Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis

A method has been developed for rapid and non invasive determination of chlorophyll content of leaves of micropropagated potato plants using RGB based image analysis. Among the trichomatic colors, R and G negatively correlated with the chlorophyll content, while a positive correlation was observed with B chromate. Compared to mean brightness value, the use of mean brightness ratio considerably improved the relationship of the tricolors with chlorophyll content. The brightness values and ratios of the primary colors are modeled as linear correlation functions for chlorophyll content. A significant correlation was observed between the model predicted chlorophyll content with the chlorophyll content measured by chlorophyll content meter. Spectral properties such as luminosity and saturation were also found to be negatively correlated with the chlorophyll content. The relationship was improved by combining the mean brightness ratio at B band region with luminosity. The potential of the imaging system in micropropagation has been discussed.     

Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants

The present work describes a digital image analysis method based on leaf color analysis to estimate chlorophyll content of leaves of micropropagated potato plantlets. For estimation of chlorophyll content, a simple leaf digital analysis procedure using a simple digital still camera was applied in parallel to a SPAD chlorophyll content meter. RGB features were extracted from the image and correlated with the SPAD values. None of the mean brightness parameters (RGB) were correlated with the actual chlorophyll content following simple correlation studies. However, a correlation between the chromaticity co-ordinates ‘r’, ‘b’ and chlorophyll content was observed, while co-ordinate ‘g’ was not significantly correlated with chlorophyll content. Linear regression and artificial neural networks (ANN) were applied for correlating the mean brightness (RGB) and mean brightness ratio (rgb) features to chlorophyll content of plantlet leaves determined through a SPAD meter. The chlorophyll content as determined by the SPAD meter was significantly correlated (RMSE = 3.97 and 3.59, respectively, for linear and ANN models) to the rgb values of leaf image analysis. Both the models indicate successful prediction of chlorophyll content of leaves of micropropagated plants with high correlation. The developed RGB-based digital image analysis has the advantage over conventional subjective methods for being objective, fast, non-invasive, and inexpensive. The system could be utilized for real-time estimation of chlorophyll content and subsequent analysis of photosynthetic and hyperhydric status of the micropropagated plants for better ex vitro survival.     

A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis

The development of smartphones, specifically their cameras, and imaging technologies has enabled their use as sensors/measurement tools. Here we aimed to evaluate the applicability of a fast and noninvasive method for the estimation of total chlorophyll (Chl), Chl a, Chl b, and carotenoids (Car) content of soybean plants using a smartphone camera. Single leaf disc images were obtained using a smartphone camera. Subsequently, for the same leaf discs, a Chl meter was used to obtain the relative index of Chl and the photosynthetic pigments were then determined using a classic method. The RGB, HSB and CIELab color models were extracted from the smartphone images and correlated to Chl values obtained using a Chl meter and by a standard laboratory protocol. The smartphone camera was sensitive enough to capture successfully a broad range of Chl and Car contents seen in soybean leaves. Although there was a variation between color models, some of the proposed regressions (e.g., the S and b index from HSB and Lab color models and NRI [RGB model]) were very close to the Chl meter values. Based on our findings, smartphones can be used for rapid and accurate estimation of soybean and Car contents in soybean leaves.     

Application of an artificial neural network (ANN) model for predicting mosquito abundances in urban areas

The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of R and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.     

Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester

Background  

Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes.     

An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing

Nowadays, artificial intelligence solutions such as digital image processing and artificial neural networks (ANN) have become important applicable techniques in phytomonitoring and plant health detection systems. In this research, an autonomous device was designed and developed for detecting two types of fungi (Pseudoperonospora cubensis, Sphaerotheca fuliginea) that infect the cucumber (Cucumis sativus L.) plant leaves. This device was able to recognise the fungal diseases of plants by detecting their symptoms on plant leaves (downy mildew and powdery mildew). For leaves of cucumber inoculated with different spores of the fungi, it was possible to estimate the amount of hour post inoculation (HPI) by extracting leaves’ image parameters. Device included a dark chamber, a CCD digital camera, a thermal camera, a light dependent resistor lightening module and a personal computer. The proposed programme for precise disease detection was based on an image processing algorithm and ANN. Three textural features and two thermal parameters from the obtained images were measured and normalised. Performance of ANN model was tested successfully for disease recognition and detecting HPI in images using back-propagation supervised learning method and inspection data. Such this machine vision system can be used in robotic intelligent systems to achieve a modern farmer’s assistant in agricultural crop fields.     

