Spectral Characteristics Of Rice Damaged By Nilaparvata Lugens (St(?)l) And Cnaphalocrocis Medinalis(Guen(?)e)

Insect and disease pests are one the factors to restrict the development of agricultural production. According to the survey of FAO, the worldwide economic losses caused by insect and disease pests reached to 120 billion dollars every year. Accurate monitoring and forecasting can guide the effective management and control of insect and disease pests, and reduce the economic losses. Remote sensing technology can objectively, accurately, and duly get the information of the ground crop's ecological environment and growth conditions, and indirectly monitor the number and damage degree of the insect and disease pests. Traditionally, assessment and monitoring of diseases and insect pests in plants is being done by visual approach, relying upon the human eye and brain, which is time-consuming, labour intensive, and the accuracy affected by the professional level of observer. Hyperspectral remote sensing is a fast and lossless technique, which can make up the shortcoming of the traditional techniques. Therefore, using remote sensing technology to monitor and forecast insect and disease pests is an important development direction of agricultural remote sensing.In this paper, we focus on the brown planthopper Nilaparvata lugens (Stal) (BPH) and rice leaf roller Cnaphalocrocis medinalis (Guenee) (RLR), and use the hyperspectral remote sensing, to study on the spectral characteristics of rice at different growth stages, the other plants in the rice field and non-biological ground objects. The spectrum of leaf and canopy of rice infested by BPH and RLR was also measured and the monitoring models to forecast the number of BPH and the damage level of RLR were established based on the spectral characteristics. The results are as follows:The results of reflectance features of different ground objects showed that there are obvious "peak" and "valley" features of the spectral curve of healthy rice, which is significantly different from the dead grass, concrete ground, pool, bare soil and other non-green plants. The spectral curve of healthy rice has a small reflection peaks in the green wave band (520-600nm). The reflectance increased sharply in the 680-750nm region, and it reached up to the maximum in 700-1000nm. There were no obvious "peak" and "valley" features in the dead grass spectral curve, and it gradually increased as a smooth straight line from visible band to near-infrared band. The spectral curve of concrete ground was the highest than that of the other objects in the visible band, but it was significantly lower than rice in the near-infrared band. The spectrum of the pool was approximated to a low level of linear. Reflectance curve of the bare soil was similar to rice in the shape, but the reflectances in the visible band were higher than these of rice, while significantly lower than rice in the near-infrared band. The first derivative of spectral reflectance could exhibit more clearly the changes in spectral characteristics of the different ground objects. There was a very obvious green peak at 510-560nm and a red edge at 680-760nm in the first derivative of spectral reflectance of rice, but the first derivative of spectra of all these ground objects, dead grass, concrete ground, pool and bare soil only showed a low curve, and no or not obvious green peak and red edge.The spectral reflectance analysis of the rice on booting stage, heading stage corn, pod of soybean, duckweed and humulus scandens, indicated that the spectral reflectance and location of the peak and valley among the different plants were significantly different. The spectral reflectance of soybean, corn and humulus scandens were the highest at the near-infrared band, followed by rice, but duckweed was the lowest. In visible band, the spectral reflectance of duckweed was the highest, followed by corn and humulus scandens, while rice was the lowest. The spectral reflectance of the indica rice was slightly higher than that of the japonica rice at the same growth period.Spectral characteristics of rice at different growth stages showed that before the heading stage of rice, the spectral reflectance increased gradually at visible band with the advance of the growth stage, and decreased in the near infrared band. But after the heading stage, spectral reflectance decreased gradually at visible band, and increased at the near-infrared band with the advance of the growth stage. The red edge location of the rice canopy moved to the longer band called "Red transference", and then moved to the shorter band called "Blue transference" as the growth stages of rice developed. Ratio vegetation index RVI and normalized difference vegetation index NDVI of rice increased from transplanting stage to heading stage, and decreased from heading stage to yellow ripeness stage. Canopy spectral reflectance and vegetation index of rice could distinguish well not only the rice from the other plants, but also the different types and growth stages of rice.The spectrum of rice seedling infested by brown planthopper was measured in the laboratory. The results showed that the spectral reflectances at the range of visible light and near infrared regions decreased significantly with the increase of the number and instar of BPHs. The damage degrees of rice plants caused by the BPH nymphae