Minzan Li

Soil nitrogen content forecasting based on real-time NIR spectroscopy

The NIR spectral absorption characteristics were analyzed for the soil samples in different layers including topsoil, subsoil and bottom soil.RSNR (relative signal-to-noise) was adopted to evaluate the filtering effect of wavelet decomposition at 1st-7th levels.After the continuum removal processing and analyzing, the sensitive wavebands (1375nm, 1520nm, 1861nm, 2100nm, 2286nm and 2387nm) were determined to predict soil TN content.Six sensitive wavebands were used to establish the regression models of soil TN content. The results showed that these sensitive wavebands could be used to predict soil TN content using real-time NIR spectra of soil. Fast and precisely estimating total nitrogen (TN) content in soil helps to promote carrying out prescription fertilization. And soil moisture is a severe interference factor in forecasting soil nitrogen content based on real-time NIR spectroscopy. This paper aims at predicting soil nitrogen content based on real-time soil spectrum through exploring pretreatment method without artificial drying and sieving soil samples. Firstly, the real-time near infrared absorbance spectra of soil samples were measured and their characteristics were analyzed. Then 1st-7th level wavelet decompositions were carried out for each soil samples real-time spectrum. RSNR (Relative Signal-to-Noise Ratio) was constructed to evaluate wavelet filtering quality at different levels, and the results indicated that low-frequency signals obtained after the 3rd level wavelet decomposing had the best performance. And then 5 soil sample groups (each group had the same moisture content but different nitrogen contents) were selected and continuum-removal method was used for processing their filtering signals. And by using the methods combined wavelet analysis and continuum removal technology, six sensitive wavebands were determined for predicting the TN content in soil, which were 1375nm, 1520nm, 1861nm, 2100nm, 2286nm and 2387nm. Finally the real-time TN content detecting models were calibrated and validated based on PLSR (Partial Least Squares Regression) and SVM (Support Vectors Machine) respectively. For the PLSR model, its calibration R2 was 0.602 and its RMSEC was 0.051mg/Kg; the validation R2 was 0.634, the RMSEP was 0.056mg/Kg and its RPD=1.838. For the SVM model, its calibration R2 reached to 0.823, the RMSEC was 0.034mg/Kg, the validation R2 reached to 0.810, the RMSEP was 0.053mg/Kg and its RPD was 2.129. It showed that, by using the proposed approach in this paper, the interference of soil moisture was mostly removed from soil real-time spectrum in the process of soil total nitrogen prediction, and the TN content regression models established by using the six sensitive wavebands had great performances in predicting soil TN content in real time.

Eliminating the interference of soil moisture and particle size on predicting soil total nitrogen content using a NIRS-based portable detector

A NIRS-based portable detector of soil TN content was developed.An algorithm was proposed to eliminate the interference of soil moisture on soil TN.A calibration was proposed to eliminate the interference of soil particle size.Combination of the two methods could well remove both the interference. Applying near infrared reflectance spectroscopy (NIRS) on farmlands can effectively estimate the total nitrogen (TN) content of soil online. We developed a NIRS-based portable detector of soil TN content that measures spectral data at 940, 1050, 1100, 1200, 1300, 1450, and 1550nm. The soil spectral data are sensitive to external environmental conditions, particularly soil moisture content and particle size. The interference of these factors on predicting soil TN content must be eliminated when using the portable detector. First, soil samples were collected from a farm in Beijing, China, and scanned using the detector to obtain their absorbance data under varying soil moisture and particle size. Second, absorbance correction method and mixed calibration set method were proposed to correct the original spectral data and to eliminate the interference of soil moisture and particle size, respectively. The absorbance of the soil sample at 1450nm exhibited a high correlation with soil moisture content. Thus, a moisture absorbance correction method (PMAI) was proposed to normalize the original spectral data into the standard spectral data and consequently eliminate the interference of soil moisture. A NIRS-based mixed calibration set based on the additivity of NIR spectra was produced with varying particle sizes, separated from the original soil samples, to eliminate the interference of soil particle size on the measurements of the portable soil TN detector. An estimation model of soil TN content was established based on the corrected absorbance data at six wavelengths (940, 1050, 1100, 1200, 1300, and 1550nm) using an algorithm of the back propagation neural network. The correlation coefficient of calibration, correlation coefficient of validation, root mean square error of calibration, root mean square error of prediction, and residual prediction deviation were used to evaluate the model. Compared with the model used the original spectral data, the accuracy and stability of the new model were significantly improved. These methods could efficiently eliminate the interference of soil moisture and particle size on predicting soil TN content.

