Flexible Multimodal Sensors Enhanced by Electrospun Lead-Free Perovskite and PVDF-HFP Composite Form-Stable Mesh Membranes for In Situ Plant Monitoring.

The pH and humidity of the crop environment are essential indicators for monitoring crop growth status. This study reports a lead-free perovskite/polyvinylidene fluoride-hexafluoropropylene composite (LPPC) to enhance the stability and reliability of in situ plant pH and humidity monitoring. The mesh composite membrane of LPPC illustrates a hydrophobic contact angle of 101.982°, a tensile strain of 800%, and an opposing surface potential of less than -184.9 mV, which ensures fast response, high sensitivity, and stability of the sensor during long-term plant monitoring. The LPPC-coated pH electrode possesses a sensitivity of -63.90 mV/pH, which provides a fast response within 5 s and is inert to environmental temperature interference. The LPPC-coated humidity sensor obtains a sensitivity of -145.7 Ω/% RH, responds in 28 s, and works well under varying light conditions. The flexible multimodal sensor coated with an LPPC membrane completed real-time in situ monitoring of soilless strawberries for 17 consecutive days. Satisfactory consistency and accuracy performance are observed. The study provides a simple solution for developing reliable, flexible wearable multiparameter sensors for in situ monitoring of multiple parameters of crop environments.

Banana Pseudostem Width Detection Based on Kinect V2 Depth Sensor.

This study used Kinect V2 sensor to collect the three-dimensional point cloud data of banana pseudostem and developed an automatic measurement method of banana pseudostem width. The banana plant was selected as the research object in a banana plantation in Fusui, Guangxi. The mobile measurement of banana pseudostem was carried out at a distance of 1 m from the banana plant using the field operation platform with Kinect V2 as the collection equipment. To eliminate the background data and improve the processing speed, a cascade classifier was used to recognize banana pseudostems from the depth image, extract the region of interest (ROI), and transform the ROI into a color point cloud combined with the color image; secondly, the point cloud was sparse by down-sampling; then, the point cloud noise was removed according to the classification of large-scale and small-scale noise; finally, the stem point cloud was segmented along the y-axis, and the difference between the maximum and minimum values in the x-axis direction of each segment was calculated as its horizontal width. The center point of each segment point cloud was used to fit the slope of the stem centerline, and the average horizontal width was corrected to the stem diameter. The test results show that the average measurement error is only 2.7 mm, the average relative error was 1.34%, and the measurement time is only about 300 ms. It could provide an effective solution for the automatic and rapid measurement of stem width of banana plants and other similar plants.

Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis.

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.

Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy.

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.

Development and Application of a Vehicle-Mounted Soil Texture Detector.

It is of great significance to obtain soil texture information quickly for the realization of farmland management. Soil with good particle condition can well regulate the needs of plants for water, nutrients, air, and temperature during crop growth, thereby promoting high crop yields. The existing methods of measuring soil texture cannot meet the requirements of time and spatial resolution. For this reason, a vehicle-mounted soil texture detector was designed and developed based on machine vision and soil electrical conductivity devices. The detector does not require pretreatment such as air-drying and screening of the soil, and completely uses the original information of the farmland. The whole process can obtain the soil texture information in real time, omitting the complicated chemical process, and saving manpower and material resources. The vehicle-mounted detector is divided into a mechanical part, a control part, and a display part. The mechanical part provides measurement support for the acquisition of soil texture information; the control part collects and processes signals and images; the measurement results can be intuitively observed and recorded on the display, and can be operated through the mobile phone. The vehicle-mounted detector obtains soil conductivity through 4 disc electrodes, while the vehicle-mounted industrial camera captures the soil surface image, and extracts texture parameters through image processing, takes EC and texture parameters as input, and the embedded SVM model of the instrument was used to perform soil texture prediction. In order to verify the measurement accuracy of the detector, farmland verification experiments were carried out on farmland loam in Tongzhou District and Haidian District of Beijing. The R2 of the correlation between the measured value of soil EC and the actual value was 0.75, and the accuracy of soil texture prediction was 84.86%. It shows that the developed vehicle-mounted soil texture detector can meet the requirements for rapid acquisition of farmland texture information.

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization.

Potato is the world's fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

Optimal fertigation for high yield and fruit quality of greenhouse strawberry.

