Chlorophyll Estimation Research Articles

Estimation of mass, chlorophylls, and anthocyanins of Spirodela polyrhiza with smartphone acquired images

Premium cameras may provide excellent quality images for analysis, but not everyone can afford to own one compared to a smartphone camera. Therefore, this has given rise to several attempts to incorporate smartphone and image analysis in analytical procedures. This study intended to seek the possibilities of using a smartphone to capture images and subsequent analysis to estimate mass, chlorophylls, and anthocyanin simultaneously in Spirodela polyrhiza by correlation models. This work serves as the substitution for conventional protocols in which determination of mass and phytochemicals in plants require the destruction of samples, usage of reagents, long processing time, and dependent on the availability of high-end equipment. In this study, the image taken with a smartphone camera was processed, and necessary information was extracted using ImageJ software. The area of plantlets was measured, and the relationship between area and mass was studied. Color parameters values extracted from the image were transformed into different combinations to explore the strength of the relationship between color parameters and combinations with chlorophyll and anthocyanin content of S. polyrhiza. Saturation channel from HSI color space was found to predict the mass of plantlets slightly more accurately than a* channel from L*a*b* color space. The mean green (G) value of the image was a robust parameter to predict chlorophyll contents in S. polyrhiza with a high r2 of 0.9693 and the lowest error compared to other color parameters and their combinations. Compared with other color parameters and combinations, the 2G-B value presented the most robust relationship with anthocyanin contents in S. polyrhiza¸having r2 = 0.8638 and the lowest percentage of errors. The mean G value predicts chlorophylls content in S. polyrhiza with 9.5 ± 7.3% of errors, while 2G-B estimates anthocyanins content with 10.42 ± 6.82% errors. This research demonstrated that the images captured with smartphones could be a ground-breaking strategy to predict mass, chlorophylls, and anthocyanins content in S. polyrhiza sufficiently accurate, rapid, and cost-effective compared with the traditional chemical method.

Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way.

Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.

Unmanned aerial vehicle (UAV) remote sensing estimation of wheat chlorophyll in subsidence area of coal mine with high phreatic level

Unmanned aerial vehicle (UAV) remote sensing estimation of wheat chlorophyll in subsidence area of coal mine with high phreatic level

Evaluation of rhizosphere Streptomyces for zinc solubilizing and plant growth promoting properties

Actinobacteria were isolated from rhizosphere soil and its plant growth-promoting ability was tested. Actinobacteria were screened for promoting plant growth by phosphate and zinc solubilization, gibberellin, siderophore, HCN and indole-3-acetic acid production. The abiotic stresses of all the isolates were tested for parameters such as pH, temperature and NaCl. Upon screening, a potent actinobacteria named Streptomyces sp. ZS-18 was isolated and identified based on the colony characteristics. The potent strain solubilized both the zinc oxide (26 ± 0.75mm) and zinc carbonate (22 ± 0.88mm). The utilization of gibberellic acid (80μg/ml) by the potential strain ZS-18 showed maximum production measured by optical density value of 0.179. The growth of the green gram plant was determined by shoot, root, total length, number of leaves and chlorophyll estimation of the leaves. Streptomyces sp. ZS-18 showed good growth on ISP 1, ISP 2, ISP 3, ISP 5 and ISP 7 medium and moderate growth was observed in ISP 4 and ISP 6 medium. In addition, Actinobacteria were tested for antagonistic activity against plant pathogens. The isolate ZS-18 in the pot trial experiment showed increased growth in comparison with control and cow dung manure.