Corn tassel detection based on image processing

Machine vision has been widely applied in facility agriculture, and played an important role in obtaining environment information. In this paper, it is studied that application of image processing to recognize and locate corn tassel for corn detasseling machine. The corn tassel identification and location method was studied based on image processing and automated technology guidance information was provided for the actual production of corn emasculation operation. The system is the application of image processing to recognize and locate corn tassel for corn detasseling machine. According to the color characteristic of corn tassel, image processing techniques was applied to identify corn tassel of the images under HSI color space and Image segmentation was applied to extract the part of corn tassel, the feature of corn tassel was analyzed and extracted. Firstly, a series of preprocessing procedures were done. Then, an image segmentation algorithm based on HSI color space was develop to extract corn tassel from background and region growing method was proposed to recognize the corn tassel. The results show that this method could be effective for extracting corn tassel parts from the collected picture and can be used for corn tassel location information; this result could provide theoretical basis guidance for corn intelligent detasseling machine.

Predicting apple sugar content based on spectral characteristics of apple tree leaf in different phenological phases

The sensitive bands of apple sugar content were found at 530-570nm and 700-720nm.Two phenological phases contribute to apple sugar accumulation higher.Apple sugar content is predictable using leaves spectra in different phenophase. Sugar degree is an important factor in determining the quality of apple. The sugar accumulation in apple fruit is closely related to fruit tree growth and development in different phases. In order to reveal the relationship between tree growth state and apple sugar content, the spectral information of apple tree leaves in different phenological phases was used to predict the fruit sugar degree. The visible and near infrared spectral reflectance of the leaves samples were measured by using a Shimadzu UV-2450 spectrograph, and the sugar content of each fruit sample growing near each leaves sample was collected and measured using laboratory methods. Then two dimensional correlation spectrum analysis was brought in, and the dynamic spectra in different phenological phases were obtained by using sugar contents as the perturbation quantity. Comprehensive observation on the spectral characteristics of leaf samples was conducted much accurately by analyzing two-dimensional correlation spectra of both synchronous and asynchronous. And then the effective spectral response bands of sugar contents and the contribution proportion to fruit sugar accumulation in different periods were investigated. And then, using the contribution proportion of each band as the single-period weighting factor, the fruit sugar sensitive wavebands were acquired. The fruit sugar content was forecasted using the sensitive bands in different phenological phases. After comparing and analyzing, it was found that the model based on parametric optimal solution of SVM showed good accuracy. The calibration R2 of the model reached to 0.8934, the RMSEC was 0.4925 Brix, the validation R2 reached to 0.8805, and its RMSEP was 0.4906 Brix. It reaches to a practical level and can be used to predict the sugar content in apple fruit.

Technology Application of Smart Spray in Agriculture: A Review

A smart spraying system in agriculture is a targeted spraying system with efficient application of chemical and low cost for the environment. A smart sprayer generally includes a targeted detection...

Analysis of soil phosphorus concentration based on Raman spectroscopy

Raman spectra signature can provide structural information based on vibrational transitions of irradiated molecules. In this work, the quantity reflecting mechanism of soil phosphorus concentration was studied based on Raman spectroscopy. 15 sand soil samples with different phosphate content were made in laboratory and the Raman signatures were measured. The relationship between sand soil Phosphorus concentration and soil Raman spectra was explored. Then the effective Raman signal was extracted from the original Raman spectra by using bior4.4 wavelet packet. The relationship between sand soil phosphorus and their extracted signals was analyzed and the PLS (Partial Least Squares) model for predicting phosphorus concentration in the soil was established and compared. The maximum accuracy model comes from the extracted effective Raman spectra after the first level decomposing. The calibration R2 was close to 1 and the validation R2 reached to 0.937. It showed high potential in soil phosphorus detecting by using Raman spectroscopy.