Nitrogen (N), phosphorus (P), potassium (K), and water are four crucial factors that have significant effects on strawberry yield and fruit quality. We used a 11 that involved 36 treatments with five levels of each of the four variables (N, P, and K fertilizers and water) to optimize fertilization and water combination for high yield and quality. Moreover, we used the SSC/TA ratio (the ratio of soluble solid content to titratable acid) as index of quality. Results showed that N fertilizer was the most important factor, followed by water and P fertilizer, and the N fertilizer had significant effect on yield and SSC/TA ratio. By contrast, the K fertilizer had significant effect only on yield. N×K fertilizer interacted significantly on yield, whereas the other interactions among the four factors had no significant effects on yield or SSC/TA ratio. The effects of the four factors on yield and SSC/TA ratio were ranked as N fertilizer > water > K fertilizer > P fertilizer and N fertilizer > P fertilizer > water > K fertilizer, respectively. The yield and SSC/TA ratio increased when NPK fertilizer and water increased, but then decreased when excessive NPK fertilizer and water were applied. The optimal fertilizer and water combination were 22.28-24.61 g plant-1 Ca (NO3)2·4H2O, 1.75-2.03 g plant-1 NaH2PO4, 12.41-13.91 g plant-1 K2SO4, and 12.00-13.05 L water plant-1 for yields of more than 110 g plant-1 and optimal SSC/TA ratio of 8.5-14.

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343⁻2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

[Research on maize multispectral image accurate segmentation and chlorophyll index estimation].

In order to rapidly acquire maize growing information in the field, a non-destructive method of maize chlorophyll content index measurement was conducted based on multi-spectral imaging technique and imaging processing technology. The experiment was conducted at Yangling in Shaanxi province of China and the crop was Zheng-dan 958 planted in about 1 000 m X 600 m experiment field. Firstly, a 2-CCD multi-spectral image monitoring system was available to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (Blue (B), Green (G), Red (R): 400-700 nm) and near-infrared (NIR, 760-1 000 nm) band. The multispectral images were output as RGB and NIR images via the system vertically fixed to the ground with vertical distance of 2 m and angular field of 50°. SPAD index of each sample was'measured synchronously to show the chlorophyll content index. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the NIR maize image was selected to segment the maize leaves from background, because there was a big difference showed in gray histogram between plant and soil background. The NIR image segmentation algorithm was conducted following steps of preliminary and accuracy segmentation: (1) The results of OTSU image segmentation method and the variable threshold algorithm were discussed. It was revealed that the latter was better one in corn plant and weed segmentation. As a result, the variable threshold algorithm based on local statistics was selected for the preliminary image segmentation. The expansion and corrosion were used to optimize the segmented image. (2) The region labeling algorithm was used to segment corn plants from soil and weed background with an accuracy of 95. 59 %. And then, the multi-spectral image of maize canopy was accurately segmented in R, G and B band separately. Thirdly, the image parameters were abstracted based on the segmented visible and NIR images. The average gray value of each channel was calculated including red (ARed), green (AGreen), blue (ABlue), and near-infrared (ANIR). Meanwhile, the vegetation indices (NDVI (normalized difference vegetation index), RVI (ratio vegetation index); and NDGI(normalized difference green index)) which are widely used in remote sensing were applied. The chlorophyll index detecting model based on partial least squares regression method (PLSR) was built with detecting R2=0. 5960 and predicting R2= 0. 568 5. It was feasible to diagnose chlorophyll index of maize based on multi-spectral images.

[Rapid Detection of Trace Dimethoate Pesticide Residues Based on Colorimetric Spectroscopy].