Unmanned air vehicle based high resolution imagery for chlorophyll estimation using spectrally modified vegetation indices in vertical hierarchy of citrus grove

Chlorophyll is a significant indicator of plant vigor and plant health which enables to estimate final crop yield. Current experiment was conducted to acquire high resolution multispectral image from UAV and accurately estimate chlorophyll contents using spectral chlorophyll indices in vertical crop plantation. Ground truthing was performed by taking chlorophyll values from citrus leaves using (SPAD-502 Minolta) chlorophyll meter. Five vegetation indices including DVI (difference vegetation index), RDVI (renormalized difference vegetation index), MTVI2 (modified triangular vegetation index 2), SARVI (soil and atmospherically resistant vegetation index) and Iron Oxide index were derived to determine most robust index by developing linear regression models among VIs and citrus leaves chlorophyll contents. Triangular indices (MTVI2) expressed highest correlation coefficient R2 = 0.81 with high accuracy and precision for ground truthed citrus leaves chlorophyll contents. MTVI2 and SARVI were proven as most robust indices to map spatial differences in chlorophyll content with R2 = 0.81 and R2 = 0.76, respectively. DVI, RDVI and Iron Oxide index resulted in less efficient VIs in vertical plantations with coefficient of determination (R2) values 0.57, 0.59 and 0.69, respectively. Moreover, in this study low cost multispectral UAV (DJI Phantom4 Pro) was used to monitor biophysical status of crop in real time and develop an on-field cost efficient, precise and accurate agricultural monitoring system against conventional techniques. This article has covered the aspect of chlorophyll detection using spectrally modified VIs in vertical hierarchy that was a citrus field and manual efforts were used by taking the help of UAV. The novel aspect of this study covers the chlorophyll detection in the trees which is not studied by the scientists as a part of Remotesensing.

Correction to: Smartphone-assisted real-time estimation of chlorophyll and carotenoid concentrations and ratio using the inverse of red and green digital color features

Correction to: Smartphone-assisted real-time estimation of chlorophyll and carotenoid concentrations and ratio using the inverse of red and green digital color features

Smartphone-assisted real-time estimation of chlorophyll and carotenoid concentrations and ratio using the inverse of red and green digital color features

Chlorophyll (Chl) concentration is a reliable indicator of leaf nitrogen content and plant health status. Currently available methods for image-based Chl estimation require complex mathematical derivations and high-throughput imaging set-up along with multiplex image-preprocessing steps. Further, the influence of carotenoid (CAR)concentration has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl concentration and Chl/CAR ratio. Color features were obtained from RGB (red, green, blue) images of spinach leaves using a smartphone, and inverse R and G values were calculated. Correlation analysis of color indices and photosynthetic pigment (PP) concentrations was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP concentration. 1/R and 1/G indices showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR concentrations, respectively. Furthermore, 1/R + 1/G and [1/R]/[1/G] presented strong positive linear correlation with Chl + CAR (r2 = 0.95) and Chl/CAR (r2 = 0.88), respectively. PCA confirmed the association of color indices with the respective PP features, which were subsequently estimated using the correlation models. A smartphone-based companion application was developed using the linear models for non-invasive, real-time estimation of Chl concentration and Chl/CAR ratio. 1/R and 1/G indices indicate the concentrations of Chl and CAR, respectively, via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl concentration and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.

Rapid and non-destructive leaf chlorophyll estimation of Fig (Ficus carica L.) cv. Iraqi grown on different root zone spatial limitation and controlled porosity level under greenhouse condition

The estimation of chlorophyll content in leaves by the chlorophyll meter (SPAD 502) is more favourable than by the extraction method for studies on photosynthesis or senescence where the total chlorophyll is estimated on the same leaf over time. However, till date there is lack of information available on leaf chlorophyll content of Fig (Ficus carica L.) cv. Iraqi grown in containers under different root zone spatial limitation and controlled porosity level. Hence, this study aim to determine the effect of both factors on leaf chlorophyll content of the crop. The experiment was design in randomized complete block design (RCBD) with four replications. The Soil-Plant Analyses Development (SPAD) chlorophyll meter (Minolta Camera Co., Ltd., Japan), has been used for instantly measuring the amount of chlorophyll present in plant leaves. Based on the results, the application of Mix 3 (50% sand: 50% clay) with high root zone spatial gave the highest chlorophyll content compared to other treatment. The application of the right root zone and porosity level may gave positive effect on leaf chlorophyll content of F. carica cv. Iraqi grown under greenhouse condition.

