Detection Of Chlorophyll Content Research Articles

Improving detection of wheat canopy chlorophyll content based on inhomogeneous light correction

Effectively improving the detection accuracy of wheat chlorophyll content is of great significance for the detection of photosynthetic capacity and growth status of wheat canopy. However, due to inhomogeneous light distribution issues in canopy, the existence of shaded and sun wheat leaves in wheat canopy has influence for spectral-based detection of chlorophyll content. Therefore, in order to improve detection of wheat canopy chlorophyll content, a light distribution correction method was proposed to correct intact leaves’ light distribution based on shaded and sun leaves in multispectral images. Firstly, R-G-NIR images were reconstructed to segment and analyze shaded and sun leaves of wheat. Secondly, Homomorphic Filter (HF) and Gamma light correction method was used to optimize shaded leaves’ light distribution. Then, the differential responses of 10 different types of vegetation indices in shaded, sun, original intact and corrected intact leaves were analyzed to screen chlorophyll-sensitive parameters based on Random Frog Method (RFM). Finally, Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR) were used to establish models for the detection of chlorophyll content in shaded, sun, original intact and corrected intact leaves of wheat, respectively. The results showed that the proposed light correction method reduced the inhomogeneous light and kept more uniform. Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), difference vegetation index (DVI), normalized redness intensity (NRI), and optimized soil-adjusted vegetation index (OSAVI) were selected as the optimal spectral variables. And the models after correction had higher accuracy than the models before correction. The wheat chlorophyll content model of corrected intact leaves based on RF had the highest accuracy, with a calibration set Rc2 of 0.816, RMSEc of 3.702, a validation set Rv2 of 0.804, RMSEv of 3.958, respectively. The research integrates the above results to improve detection of wheat canopy chlorophyll content, which provides technical support for the light distribution correction in multispectral images.

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Detection of chlorophyll content based on optical properties of maize leaves

The chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status. Maize is one of the three widely planted gain crops in the world. In order to offer useful information for the development of chlorophyll content detectors of maize leaves, a single integrating sphere system was used to measure the transmittance and reflectance spectra of maize leaves over the wavelength range of 500–950 nm. The linear relationships of transmittance and reflectance with chlorophyll content were investigated. The feature wavelengths (FWs) sensitive to chlorophyll content were extracted from the full transmittance and reflectance spectra using the successive projections algorithm (SPA). The partial least squares regression (PLSR) models for predicting the chlorophyll content were established using the full spectra and extracted FWs. The results showed that there were obvious linear relationships between transmittance and reflectance with chlorophyll content of maize leaves and the best linear relationships were found at 709 nm and 714 nm, respectively, with the linear correlation coefficients of 0.801 and 0.696, and the root-mean-squares error (RMSEP) of 0.321 mg·g−1 and 0.405 mg·g−1, respectively. Eight and 6 FWs were extracted from the transmittance and reflectance spectra, respectively. The PLSR model established using the selected FWs from transmittance spectra had better prediction performance with RMSEP of 0.208 mg·g−1 than using full transmittance spectra. The built PLSR models using the full reflectance spectra and extracted FWs had poor robustness. This research offers some theoretical basis for developing a maize leaf chlorophyll content detector based on transmittance or reflectance.

Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection

In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems.

Improving chlorophyll content detection to suit maize dynamic growth effects by deep features of hyperspectral data

Improving chlorophyll content detection to suit maize dynamic growth effects by deep features of hyperspectral data

Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments

In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m2·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (RP = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories.

UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages

• LCC was estimated based on the UAV-based image analysis. • The response stability of vegetation index and LCC was analyzed under different coverage. • Different variable screening methods were used to optimize the spectral parameters. • The spatial distribution map of maize LCC was constructed to provide potentials for fertilization application. Efficiently estimating chlorophyll content is important in monitoring the photosynthesis capacity and growth status of maize canopy in precision agriculture management. Vegetation index (VI) easily obtained by proximal remote sensing has been used as a non-destructive and high-throughput way in crop monitoring, especially in chlorophyll estimation. However, the estimated results of the field chlorophyll content by VIs always face challenges from soil background inhibition and estimation stability under the dynamic changes of vegetation biomass. Thus, an unmanned aerial vehicle (UAV)-based chlorophyll content estimation was conducted by evaluating VI responses under different crop coverages. An analysis was conducted on 36 VIs under different crop coverage conditions to explore their response differences and robustness for chlorophyll estimation. This work focused on the three kinds of VIs named simple vegetation index, modified vegetation index, and functional vegetation index. In 2020, at the experimental station of Dryland Farming Institute of Hebei Academy of Agriculture and Forestry Sciences, UAV carrying multispectral sensor was used to collect visible and near-infrared images of the canopy at the jointing stage of maize under six fertilization levels to obtain VIs. After the UAV fled, ground calibration and sample collection were performed simultaneously, and chlorophyll content was measured. For data processing, correlation coefficient method (CCM) and maximal information coefficient (MIC) were first used to analyze the correlation response characteristics of VIs and chlorophyll content under three different coverage levels. The results showed that when the level of canopy coverage was increased, the linear correlation between VIs and chlorophyll content was substantially reduced. The MIC response indicating linear and non-linear combination relationship was more robust. In addition, the VIs obtained by UAV had a significant linear correlation with maize canopy chlorophyll under low (0.05–0.35) and medium (0.35–0.48) coverage, but an obvious non-linear correlation under high (0.48–0.75) coverage. Chlorophyll-sensitive parameters were then screened based on methods of CCM, MIC, and random frog method (RFM), respectively. Partial least squares regression (PLS) and random forest (RF) algorithms were used to establish the maize canopy chlorophyll content detection models. The findings showed that when Green minus red vegetation index (GMR), Red light normalized value (NRI), Normalized difference red edge (NDRE), Modified simple ratio with red edge (MSRREG), Enhanced Vegetation Index (EVI), Normalized red green difference vegetation index (NDIg), Normalized red blue difference vegetation index (NDIb), Soil-adjusted vegetation index (SAVI), Optimized soil-adjusted vegetation index with red edge (OSAVIREG), Soil-atmospherically resistant vegetation index (SARVI) were selected based on RFM as the optimal spectral variables, the chlorophyll content detection model constructed based on PLS had the least numbers of characteristic variables and the best model accuracy. The training set R 2 and RMSE were 0.753 and 2.089 mg/L, respectively, and the verification set R 2 and RMSE were 0.682 and 2.361 mg/L, respectively. Field chlorophyll content and detection error distribution maps were also drawn and combined with the distribution of fertilization management to provide support for the UAV monitoring of crop growth in the field and variable fertilization management decisions.

Chlorophyll detector development based on snapshot-mosaic multispectral image sensing and field wheat canopy processing

To achieve rapid nondestructive detection of chlorophyll content in crops in a field environment, we designed a new lightweight device based on a snapshot-mosaic imaging sensor given the sensitive properties of chlorophyll in the red-edge to near-infrared band (700–900 nm) and the advantages of imaging spectroscopy in environmental noise separation. The hardware part of the device includes a spectral camera based on a snapshot-mosaic sensor, a main control unit, a network module, a storage module, a power supply module, and a display and remote-control device. The software part was written using the Qt library and the C++ programming language. It includes the connection and initialization of the sensor, the control of the exposure time, the display of the image in real time, the acquisition and storage, and the calculation of the reflectance. The sensor was tested and calibrated to evaluate the sensor performance. Experimental results show that the different channels of the sensor have an excellent linear relationship for light intensity changes and can be used to measure reflected radiation from crop leaves in a field environment after calibration. At the same time, field experiments on wheat were conducted. A dark channel filtering method for 25 channels of the developed device was proposed to effectively eliminate the scattered light interference from the crop canopy in the spectral images. The background interference was effectively eliminated by background segmentation. Partial least squares regression (PLSR) method was used for modeling after preprocessing and band screening of the spectral data. The model achieved high accuracy with RC2 of 0.87, RV2 of 0.79, and RMSE of 3.94 mg/L. As a result, the system combined the proposed model with the developed device can effectively eliminate the interference of soil background and scattered light caused by the complex structure of crop canopy, and improve the accuracy of diagnosis. So that the system has potential in the crop growth variation analysis.

Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves

• A CNN assisted CWT-based spectrogram strategy is proposed for chlorophyll content detection. • The first-order derivative was performed to capture detailed spectral information. • CWT was applied to obtain effective features from spectral data. • CNN model was applied to explore deep features hidden in spectral data. Visible and near-infrared spectroscopy is a nondestructive method for the chlorophyll content detection of potato crops, in which effective feature extraction is a crucial issue for detecting accuracy in the field. This study aimed to explore comprehensive features to improve the accuracy of chlorophyll content detection in potato leaves. A feature extraction method was proposed by assisting continuous wavelet transform (CWT)-based spectrogram and convolutional neural network (CNN) of the deep learning algorithm. In the experiments, the spectral data in the range of 325–1075 nm were measured in the field. A total of 314 potato leaf samples were collected, and the chlorophyll content was determined in four growth periods. The spectral features in the spatial domain and time–frequency domain were considered in data processing. First, we compared features of first-order derivative (FOD) and Savitzky–Golay smoothing (SG) in the spatial domain to select the preprocessing method. Second, in the time–frequency domain, CWT decomposes spectral data into a series of wavelet coefficient curves at various scales and wavelengths. We supposed that combining both scales and wavelengths of the wavelet coefficients could benefit the detection of the chlorophyll content in potato crops. Thus, the spectrograms were established by transforming 1D wavelet coefficient curves into 2D wavelet coefficient spectrograms. The CNN model was applied to explore potential and comprehensive features in the spectrograms. Finally, partial least squares models were established to compare the detection capability of three features including the spatial domain, wavelet coefficients by CWT, and spectrogram features by CNN. The modeling performances showed that the FOD features obtained the coefficient of determination of 0.7856 in the prediction set ( R P 2 ) and the root mean square error in the prediction set ( RMSEP ) with 3.9357; the FOD features performed better than the SG features ( R P 2 = 0.7734, RMSEP = 4.0460); The wavelet coefficients by CWT obtained R P 2 = 0.8293 and RMSEP = 3.5117. The PLS model with spectrogram features by the CNN model performed best and achieved R P 2 = 0.8749 and RMSEP = 3.0067. It preferably captured the deeper features of spectral data compared with features in the spatial domain. This research provides an effective method for leaf chlorophyll content detection in the precision management of potato crops.

Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.

Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.

A Low Cost and Nondestructive Micro-Pocket Meter on Chlorophyll Contents of Vegetable Leaves

Highlights A meter on the contents of chlorophyll a and b and total chlorophyll of vegetable leaves was developed. Three LEDs as light sources and a light sensor to sense transmitted light through leaves. The models to predict the chlorophyll contents were established using multiple linear regression. The pocket detector makes chlorophyll content detection easy, fast, and in situ. Abstract. Detecting the contents of chlorophyll (chlorophyll a, chlorophyll b) is important to instruct vegetable growth. However, the traditional spectrophotometry used in measuring these contents requires expensive equipment, is destructive, time-consuming, toxic (acetone), and unable to measure in situ. To make the detection nondestructive, quick, cheap, safe, and on-site, a low-cost micro-pocket photosynthetic chlorophyll content meter was developed in this study. The meter consisted of a micro-controller of STC12C5A60S2, three light-emitting diodes at 460, 660, and 980 nm, a digital light sensor, three keys, one organic light-emitting diode display, and a power supply unit. The software was developed by using the C language. The leaves of leaf lettuce, garden sass, and spinach were used as samples. The models used to predict the contents of chlorophyll a, chlorophyll b, and total chlorophyll, as well as SPAD values, were established based on the absorbance of vegetable leaves at three wavelengths by using the multiple linear regression method. The root-mean-squares errors of the developed meter for chlorophyll a, chlorophyll b, total chlorophyll, and SPAD value were 0.068 mg/g, 0.024 mg/g, 0.085 mg/g, and 1.7, respectively. The meter was small in size and low in cost. The contents of chlorophyll a, chlorophyll b, total chlorophyll, and SPAD value, could be given in 2 s. Keywords: Absorbance, Chlorophyll a, Chlorophyll b, Chlorophyll content, Pocket meter, SPAD.