In order to detect dimethoate pesticide residues rapidly and safely, a feasible method based on colorimetric spectroscopy was developed. Because dimethoate is one of organophosphorus pesticides containing sulfur, its sulfenyl can react with Pd2+ to produce a yellow complex named palladium sulfide. PdCl2 was used as the color agent, which was dissolved in acetic acid instead of the common concentrated hydrochloric acid. The dimethoate solution was prepared by dissolving the commercial pesticides into distilled water at different concentrations. The pesticide samples were reacted with the same amount of PdC2 solution respectively. The absorbance spectra of the samples after coloring reaction were measured in the region of 300-900 nm by a spectrophotometer. The result showed that the effect of using acetic acid instead of concentrated hydrochloric acid was not only safe but also preferable, and 0.5 mg x kg(-1) was the minimum concentration of the pesticide that could be distinguished in the spectra. The result met the pesticide residue detecting requirements of part fruits and vegetables in the national standard GB2763-2012 regulations. Further studies on random 40 dimethoate samples from 0.5 to 88 mg x kg(-1) were carried out. Thirty samples were randomly selected to establish the training model and remaining 10 samples were used to test the model. The preprocessing methods were carried on the spectrum data such as normalization and smoothing to get a better effect through comparison their prediction results with the correlation coefficient (r) and the root mean square error of cross-validation (RMSEP). The principal component analysis (PCA) method and partial least squares (PLS) method were used to establish prediction models respectively in the different wave ranges. By calculating the correlation coefficient of dimethoate samples in 350-900 nm the maximum of 0.9572 was obtained at wavelength 458 nm, so 453-463 and 400-600 nm were selected as feather regions. Experiments showed that the effect of SG preprocessing on the absorbance spectra in the region of 350-900 and 400-600 nm was obvious, and PLS method were better than PCA method. The optimum model was obtained in the region of 400-600 nm, when principal component number was 4, the training set r=0.9941, RMSEP=2.7703 and the validation set r=0.9933, RMSEP = 2.2148. This method was safe in operation and the colorimetric reaction time was 2 min, which provided theoretical and technical support for further studying on development of rapid, safe organophosphorus pesticide detection instrument.

[Visible-NIR spectral feature of citrus greening disease].

Citrus greening (Huanglongbing, or HLB) is a devastating disease caused by Candidatus liberibacter which uses psyllids as vectors. It has no cure till now, and poses a huge threat to citrus industry around the world. In order to diagnose, assess and further control this disease, it is of great importance to first find a quick and effective way to detect it. Spectroscopy method, which was widely considered as a fast and nondestructive way, was adopted here to conduct a preliminary exploration of disease characteristics. In order to explore the spectral differences between the healthy and HLB infected leaves and canopies, this study measured the visible-NIR spectral reflectance of their leaves and canopies under lab and field conditions, respectively. The original spectral data were firstly preprocessed with smoothing (or moving average) and cluster average procedures, and then the first derivatives were also calculated to determine the red edge position (REP). In order to solve the multi-peak phenomenon problem, two interpolation methods (three-point Lagrangian interpolation and four-point linear extrapolation) were adopted to calculate the REP for each sample. The results showed that there were, obvious differences at the visible & NIR spectral reflectance between the healthy and HLB infected classes. Comparing with the healthy reflectance, the HLB reflectance was higher at the visible bands because of the yellowish symptoms on the infected leaves, and lower at NIR bands because the disease blocked water transportation to leaves. But the feature at NIR bands was easily affected by environmental factors such as light, background, etc. The REP was also a potential indicator to distinguish those two classes. The average REP was slowly moving toward red bands while the infection level was getting higher. The gap of the average REPs between the healthy and HLB classes reached to a maximum of 20 nm. Even in the dataset with relatively lower variation, the classification accuracy of threshold segmentation method by the REP could reach to more than 90%. The four-point linear extrapolation method had slightly better result than the three-point Lagrangian interpolation method. This study provided useful theoretical foundation to detect HLB by spectral reflectance.

[A novel vegetation index (MPRI) of corn canopy by vehicle-borne dynamic prediction].

Ground-based remote sensing system is a significant way to understand the growth of corn and provide accurate and scientific data for precision agriculture. The vehicle-borne system is one of the most important tools for corn canopy monitoring. However, the vehicle-borne growth monitoring system cannot maintain steady operations due to the row spacing of corn. The reflectance of corn canopy, which was used to construct the model for the chlorophyll content, was disturbed by the reflectance of soil background. The background interference with the reflectance could not be removed effectively, which would result in a deviation in the growth monitoring. In order to overcome this problem, a novel vegetation index named MPRI was developed in the present paper. The tests were carried out by the vehicle-borne system on the cornfield. The sensors which configured the vehicle-borne system had 4 bands, being respectively 550, 650, 766 and 850 nm. It would obtain the spectral data while the vehicle moved along the row direction. The sampling rate was about 1 point per second. The GPS receiver obtained the location information at the same rate. MPRI was made up by the reflectance ratio of 660 and 550 nm. It was very effective to analyze the information about the reflectance of the canopy. The results of experiments showed that the MPRI of soil was the positive value and the MPRI of canopy was the negative value. So it is easier to distinguish the spectral information about soil and corn canopy by MPRI. The results indicated that: it had satisfactory forecasting accuracy for the chlorophyll content by using the MPRI on the moving monitoring. The R2 of the prediction model was about 0.72. The R2 Of the model of NDVI, which was used to represent the chlorophyll content, was only 0.24. It indicates that MPRI had good measurement results for the dynamic measurement process. It provided the novel measurement way to get the canopy reflectance spectra and the better vegetation index to construct the prediction model of the contents of chlorophyll.