Chlorophyll‐Based Model to Estimate Underwater Photosynthetically Available Radiation for Modeling, In‐Situ, and Remote‐Sensing Applications

AbstractAccurate estimation of the underwater light field associated with photosynthetically available radiation (PAR) is critical to compute phytoplankton growth rate and net primary production (NPP), and to assess photo‐physiological response of phytoplankton, such as changes in cellular pigmentation. However, methods to estimate PAR used in many previous studies lack in accuracy, likely resulting in significant bias in light‐dependent products such as NPP derived from remote sensing, model simulations, or autonomous platforms. Here we propose and validate a new model for more accurate estimation of the subsurface PAR profile which uses chlorophyll concentration as its input. Validation is performed using 1,744 BGC‐Argo profiles of chlorophyll fluorescence that are calibrated with surface satellite‐derived chlorophyll concentration over their lifetime. The independent verification with the float's PAR sensors confirms the accuracy of satellite chlorophyll estimate worldwide and in the Southern Ocean in particular.

Regional warming exacerbates match/mismatch vulnerability for cod larvae in Alaska

The match-mismatch hypothesis predicts that variability in spring primary production will increase starvation risk for marine fish populations. However, it is often unclear whether the synergic effects of temperature, phytoplankton phenology and metabolic demands of fish support these prediction within and across regions. In this study, we combine experimentally-derived rates of larval survival in relation to water temperature with satellite remote-sensing estimates of chlorophyll a and sea surface temperature to predict habitat quality of first feeding Pacific cod (Gadus macrocephalus) larvae in two adjacent large marine ecosystems; southeast Bering Sea (BS) and Gulf of Alaska (GOA). Predictions are made from 1998 to 2019, a period comprised of both warm and cool springs, followed by a series of extreme warming events (heatwaves) beginning in 2014. Model results indicate that the drivers of a mismatch between cod larvae and their food is fundamentally different between the two systems. In the GOA, larval habitat suitability is highly dependent on interannual fluctuations in sea surface temperature that regulate yolk reserves in embryos. In contrast, larval habitat suitability in the BS was more sensitive to the onset of the surface expression of Chl a production. The combined effects of changes in timing of Chl a and increased metabolic demands of larvae were captured by the model during the GOA marine heatwave (2014–16, 2019), which predicted significant loss of larval habitat across the entire region. These habitat models integrate important spatial, temporal and physiological components of match-mismatch theory that can be used to examine marine fish populations experiencing different rates of climate stress across regions.

Does Leaf Waxiness Confound the Use of NDVI in the Assessment of Chlorophyll When Evaluating Genetic Diversity Panels of Wheat?

Ground and aerial-based high throughput phenotyping platforms (HTPPs) to evaluate chlorophyll-related traits have been utilized to predict grain yield in crops including wheat (Triticum aestivum L.). This study evaluated chlorophyll-related and other physiological and yield traits in a panel of 318 Nepali spring wheat genotypes, termed the Nepali Wheat Diversity Panel (NWDP). Field experiments were conducted using an alpha-lattice design in Nepal and Canada. Chlorophyll-related traits were evaluated with a Soil Plant Analysis Development (SPAD) meter and the normalized difference vegetation index (NDVI) using a handheld GreenSeeker and an Unmanned Aerial Vehicle (UAV). Relative leaf epicuticular waxiness was recorded using visual assessments. There was a significant positive association (p &lt; 0.001) between waxiness and SPAD-based chlorophyll estimates, and both of these traits displayed a significant positive relationship with grain yield. However, unexpectedly, NDVI derived from both GreenSeeker and UAV was negatively associated with waxiness and grain yield. The results obtained after segregating the trait means into groups based on waxiness scores and breeding history of genotypes indicated that waxiness along with precipitation could be affecting the multispectral reflectance. These results suggest that caution should be taken when evaluating a large and diverse wheat population for leaf chlorophyll using high-throughput NDVI methods.

Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and Cyanobacteria cell number: the Cantareira System reservoirs (São Paulo, Brazil).

Satellite images were used to assess surface water quality based on the concentration of chlorophyll a (chla), light penetration measured by the Secchi disk method (SD), and the Cyanobacteria cells number per mL (cyano). For this case study, six reservoirs interconnected were evaluated, comprising the Cantareira System (CS) in São Paulo State (Brazil). The work employed Sentinel-2 images from 2015 to 2018, SNAP image processing software, and the native products conc_chl and kd_z90max, treated using Case 2 Regional Coast Color (C2RCC) atmospheric correction. The database was obtained from CETESB, the agency legally responsible for operation of the Inland Water Quality Monitoring Network in São Paulo State. The results demonstrated robustness in the estimates of chla (RMSE = 3.73; NRMSE% = 19%) and SD (RMSE = 2,26; NRMSE% = 14%). Due to the strong relationship between cyano and chla (r2 = 0.84, p < 0.01, n = 90), both obtained from field measurements, there was also robustness in cyano estimates based on the estimates of chla from the satellite images. The data revealed a clear pattern, with the upstream reservoirs being more eutrophic, compared to those downstream. There were evident concerns, about water quality, particularly due to the high numbers of Cyanobacteria cells, especially in the upstream reservoirs.

Insights into role of STP13 in sugar driven signaling that leads to decrease in photosynthesis in dicot legume crop model (Phaseolus vulgaris L.) under Fe and Zn stress.

Mineral (Fe/Zn) stress significantly affects fundamental metabolic and physiological responses in plants that results in reduction of plant growth and development. Deficiency of these micronutrients leads to inhibition of photosynthesis by having impact on various crucial biological processes like protein synthesis, primary and secondary metabolism and carbohydrate partitioning between source and sink tissues. In the present study, common bean variety Shalimar French Bean-1 (SFB-1) plants were used as an experimental material and were grown under in vitro condition on four different MGRL media i.e. normal MGRL medium (Control), MGRL without Fe (0-Fe), MGRL without Zinc (0-Zn) and MGRL with excess Zn (300-Zn) for 21days under optimum conditions. Shoot and root tissues from all the treatments were harvested and further subjected to estimation of total chlorophyll, total sugar and extraction of total RNA for differential gene expression of sugar transporter 13 (STP13). We observed significant decrease in total chlorophyll content in samples harvested from mineral stress plants. However, the concentration of total sugar and fold expression of STP13 gene was significantly higher in shoots of Fe/Zn stressed and in roots of 300-Zn plants. We observed higher accumulation of sugar under stress condition that correlated with high expression of sugar transporter 13 (STP 13). Further, we observed decrease in the chlorophyll content under stress conditions. Based on these findings, we propose the role of sugar driven signaling in decreasing photosynthesis in case of common bean. The decrease in photosynthesis is confirmed by observing significant decrease in chlorophyll content in stressed plants.

Efficient estimation of chlorophyll a concentration in artificial upwelling

Artificial upwelling is an effective method to boost the primary productivity in the sea area, which provides a strong support for fishery. The chlorophyll a concentration is a key indicator to evaluate the marine productivity. This paper aims to provide a high-precision model to approximate the chlorophyll a concentration of artificial upwelling. After eliminating the abnormal values from the experimental data, we first used the global linear fitting (GLF) and the global quadratic fitting (GQF) models respectively to obtain reference errors. In order to improve the precision, we introduced the idea of the piecewise fitting to the model. The training set was partitioned into two, four and eight segments to find the optimal number of segments that the model can achieve the best precision. These models calculate the least squares solutions of the error function on each segment. To satisfy the engineering requirement, which is the continuity at boundary points, constraints were added to the boundary points of adjacent segments. Further, the quadratic fitting was proposed and applied on segments where the errors of fitting were relatively large when applying the constrained piecewise linear fitting (CPLF) model, namely the local quadratic fitting (LQF) model. Finally, we partitioned one more variable on the piecewise linear fitting model to examine if the predictive precision can be enhanced. Compared with the work of predecessors, where the GLF, GQF models and intelligent algorithms were applied to this issue, the results of our tested models showed higher precision in predicting chlorophyll a concentration and less complexity in model operation.