Molecular analysis of micropropagated neem plants using aflp markers for ascertaining clonal fidelity

Summary The importance of neem (Azadirachta indica A. Juss.) as a medicinal tree species has been acknowledged worldwide. Superior trees with desired traits such as high azadirachtin content have been identified and micropropagated. Somaclonal variants that may arise in vitro, however, pose limitations to large-scale micropropagation. It is, therefore, imperative to establish genetic uniformity of such plantlets by ensuring strict quality checks at various stages of in vitro culture. This is the first study that evaluates the applicability of amplified fragment length polymorphism (AFLP) markers in establishing clonal fidelity of tissue culture(TC)-raised neem plants. Seven AFLP primer combinations generated a total of 334 amplified fragments across the mother plant, TC progenies, and other neem accessions that were included as controls. Two hundred and thirty-nine amplified fragments were monomorphic across the mother tree and its TC progenies. No extra band was detected in the TC plantlets that was absent in the mother tree, indicating that the TC plantlets regenerated through nodal explants are indeed true-to-type. Ninety-five AFLP fragments were detected in the controls, which allowed their discrimination from the elite mother tree and its TC progenies. Similarity matrix based on Jaccard's coefficient revealed that the pair-wise value between the mother tree and its TC plantlets was ‘1’, indicating perfect similarity. Phenetic dendrogram based on UPGMA (unweighted pair group method of arithmetic averages) analysis further confirmed the true-to-type nature of TC progenies, since a tie was observed between the mother tree and its TC plantlets. On the contrary, the control neem accessions were distinct from the mother and its TC progenies. AFLP markers proved to be an ideal tool for routine analysis and certification of genetic fidelity of micropropagated plants prior to commercialization, especially in tree species because of their long generation time.     

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg^?1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R² (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.     

城市边缘区景观生态规划的人工神经网络模型

景观生态规划是景观生态学的一个重要应用领域,本文在地理信息系统的辅助下引入了人工神经网络这一新兴应用技术,建立了城市边缘区景观生态规划的BP神经网络模型,模型以区域的高程、高程离差、坡度、坡度离差、地貌分区、离黄河距离、居民点数七个要素作为输入变量,选取斑块密度、分维数、Shannon多样性指数和聚集度指数作为输出变量,精心采集了20个样本对网络进行训练,结果表明网络收敛效果理想,泛化能力强,为景观生态规划提供了一个新的模拟分析手段。     

Feasibility of using smart phones to estimate chlorophyll content in corn plants

New spectral absorption photometry methods are introduced to estimate chlorophyll (Chl) content of corn leaves by smart phones. The first method acquires light passing through a leaf by smartphone camera, compensating for differences in illumination conditions. In order to improve performance of the method, spectral absorption photometry (SAP) with background illumination has been considered as well. Data were acquired by smartphone camera in Iowa State University maize fields. Various indices were extracted and their correlation with Chl content were examined by Minolta SPAD-502. Hue index in SAP reached R ² value of 0.59. However, with light-aided SAP (LASAP), R ² of 0.97 was obtained. Among traits, the vegetation index gave the most accurate indication. We can conclude that the high performance of LASAP method for estimating Chl content, leads to new opportunities offered by smart phones at much lower cost. This is a highly accurate alternative to SPAD meters for estimating Chl content nondestructively.     

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

Background  

Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.     

Random amplified polymorphic DNA (RAPD) markers for genetic analysis in micropropagated plants of Populus deltoides Marsh

RAPD markers were used to assess genetic fidelity of 23 micropropagated plants of a single clone (L34) of Populus deltoides. Eleven arbitrary 10-base primers were successfully used to amplify DNA from in vivo and in vitro material. Of these, 5 distinguished a total of 13 polymorphisms common across 6 micropropagated plants. Apart from these 6 plants, the amplification products were monomorphic across all the micropropagated plants, the mother plant and 4 additional field-grown control plants. Our results show that RAPD markers can be used to gain rapid and precise information about genetic similarities or dissimilarities in micropropagation systems that might not be so easily evident from other commonly used techniques.     

Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach

A horizontal subsurface flow (HSSF) and a free water surface flow (FWSF) constructed wetlands (4 m² of each) were set up on the campus of Harran University, Sanliurfa, Turkey. The main objective of the research was to compare the performance of two systems to decide the better one for future planning of wastewater treatment system on the campus. Both of the wetland systems were planted with Phragmites australis and Canna indica. During the observation period (10 months), environmental conditions such as pH, temperature and total chemical oxygen demand (COD), soluble COD, total biochemical oxygen demand (BOD), soluble BOD, total suspended solids (TSS), total phosphate (TP), total nitrogen (TN) removal efficiencies of the systems were determined. According to the results, average yearly removal efficiencies for the HSSF and the FWSF, respectively, were as follows: total COD (75.7% and 69.9%), soluble COD (85.4% and 84.3%), total BOD (79.6% and 87.6%), soluble BOD (87.7% and 95.3%), TN (33.2% and 39.4%), and TP (31.5% and 6.5%). Soluble COD and BOD removal efficiencies of both systems increased gradually since the start-up. After nine months of operation, above 90% removal of organic matters were observed. The treatment performances of the HSSF were better than that of the FWSF with regard to the removal of suspended solids and total COD at especially high temperatures. In FWSF systems, COD concentrations extremely exceeded the discharge limit values due to high concentrations of algae in spring months.The performance of the two systems was modelled using an artificial neural network-back-propagation algorithm. The ANN model was competent at providing reasonable match between the measured and the predicted concentrations of total COD (R = 0.90 for HSSF and R = 0.96 for FWSF), soluble COD (R = 0.90 for HSSF and R = 0.74 for FWSF) and total BOD (R = 0.94 for HSSF and R = 0.84 for FWSF) in the effluents of constructed wetlands.     

基于神经网络的马尾松叶绿素含量高光谱估算模型

分析不同生长期的马尾松冠层反射光谱特征与相应叶绿素含量的相关关系.利用36个红边参数逐一筛选,最终确定7个与叶绿素含量相关性较高的红边参数作为光谱特征参数,分别应用逐步分析法与BP神经网络构建叶绿素含量的高光谱估算模型;同样,筛选出4个植被指数作为光谱特征参数,同时,将对原始光谱进行主成分分析降维后的前4个主成分作为BP神经网络的输入变量,分别应用逐步分析法与BP神经网络构建叶绿素含量的高光谱估算模型.结果表明: 将红边参数作为输入变量建立的逐步回归模型和BP神经网络模型的决定系数(R²)分别为0.5205、0.7253,均方根误差(RMSE)分别为0.1004、0.0848,相对误差分别为6.3%、5.7%.将植被指数作为输入变量建立的逐步回归模型和BP神经网络模型的R²分别为0.5392、0.7064,RMSE分别为0.0978、0.0871,相对误差分别为6.2%、6.0%.基于主成分分析的BP神经网络模型的预测效果最好,R²为0.7475,RMSE为0.0540,相对误差为4.8%.     

Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra

A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.     

Increasing productivity of Spirulina platensis in photobioreactors using artificial neural network modeling

Although production of microalgae in open ponds is conventionally practiced due to its economy, exposure of the algae to uncontrollable elements impedes achievement of quality and it is desirable to develop closed reactor cultivation methods for the production of high value products. Nevertheless, there are several constraints which affect growth of in closed reactors, some of which this study aims to address for the production of Spirulina. Periodic introduction of fresh medium resulted in increased trichome numbers and improved algal growth compared to growth in medium that was older than 4 weeks in 20 L polycarbonate bottles. Mixing of the cultures by bubbling air and use of draft tube reduced the damage to the growing cells and permitted increased growth. However, there was better growth in inclined cylindrical reactors mixed with bubbling air. The oxygen production rates were very similar irrespective differences in the maintained cultures densities. The uniformity in oxygen production rate suggested a tendency towards homeostasis in Spirulina cultures. The frequency of biomass harvest on the productivity of Spirulina showed that maintenance of moderate culture density between 0.16 and 0.32 g/L resulted in about 14% more productivity than maintaining the cell density between 0.16 and 0.53 g/L or 48% more than by daily harvest above 0.16 g/L. An artificial neural network based predictive model was developed, and the variables useful for predicting biomass output were identified. The model could predict the growth of Spirulina up to 3 days in advance with a coefficient of determination >0.94.     

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut‐off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R²) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte‐Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436–1443, 2016     

An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang,China

Artificial neural network (ANN) models have been widely used in environmental modeling with considerable success. To improve the reliability of ANN models, ensemble simulations were applied in this study to develop four ANN ensemble models for chlorophyll a simulation in the largest freshwater lake (Lake Poyang) in China. Reliability (evaluated by model fit and stability) of these ANN ensemble models was compared with that of single ANN models from ensemble members. The model fit of these single ANN models varied significantly over repeated runs, indicating the unstable performance of the single ANN models. Comparing with the single ANN models, the ANN ensemble models showed a better model fit and stability, implying the potential of ensemble simulation in achieving a more reliable model. An ensemble size of 30 was adequate for the ANN ensemble models to achieve a good model fit, while an ensemble size of 50 was adequate to achieve good stability. This case study highlighted both the necessity and potential of the ensemble simulation approach to achieve a reliable ANN model with good model fit and stability.