Development of crop chlorophyll detector based on a type of interference filter optical sensor

To achieve a non-destructive detection of chlorophyll content in field crops based on the reflectance characteristics of chlorophyll in the visible and near-infrared spectrum (400 nm–1000 nm), a crop chlorophyll detector based on an interference filter optical sensor was designed. The hardware part of this detector mainly comprises a microcontroller unit, a sensor module, an input/output module, and a power module. The software is written in Python language and includes main functions, acquisition sub-functions, data processing sub-functions, and data storage sub-functions. Calibration and test experiments were carried out to evaluate the performance of the sensor. Results show that the sensor has a good responsivity of light intensity changes, so as to measure the reflected radiation from crops with the absorption of chlorophyll content. Field verification experiments of corn crops were also carried out, and chlorophyll content detecting models were built by using four combinations of characteristic wavelengths, including 3 peak bands, 9 bands selected via the stepwise regression analysis method, 8 bands selected via the Monte Carlo uninformed variable elimination method, and all 18 bands. Among them, the stepwise regression method obtained the best modeling results. The model showed better performance after calibration than before the calibration with RC2 of 0.72, RV2 of 0.61, RMSEc of 2.35 mg/L, and RMSEv of 2.43 mg/L. The crop chlorophyll detector based on the interference filter optical sensor was used for filed estimation of chlorophyll content which showed a potential for the analysis of crop growth differences.

Chlorophyll Content Detection Based on Image Segmentation by Plant Spectroscopy

Chlorophyll Content Detection Based on Image Segmentation by Plant Spectroscopy

Monitoring of Canopy Reflectance Change Based on Flowering Rate

Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.

Detection of chlorophyll content in growth potato based on spectral variable analysis

Monitoring the chlorophyll content in tuber formation and tuber bulking stage has great significance for the nutritional status diagnosis in the potato field. In this paper, the 314 canopy spectra of potato crops were collected at four stages, respectively. The leaves were collected simultaneously to measure the chlorophyll content. After spectra pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. Two variable selection algorithms (successive projection algorithm, and random frog algorithm) were employed to select chlorophyll characteristic wavelengths. The partial least square algorithm was used for modeling analysis. The performance of sensitive wavelengths selected by two algorithms was compared, respectively, from the perspective of correlation with chlorophyll, reflecting leaf information, and model result. The sensitive wavelengths selected by random frog was optimal. The determination coefficient of calibration and validation set of the detection model established based on above sensitive wavelengths was respectively 0.827 and 0.798. The results showed the chlorophyll content of potato could be detected accurately based on the spectroscopy method.