Long-term trends in the frequency and magnitude of upwelling along the West Coast of the South Island, New Zealand, and the impact on primary production

ABSTRACT Satellite-derived estimates of sea surface temperature (SST), chlorophyll (Chl), and fluorescence line height (FLH), from 22 sites ∼20 km offshore from the West Coast of the South Island, New Zealand (WCSI) are used to investigate long-term trends in SST, the frequency and magnitude of cool and warm events, and their impacts on SSC and FLH. WCSI trends have been towards higher temperature and less chlorophyll, with a 0.83°C rise in mean along-shore SST since 1982, and a 0.1 mg Chl m−3 drop in mean along-shore SSC since 2002. With respect to the trend, there were 13 extreme cool and 14 extreme warm events between 1982 and 2019, with mean values of −2.55 and 2.62°C, respectively. There was no measurable change in the frequency of extreme events, but there is a suggestion that extreme events are becoming warmer. During extreme events, Chl and FLH show inverse relationships with SST, suggesting that there is an underlying response of increased primary production to lower temperatures. Likely mechanisms are that upwelling brings nutrients up from depth, or that Ekman flow forces freshwater farther offshore, simultaneously introducing nutrients into the offshore region and providing a more stable water column allowing production.

A bio-optical model for the estimation of chlorophyll a using animal-borne instruments in an optically complex ecosystem

Studies using marine animals instrumented with biologging devices to estimate phytoplankton biomass have typically omitted continental shelf regions due to the confounding effects of optically active constituents other than phytoplankton present. The lack of algorithms for these regions is problematic, as they are some of the most biologically productive in the world and are often inhabited by the species of interest. We developed a bio-optical model to estimate chlorophyll a concentration (chl a) using light attenuation (LA) measured using a standard oceanographic instrument in an optically complex water body that is applicable to data collected by animal-borne devices. To achieve this, we conducted a replicated experiment to compare measurements made using time-depth-light recorders (TDLRs) to those of a standard oceanographic instrument (the HyperPro) in an adjacent water body, the Bedford Basin, Nova Scotia, Canada. Measurements of LA made by TDLRs were comparable to those of the HyperPro atdepth. The best supported bio-optical model for the estimation of chl a included both LA measured by the HyperPro and season as a fixed effect. The use of animal-borne devices to collect subsurface chl a data not only provides an opportunity to collect valuable oceanographic data but also allows for the exploration of broader ecological questions relating to the influence of primary productivity on the movement patterns of wide-ranging marine species.

FASPECT: A model of leaf optical properties accounting for the differences between upper and lower faces

Many plant species have distinct optical properties between upper and lower leaf faces. These differences between faces are mainly attributed to the non-homogeneous distribution of absorbing and scattering materials within the leaf depth as well as particular surface features of both epidermises. We proposed the FASPECT model which is an evolution of the PROSPECT model to describe the differences in reflectance and transmittance between leaf faces. The upper and lower epidermis layers are characterized by distinct wavelength-independent reflectivities. Leaf mesophyll is made of a palisade and a spongy parenchyma layers using two parameters that describe the distribution of pigments and leaf structure between these two layers. As compared to the original PROSPECT model that treats the two leaf faces symmetrically, six additional parameters are required to describe the differences in leaf optical properties between the two faces. The specific absorption coefficients of chlorophyll and carotenoids have been recalibrated for the FASPECT model over a dedicated dataset. FASPECT was validated over eight datasets and compared with PROSPECT-5 and PROSPECT-D as well as with the DLM model that also accounts for the differences between the two faces. Results show that the FASPECT model simulates accurately the reflectance and transmittance of the two faces for species presenting distinct reflectance and transmittance properties between the faces. FASPECT outperforms PROSPECT-5 and PROSPECT-D, while providing generally more realistic simulations as compared to DLM. The capacity of the FASPECT model to retrieve leaf biochemical composition from reflectance and transmittance measurements was also evaluated. Marginal improvement is observed for the estimation of chlorophyll, carotenoids, and water content. Conversely, significant improvement is observed for dry matter content estimation. Conclusions are finally drawn on the interest and limits of the FASPECT model.

Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone

Leaf biochemical and structural traits are vegetation characteristics related to various physiological processes. Taking advantage of the physical relationship between optical properties and leaf biochemistry, field-based spectroscopy has allowed for the rapid estimation of leaf biochemical constituents and repeated non-destructive measurements through time. Leaf constituent retrieval from leaf optical properties following inversion of the physically-based radiative transfer model PROSPECT is now a popular method, but some cases prompt poor retrieval success and this approach requires a strict inversion procedure. We investigated the performances of different inversion procedures for the estimation of leaf constituents, specifically chlorophyll a and b, carotenoids, water (EWT), and dry matter (LMA) from >1400 broadleaf samples, including the definition of optimal spectral subdomains, and the use of leaf reflectance or transmittance alone. We also developed a strategy to obtain prior information on the leaf structure parameter (N) in PROSPECT, when only reflectance or transmittance is measured, and examined the influence of this prior information in combination with different inversion procedures. We found that using the full domain of reflectance or transmittance only systematically leads to suboptimal estimation of chlorophyll a and b, carotenoids, EWT, and LMA, due to either the combined absorption of multiple constituents or inaccurate estimation of the N parameter. Our study confirms that the selection of optimal spectral subdomains leads to improved estimation of all leaf constituents, from 700 to 720 nm for chlorophyll a and b, 520–560 nm for carotenoids, and from 1700 to 2400 nm for EWT and LMA. Prior information on N, computed directly from the spectra, leads to systematic improved estimation of leaf constituents when only reflectance or transmittance is measured, with reductions in normalized root mean square error from 8 to 37%. We strongly recommend using optimal subdomains when inverting PROSPECT to retrieve leaf constituents, and with the availability of only reflectance or transmittance we further recommend the use of prior information on the N parameter.

Impact of bovine serum albumin – A protein corona on toxicity of ZnO NPs in environmental model systems of plant, bacteria, algae and crustaceans

Zinc oxide nanoparticles (ZnO NPs) are widely applied in industrial, household and medical areas that lead to its discharge and accumulation in ecosystem. Here, the toxic effect of ZnO NPs in presence and absence of bovine serum albumin (BSA) was analyzed. The difference in toxicity of bare ZnO and BSA interacted ZnO was studied with different environmental models. P. aeruginosa and S. aureus were used as model bacterial systems. Toxicity against bacteria was determined by employing plate count method. C. pyrenoidsa was used as algal system for evaluating toxicity and it was determined by chlorophyll estimation assay. Daphnia sp. was chosen as crustacean system model. A. cepa root cells were chosen as plant model. ZnO NPs increased the ROS formation, lipid peroxidation and oxidative stress and it reduced in the presence of BSA. The cytotoxicity, chromosomal aberrations and micronuclei (MN) index of A. cepa were increased after ZnO NPs treatment. Same time the toxic effect was decreased in case of BSA coated ZnO NPs. The NPs toxic potential on the organisms decreased in the order of P. aeruginosa (LC50–0.092 mg/L) > S. aureus (LC50–0.33 mg/L) > Daphnia sp (LC50–0.35 mg/L) > C. pyrenoidosa (LC50–8.17 mg/L). LC50 in presence of BSA was determined to be 18.45, 26.24, 17.27 and 53.97 mg/L for P. aeruginosa, S. aureus, Daphnia sp and C. pyrenoidosa respectively. Therefore, the report suggests that BSA stabilized ZnO NPs could be more amenable towards applications in biotechnology and bioengineering.