was better with3.81RMSE. The stepwise regression models for diagnosing the number of BPH at joint-booting, booting and flowering stage of rice using all25indices were Y=179.85-1481.037MRnir+2341.975DVI730-542,Y=353.754-472.028MRnir-108.122WI+88.052DVI542-500, and Y=158.46-1.251WI-65.499DVI935-670-133.89AI, which had RMSE15.06,10.6and3.29, respectively.The diagnostic method for the number of BPH in rice based on RBF were studied using the MSR, factor analysis, and PLS to reduce the dimensions of reflectance at400-1000nm and25spectral indices. For6,7,7times prediction of BPH numbers at joint-booting, booting and flowering stage, the correction rates of RBF network built here based on factors from MSR were67%,57%and57%, respectively, and they were50%,57%and57%based on principal factors, and they were83%,71%and71%based on factors from PLS. The predicting correction rates of RBF network based on25spectral indices were83%,71%and71%for BPH numbers at joint-booting, booting and flowering stage, respectively.(4) Reflectance and SPAD readings were measured in rice using different levels of nitrogen-fertilizer and infested by different number of BPH. The results showed that the reflectance from rice canopy at790-1000nm and SPAD values from leaves increased significantly, and they decreased at650-690nm regions as N fertilizer rates increased. In the pots used low N fertilizer rate, Canopy reflectance of rice in the near-infrared region and SPAD value from leaves decreased significantly as the number of BPH increased when the BPH infested for14days, but in the high N fertilizer rate, canopy reflectance of near-infrared region decreased significantly when BPH infested for35days. SPAD value of the fourth fully expanded leaf (4LFT) had significant correlation with the number of BPH, and the relative ratio between SPAD reading of the second full expanded leaf (2LFT) and the fourth fully expanded leaf (4LFT) was relatively insensitive to N fertilizer rate but was sensitive to the number of BPH in rice used different N fertilizer rates. Spectral indices and SPAD index had great potential for detecting BPH damage in rice which was used different N fertilizer.