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares (MWPLS) and genetic algorithm (GA) for the chlorophyll content detection of winter wheat canopy using spectroscopy technology. Firstly, the original spectral dataset was pre-processed by wavelet denosing, multiple scatter correction. Then, abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY. Finally, the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares (PLS) models were established for chlorophyll content prediction. For the model established by using all the wavebands in the region of 400-900 nm, its Rc2 and Rv2 were 0.4468 and 0.3821 respectively; its modeling root mean square error (RMSEM) and verification root mean square error (RMSEV) were 2.9057 and 1.7589 respectively. For the model established by using 151 wavebands selected by MWPLS, its Rc2 and Rv2 were 0.6210 and 0.5901 respectively; its RMSEM and RMSEV were 2.4007 and 1.6408 respectively. For the model established by using 36 wavebands selected by MWPLS+GA, its Rc2 and Rv2 were 0.7805 and 0.7497 respectively; its RMSEM and RMSEV were 1.8504 and 1.1315 respectively. The results show that wavelength selection can remove redundant information and improve model performance. The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction. Keywords: MWPLS, GA, canopy spectral reflectance, Chlorophyll content prediction DOI: 10.25165/j.ijabe.20191205.4033 Citation: Liu H J, Li M Z, Zhang J Y , Gao D H , Sun H , Zhang M, et al. A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA. Int J Agric & Biol Eng, 2019; 12(5): 149–155.

Rapid detection of chlorophyll content in rapeseed based on near infrared spectroscopy

Rapid detection of chlorophyll content in rapeseed based on near infrared spectroscopy

Detection of Chlorophyll Content in Maize Canopy from UAV Imagery

Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and non-destructively, the chlorophyll content detection of maize canopy was carried out based on UAV image processing. In this paper, the RGB (red, green, blue) images of the maize canopy were measured in the Hengshui, Hebei province. The processing method was proposed to estimate the chlorophyll content in the field. Firstly, the image was segmented based on the HSV (hue, saturation, value) color model to remove soil background. The parameters were extracted related to the color feature and the texture feature in the image. On the one hand, there were 10 color parameters were involved including the red, green, blue, green and red differences, normalized red and green differences, and so on. On the other hand, the texture parameters were calculated with mean, standard deviation, smoothness, third moment, etc. The detection model of maize chlorophyll content was established and discussed based on BP neural network. The experiment results showed that: (1) The detecting accuracy of chlorophyll content was increased by the image parameter combination of color and texture features. Compared with the color feature, the determination coefficient of the model was increased from 0.6987 to 0.7246 by involving the texture feature. (2) The segmentation of canopy could help to improve the estimation accuracy due to the influence elimination of soil background, and the determination coefficient of model increased from 0.7246 to 0.7564, meanwhile, the root mean square error (RMSE) decreased from 4.4659 mg·L-1 to 4.4425 mg·L-1. The chlorophyll content of maize canopy was calculated at pixel level to indicate the field statues. The distribution map of chlorophyll content in field maize canopy was drawn based on pseudo-color technique. It provided a tool to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. The UAV imagery could help to measure the content and distribution of maize chlorophyll non-destructively, and provide a support for crop evaluation and precision management in the field.

Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder

ABSTRACT Most leaf chlorophyll predictions based on digital image analyzes are modeled by manual extraction features and traditional machine learning methods. In this study, a series of image preprocessing operations, such as image threshold segmentation, noise processing, and background separation, were performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods.

Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors

The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards. The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed. Additionally, the associations of the leaf SPAD (soil and plant analyzer development) value with the ratio vegetation index (RVI) and normalized differential vegetation index (NDVI) were analyzed. The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method. Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created. The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor, FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant. The measures of goodness of fit of the predictive models were R2=0.7063, RMSECV=3.7892, RE=5.96%, and RMSEP=3.7760 based on RVI(570/800) and R2=0.7343, RMSECV=3.6535, RE=5.49%, and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)]. The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard, which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management. Keywords: citrus, remote sensing, bio-sensor, chlorophyll detection, spectrum, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), spatial distribution map DOI: 10.25165/j.ijabe.20181102.3189 Citation: Wang K J, Li W T, Deng L, Lyu Q, Zheng Y Q, Yi S L, et al. Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors. Int J Agric & Biol Eng, 2018; 11(2): 164–169.