[Detection of chlorophyll content in water body based on two-dimensional correlation spectroscopy].

Twenty five samples were collected from 10 different ponds in Jiangsu Province of China. According to the different water status and surface area of each pond, different numbers of water samples were collected. The present paper aims to detect chlorophyll content in water body based on hyperspectrum. The visible and near infrared spectral transmittance of the water samples was measured by using a Shimadzu UV-2450 spectrograph. At the same time, the chlorophyll content of each sample was measured using hot-ethanol extraction method in the laboratory. Then the spectral characteristics were analyzed for the water samples and the results showed that with chlorophyll concentration increasing, spectral transmittance decreased gradually. There is an apparent transmission valley at 676 nm. And then two dimensional correlation spectrum technology was used to analyze the sensitive absorption band of chlorophyll in water body. Comprehensive observation of the spectral characteristics of water samples can be carried out much accurately by analyzing two-dimensional correlation spectra of synchronous and asynchronous spectrograms. And the effective spectral response bands of the chlorophyll content were found at 488 and 676 nm. Then the NDWCI (normalized difference water chlorophyll index) was established with the transmittance of red band and blue band. Two regression models were built to predict the chlorophyll concentration in water. One is a multiple linear regression model based on the original transmittances at 488 and 676 nm. The other is the linear regression model based on NDWCI. By comparison, the model based on NDWCI was better. The R2 of its training model reached to 0.7712, and the root mean square error of calibration was 45.5099 mg x L(-1). The R2 of prediction model reached to 0.7658, and the root mean square error of prediction was 39.5038 mg x L(-1). It reached to a practical level to predict the chlorophyll content in water body rapidly.

[Detection of onion soluble solids content based on the near-infrared reflectance spectra].

Onion soluble solids content (SSC) was detected using near-infrared (924-1720 nm) reflectance spectra. Three cultivars of onions, harvested at different period, were selected for experiment and the total number of samples is 268. SSC reference value of onion juice was determined using the temperature compensated refractometer. Some pre-processing methods, such as S-G smoothing, scatter correction, and derivation, were compared to establish a statistical model based on partial least squares regression (PLSR) method. The results show that the avitzky-Golay smoothing with window 32 and span 10 is more efficient. The determination correlation coefficient of prediction R2 is 0.87 and root mean square error (RMSEP) is 2.42 degrees Brix. Compared to the 2nd derivation, the 1st derivation got better prediction result, but the spectra scatter correction is the best (R2 = 0.88, RMSEP of = 2.31 degrees Brix). The optimal prediction (R2 = 0.90, RMSEP = 1.84 degrees Brix and RPD = 3) was built based on crossing validation modeling, which shows that infrared reflectance spectroscopy with scatter correction pre-processing is feasible for onions soluble solids detection.

[Effect of soil moisture on prediction of soil total nitrogen using NIR spectroscopy].

As one of the most important components of soil liutrient, it is necessary to obtain the soil total nitrogen(STN)content in precision agriculture. It is a feasible method to predict soil total nitrogen content based on NIRS. However, the effect of soil moisture content (SMC) on the prediction of STN is very serious. In the present research, the effect of SMC was discussed from qualitative analysis and quantitative analysis by the Fourier spectrum analyzer MATRIX_I. Firstly, sixty soil samples with different STN and SMC were scanned by the MATRIX_I. It was found that the reflectince of soil samples in near infrared region decreased with the increase in SMC. Subsequently, Moisture absorbance index (MAI) was proposed by the diffuse of absorbance at the wavelengths of 1 450 and 1 940 nm to classify soil properties and then correction factor was present Finally, the STN forecasting model with BP NN method was established by the revised absorbance data at the six wavelengths of 940, 1 050, 1,100, 1,200, 1,300 and 1,550 nm. The model was evaluated by correlation coefficient of Rc, correlation coefficient of Rv, root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP) and residual prediction deviation (RPD). Compared with the model obtained from original spectral data, both the accuracy and the stability were improved. The new model was with Rc of 0.86, Rv of 0.81, RMSEC of 0.06, RMSEP of 0.05, and RPD of 2.75. With the first derivative of the revised absorbance, the RPD became 2.90. The experiments indicated that the method could eliminate the effect of SMC on the prediction of STN efficiently.