Estimation of Soil Total Nitrogen and Soil Moisture Based on NIRS Technology

Estimation model between soil moisture content and the near infrared reflectance was established by the linear regression method and the models between soil total nitrogen content and the near infrared reflectance were also established by the BP neural network method and Support Vector Machine (SVM) method. Forty-eight soil samples were collected from China Agricultural University Experimental Farm. After the soil samples were taken into the laboratory, NIR absorbance spectra were rapidly measured under the original conditions by the FT-NIR (Fourier Transform Near Infrared Spectrum) analyzer. At the same time the soil moisture (SM) and soil total nitrogen (TN) were measured by the laboratory analysis methods. The results of the study showed that a linear regression method achieved an excellent regress effect for soil moisture. The correlation coefficient of the calibration (RC) was 0.88, and the correlation coefficient of the validation (RV) was 0.85. The model was passed F test and t test. For soil total nitrogen, the model effect of BP neural network was better than that of SVM method, and the correlation coefficient of the calibration (RC) coefficient and the validation (RV) was 0.92 and 0.88, respectively. Both RMSE and PMSE were low. The results provided an important reference for the development of a portable detector.

Comparison of Different Detection Methods for Citrus Greening Disease Based on Airborne Multispectral and Hyperspectral Imagery

Citrus greening is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthing including ground reflectance measurement and diseased tree confirmation was conducted to build a proper library for HLB infected and healthy canopies. Several classification and spectral mapping methods were investigated to evaluate their applicability to HLB detection. Spectral features derived from both ground reflectance measurement and airborne images were analyzed. Both field, indoor and image spectral analysis showed that HLB infected canopy had higher reflectance in visible range. High positioning error of the ground truth in the 2007 HS image led to detection accuracy of less than 50% in the validation set for every classification methods. In the 2010 images, with better ground truth records, more precise library for HLB infected and healthy canopies were collected and higher classification accuracy was then achieved. Spectral angle mapping (SAM) showed the highest detection accuracy of more than 95% in the training sets of both HS and MS images, but its accuracy in the validation set deceased a lot, to only 55% in HS image and 62% in MS image. The simpler classification method MinDist and MahaDist have somewhat more balanced accuracy rates between the training and validation sets. Support vector machine (SVM) couldn’t work properly in HLB detection, but provided a fast, easy and adoptable way to build a mask for tree canopy, so that other background could be easily blocked out for classification.

Evaluation of maize growth by ground based multi-spectral image

In order to evaluate growth status of maize automatically and accurately, a multi-spectral image camera was used to collect ground-based images of maize canopy in the field. The average gray value (GIA, RIA and NIRIA) and the vegetation indices (DVI, RVI, NDVI, et al.) widely used in remote sensing were selected as the parameters for maize growth monitoring. The parameters were obtained based on image processing including image preprocessing, canopy segmentation and parameter calculation. After analyzing the correlation between each image parameter and chlorophyll content of maize leaf at the shooting stage, a new vegetation index, combination of normalized difference vegetation index (CNDVI), was developed and a positive correlation was observed between CNDVI and the chlorophyll content. The values of correlation coefficient were 0.63 and 0.60 under high and normal nitrogen treatment respectively. CNDVI performed great potential to estimate the chlorophyll content of maize.

Real Time Detection of Soil Moisture in Winter Jujube Orchard Based on NIR Spectroscopy

The measurement and control of soil moisture are the key technologies of precision agriculture. In order to real-time detect soil moisture content faster and more accurately, a portable soil moisture sensor based on NIR spectroscopy was developed. With the sixty soil samples collected from a winter jujube orchard, a linear regression model was established. The determination coefficients of the calibration (\(R^2_c\)) and validation (\(R^2_v\)) reached 0.88 and 0.92, respectively. The model passed F-test and t-test and showed robust. Subsequently, two spatial distribution maps of soil moisture were generated based on the data obtained by the portable soil moisture detector and the data obtained by oven drying method, respectively. Finally, the correlation between these two maps was investigated by using the software of Surfer 8.0. The zones of dry and wet soil could be distinguished easily in both maps. The results of the study showed that the developed detector was practical.