with different numbers or stars, and by the oviposition behaviour of adult were expressed well by the spectral reflectance in the near-infrared wavelengths. The reflectance was negatively correlated with the number of BPHs, and the correlation coefficients were significant at the range of wavelengths of 520-570nm and 700-1000nm. The red edge slope (Dλr) and red edge area (Sλr) of the reflectance also significantly correlated with the number of nymphae. The linear models for forecasting the occurrence number of BPHs were built using the following relative indexes to the undamaged plants:the spectral reflectance in the wavelengths of 550nm (R550) and 760nm (R760), and the red edge indexes (Dλr and Sλr). The accuracy of the models was 53%-79% for the 19 times tests. The factor of R760 was efficient for forecasting the number of BPHs. The chi-square test demonstrated that the coincidence rate between the real values of the 5 series of number of BPH and the calculated numbers by models achieved 80%-100%.The canopy spectral characters of rice were measured and analyzed in the filling stage and yellow ripeness stage, respectively, after being inoculated different amounts of BPH at the booting stage. The results showed that in the filling stage, canopy spectral reflectance significantly decreased at 760-1000nm of near-infrared region with the increase of BPH, and there was also a decreased trend in the visible band. The correlation between canopy reflectance and the number of BPH showed that 725-1000nm was the sensitive wave brands for monitoring BPH in the filling stage. In the yellow ripeness stage, the canopy reflectance of rice increased in the visible region as the number of BPH increased, especially in the red band region (620-720nm). So the wave brands 610-700nm could be considered as the sensitive band to monitor the number of BPH at the yellow ripeness stage of rice.Spectral reflectances of the canopy, stem and single leaf of rice plant infested by various numbers of BPH were measured at the tillering stage. Results showed that canopy reflectance decreased at the green and near-infrared region, while it increased at blue and red bands with the increase of BPH. The sensitive wave band to BPH under the level of canopy was 729-1000nm. The spectral reflectances of the single leaf and stem in the whole bands were decreased with the increase of the number of BPH. According to the results of the correlative analysis between reflectance and the number of BPH, the sensitive wave bands to BPH under the level of flag leaf, next-to-last and stem were 471-589nm and 660-100nm,489-563nm and 708-100nm,527-569nm and 699-1000nm, respectively.The spectral vegetation index of the rice showed that canopy spectrum DVI, RVI, and NDVI decreased significantly with the increase of BPH. All of these indexes had a significant correlation with the number of BPH. The spectrum DVI index of leaves and stems was significantly correlated to the amount of BPH, but there was no significant correlation between the number of BPH and RVI or NDVI. Spectral vegetation index RVI and NDVI would be used to monitor the BPH on the canopy level, but they were unsuitable for the single leaf and stem level.The relationship between the rice yield and the number of BPH at the booting stage of rice was very significant. The weight of a spike and 1000-seed of rice significantly decreased with the increase of BPH. There was significantly correlation between rice yield and the spectral parameters (R696, Dλy,μr, Dμr and Sμr) in yellow ripeness stage. The models to assess the yield of rice were established based on the spectral parameters. The two modelsY3=0.143kr-99.272 and Y3=383.121Dμr+1.931 were efficient to monitor the 1000-seed weight (Y3) of rice under different amount of BPHs.Canopy and single leaf reflectance of rice infested by RLR were measured. The results indicated that the spectral reflectance of rice canopy decreased in the near-infrared regions with the increase of the damage degree by RLR.738-1000nm was the sensitive wave-length band which could reflect the impaired level of rice canopy. The spectral reflectance of the roll leaf increased in the red band regions with the increase of the rate of roll leaf in the plot of rice, and 582-688nm was the sensitive band to reflect the impaired level of rice leaf-roll. The spectral reflectance of the developed blade leaf in the damaged plot by RLR decreased in the visible band and near-infrared regions with the increase of the rate of the roll leaf. The sensitive wave bands to RLR under the level of developed blade leaf were 512-606nm and 699-1000nm. First derivative of canopy spectral reflectance showed that location of the red edge (λr) moved to the short band side. Dλr and Sλr decreased with the increase of infestation level of leaves. The Dλr and Sλr were significantly correlative to the infestation level of rice. These two indexes could be used to detect the damage degree of rice by RLR. Prediction models based on the spectral characteristics of rice were established to forecast the damage levels by RLR. At the canopy level, Y=-1338.406DAλr+11.123 was the best model, whereas at the developed blade leaf level, Y=-90.280R550+11.902 was the best one, and the model was Y=198.620R670-18.505 at the damaged leaf-roll leaf level. All these models were tested through the 0.01 level, and they could be used to monitor the RLR.