[Grey analysis of NIR spectra and prediction of nitrogen content in jujube leaves].

Jujube was chosen as the object in the present research. Spectra data of jujube leaves were collected during the period of budding, branch leaf, flowering and coloring. The nitrogen contents of jujube leaf samples were determined by Kjeldahl analysis method. Grey relation analysis between spectral reflectance and nitrogen content of jujube leaves was done based on Grey theory. It was found that the gray relation between spectral reflectance and nitrogen content of jujube leaves at 560, 678 and 786 nm was high. Nine kinds of vegetation index based on spectra data of NIR786, R678 and G570 were calculated. The gray relation of nine kinds of vegetation index was NDVI>GRVI>NDGI>GNDVI>CNDVI>RVI>GDVI>DVI>SAVI. NDVI, GRVI, NDGI, GNDVI and CNDVI were chosen to build prediction models of nitrogen content of jujube leaves. Spectra data of 560, 678 and 786 nm were also used to build prediction models of nitrogen content of jujube leaves. LS-SVM and GM(1, N) were used to build prediction module. The prediction R2 and verification R2 of LS-SVM module were 0.805 and 0.704 respectively when five kinds of vegetation index were used as input of prediction module. When when Spectra data of 560, 678 and 786 nm were used as input, the prediction R2 and verification R2 of LS-SVM prediction model were 0.772 and 0.685 respectively. The prediction R2 and verification R2 of GM(1, N) module were 0.927 7 and 0.895 8 respectively when spectra data of 560, 678 and 786 nm were used as input. The results of prediction GM(1, N) module which used five kinds of vegetation index as input were 0.547 6 and 0.489 7. From those results it was observed that grey theory only needed little information to build prediction module with high precision, so that it could be used in precision management of jujube plants.

[Prediction of chlorophyll content of greenhouse tomato using wavelet transform combined with NIR spectra].

In quantitative analysis of spectral data, noises and background interference always degrade the accuracy of spectral feature extraction. The wavelet transform is multi-scale decomposition used to reduce the noise and improve the analysis precision. On the other hand, the wavelet transform denoising is often followed by destroying the efficiency information. The present research introduced two indexes to control the scale of decomposition, the smoothness index (SI) and the time shift index (TSI). When the parameters satisfied TSI < 0.01 and SI > 0.100 4, the noise of spectral characteristic was reduced. In the meanwhile, the reflection peaks of biochemical components were reserved. Through analyzing the correlation between denoised spectrum and chlorophyll content, some spectral characteristics parameters reflecting the changing tendency of chlorophyll content were chosen. Finally, the partial least squares regression (PLSR) was used to develop the prediction model of the chlorophyll content of tomato leaf. The result showed that the predictiong model, which used the values of absorbance at 366, 405, 436, 554, 675 and 693 nm as input variables, had higher predictive ability (calibration coefficient was 0. 892 6, and validation coefficient was 0.829 7) and better potential to diagnose tomato growth in greenhouse.

[Detection of corn chlorophyll content using canopy spectral reflectance].

The canopy spectral reflectance and chlorophyll content of corn were measured and analyzed under different nitrogen treatments. The correlation between spectral reflectance and chlorophyll content was discussed based on different growth stages and different nitrogen levels. The results showed positive correlations under high and normal nitrogen treatment, while negative correlation under low nitrogen treatment. The relation between reflectance of normal fertilizer region and chlorophyll content was better than others, with r(Normal) > r(High) > r(Low). Normal fertilizer was the best condition to detect the corn chlorophyll content using spectral reflectance. Analysis of the relations at different growth stages showed that on the band of 400-1000 nm the absolute value of correlation coefficient increased and reached the maximum at shooting stage, it decreased until anthesis-silking stage, and then rebounded at milking stage. The positive correlations were found at shooting and milking stage, while negative correlations were found at seedling, trumpet and anthesis-silking stage. It was indicated that the sensitive stages to detect the chlorophyll content were shooting and trumpet stage with high absolute value of correlation coefficient above 0.6 around 550 nm. In order to detect the chlorophyll content of corn, 558, 667, 714 and 912 nm were selected to establish the MLR model and PLSR model. The results showed that PLSR was more capable of building chlorophyll content models reflecting correct relations among multi-variables compared with MLR. In the meanwhile, three wavelengths were selected (558, 667 and 714 nm) to build different vegetation indices such as GDVI, GRVI, GNDVI, DVI, RVI and NDVI. The correlation between DVI and chlorophyll con tent was better than others and DVI was used to establish binomial model and exponential model at shooting stage (R2 = 0.80) and trumpet stage (R2 = 0.66) respectively which was higher than PLSR It also provided a feasible method to detect chlorophyll content non-destructively.

[Monitoring of Cnaphalocrocis medinalis Guenee based on canopy reflectance].

The canopy reflectance of rice was measured in the filed in order to monitor the damaged region caused by Cnaphalocrocis medinalis Guenee. The characteristics of canopy spectral reflectance were analyzed in contrast region and damaged regions. When rice plant was damaged by Cnaphalocrocis medinalis Guenee, the chlorophyll absorption was decreased in the band of 600-700 nm. The canopy reflectance of moderate damage region was lower than that of the contrast region, while the reflectance of severe damage region rice was higher near 550 nm. The canopy reflectance of Cnaphalocrocis medinalis Guenee damaged rice was fluctuant and exhibited the significant peak in the NIR band of 750-770nm. Meanwhile, red edge inflection point as one of the most important spectral parameters was analyzed at different damage levels based on the first derivative of reflectance spectra. The analysis results indicated that red edge inflection position moved to direction of blue light (short wavelength) with the affection severity increasing. Then the modified reflectance of rice canopy was calculated based on zero-mean calculation and standard deviation. It was easy to find the degree of deviation from the average of samples and distinguish the damaged region from experiment plots. The canopy modified reflectance was gently in the contrast region, but changed violently in the affected regions in the band of 750-950 nm. The analysis of Cnaphalocrocis medinalis Guenee affected regions illustrated that the Cnaphalocrocis medinalis Guenee was increased with the increase in severity. The vegetation index was applied in detection of Cnaphalocrocis medinalis Guenee damaged regions because of the composition of multi-wavelength information. The wavelengths 762 and 774 nm were chosen to build detection parameters of Cnaphalocrocis medinalis Guenee such as NIR-RVI, NIR-DVI, NIR-NDVI and KI. The results indicated that the NIR-NDVI could be used to identify the damaged region with contrast region efficiently. The accurate rate of 25 verification samples selected randomly reached 70%. The preliminary studies on rice Cnaphalocrocis medinalis Guenee damaged regions provided a new method to detect the affected regions in the wide area.

[Development of a portable SOM detector based on NIR diffuse reflection].

A portable soil organic matter detector based on near infrared diffuse reflectance was developed. The detector uses a microprocessor 89S52 as the micro controller unit (MCU) and consists of an optical system and a control system. The optical system includes an 850 nm near-infrared LED lamp-house, a lamp-house driving-circuit, a Y type optical fiber, a probe, and a photoelectric sensor. The control system includes an amplifying circuit, an A/D circuit, a display circuit with LCD, and a storage circuit with USB interface. Firstly the single waveband optical signal from the near-infrared LED is transferred to the surface of the target soil via the incidence fibers. Then the reflected optical signal is collected and transferred to the photoelectric sensor, where the optical signal is converted to the electrical signal. Subsequently, the obtained electrical signal is processed by 89S52 MCU. Finally, the calculated soil organic matter content is displayed on the LCD and stored in the USB disk. The calibration experiments using the estimation model of the soil organic matter were conducted. Thirteen kinds of natural soil samples were prepared, each divided into five sub-samples. After measurement, the natural samples were dried under the condition of 105 degrees C for 24 h, and then the same measurements were performed. The analysis of the correlation between the detected SOM content and the measured reflectance was carried out. For the natural soil samples, R2 = 0.907, while R2 reached 0.963 for the dried soil samples. The average reflectance of the five sub-samples from the same kind soil was calculated for each kind of soil. And then the same correlation analysis was conducted, for the natural samples R2 = 0.950, and for the dried samples R2 = 0.982. The results showed that the developed detector is practical. And the soil moisture has an effect on the accuracy of the detector. It is necessary to correct the real time measurement result of the detector based on soil moisture.