High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Nutrients from spawning salmon influence leaf area, tissue density, and nitrogen-15 in riparian plant leaves

Nutrient subsidies have significant impacts on ecosystems by connecting disjunct habitats, often through long-distance animal migrations. Salmon migrations on the North Pacific coasts provide these kinds of nutrient subsidies from senescent fish at the end of their life cycle, which can have significant ecological effects on terrestrial species. This can include impacts on individuals, populations, and communities, where shifts in community composition towards plant species that indicate nitrogen-rich soils have been documented. We investigated the effects of variation in salmon spawning density on the leaf traits of four common riparian plant species on the central coast of British Columbia, Canada. We found that all plant species had higher foliar salmon-derived nitrogen on streams with a higher spawning density. Three of the four species had larger leaves, and one species also had higher leaf mass per area on streams with more salmon. However, we found no differences in leaf greenness or foliar percent nitrogen among our study streams. These results demonstrate that nutrient subsidies from spawning salmon can have significant impacts on the ecology, morphology, and physiology of riparian plants, which lends support to a mechanism by which certain plants are more common on productive salmon streams.

Enhancing sustainable crop cultivation: The impact of renewable soil amendments and digestate fertilizer on crop growth and nutrient composition

Utilizing digestate as a fertilizer enhances soil nutrient content, improves fertility, and minimizes nutrient runoff, mitigating water pollution risks. This alternative approach replaces commercial fertilizers, thereby reducing their environmental impact and lowering greenhouse gas emissions associated with fertilizer production and landfilling. Herein, this study aimed to evaluate the impact of various soil amendments, including carbon fractions from waste materials (biochar, compost, and cocopeat), and food waste anaerobic digestate application methods on tomato plant growth (Solanum lycopersicum) and soil fertility. The results suggested that incorporating soil amendments (biochar, compost, and cocopeat) into the potting mix alongside digestate application significantly enhances crop yields, with increases ranging from 12.8 to 17.3% compared to treatments without digestate. Moreover, the combination of soil-biochar amendment and digestate application suggested notable improvements in nitrogen levels by 20.3% and phosphorus levels by 14%, surpassing the performance of the those without digestate. Microbial analysis revealed that the soil-biochar amendment significantly enhanced biological nitrification processes, leading to higher nitrogen levels compared to soil-compost and soil-cocopeat amendments, suggesting potential nitrogen availability enhancement within the rhizosphere's ecological system. Chlorophyll content analysis suggested a significant 6.91% increase with biochar and digestate inclusion in the soil, compared to the treatments without digestate. These findings underscore the substantial potential of crop cultivation using soil-biochar amendments in conjunction with organic fertilization through food waste anaerobic digestate, establishing a waste-to-food recycling system.

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

Comparative study of the doses of cytokinin in the quality of caigua (Cyclanthera pedata L.) in Peru

Bioavailability of nutrients, the scarcity of synthetic fertilisers, and the rising cost of fuel have all contributed to an increase in production costs, which has in turn reduced crop productivity and led scientists to seek out new methods to ensure high-quality output. In this context, various cytokinins dosages were tested in Peru to see whether they affected the quality of caigua, in an effort to address these issues. To mitigate these problems, a pot experiment was carried out to check the effects of various doses of cytokinin in the quality of caigua in Peru. The experiment consisted of 5 treatments including (0, 50, 100, 150 and 200 mL of cytokinin) by using (Anthesis Plus per 200 L of water) as a source, each with three replicates and placed following a randomized complete block design (RCBD). Treatment with 100 mL of cytokinins foliar analysis resulted in a caigua length of 18.9 cm, an increase in diameter of 5.65 cm, and an improvement in pulp thickness of 7.60 millimeters. Physiological parameters of caigua plants taken after 45 days of sowing were considerably improved with the same treatment. Similarly, N, K and Zn concentration in leaf was higher in case of 100 mL of cytokinins foliar analysis. Therefore, policymakers must advise using the recommended quantity of cytokinins to bring about regime transition, and formers can gain by injecting 100 mL of cytokinins to boost production and the economy. It was concluded that the adequate dose of cytokinins is in treatment T3, which raised value of potassium concentration in leaves, this influenced optimal development, strengthening against environmental stress and therefore quality. For this reason, research was carried out on the comparative study of cytokinin doses in the quality of caigua in Peru; the objective was to determine the appropriate dose to obtain higher quality fruit. Likewise, it was underlined that the objective was to employ an ecological alternative of plant origin such as the usage of phytohormone that stimulates the growth of the plant and consequently the quality of the fruit. The obtained the results were served as a recommendation for farmers in the area.

The Impact of 2-Ketones Released from Solid Lipid Nanoparticles on Growth Modulation and Antioxidant System of Lactuca sativa

2-Ketones are signal molecules reported as plant growth stimulators, but their applications in vegetables have yet to be achieved. Solid lipid nanoparticles (SLNs) emerge as a relevant nanocarrier to develop formulations for the controlled release of 2-ketones. In this sense, seedlings of Lactuca sativa exposed to 125, 375, and 500 µL L-1 of encapsulated 2-nonanone and 2-tridecanone into SLNs were evaluated under controlled conditions. SLNs evidenced a spherical shape with a size of 230 nm. A controlled release of encapsulated doses of 2-nonanone and 2-tridecanone was observed, where a greater release was observed as the encapsulated dose of the compound increased. Root development was strongly stimulated mainly by 2-tridecanone and leaf area (25-32%) by 2-nonanone. Chlorophyll content increased by 15.8% with exposure to 500 µL L-1 of 2-nonanone, and carotenoid concentration was maintained with 2-nonanone. Antioxidant capacity decreased (13-62.7%) in L. sativa treated with 2-ketones, but the total phenol concentration strongly increased in seedlings exposed to some doses of 2-ketones. 2-Tridecanone strongly modulates the enzymatic activities associated with the scavenging of H2O2 at intra- and extracellular levels. In conclusion, 2-ketones released from SLNs modulated the growth and the antioxidant system of L. sativa, depending on the dose released.

Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R_p=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R_p=0.9161) for SP and CARS-ELM (R_p=0.9021) for MDA, all with R_p higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging

Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R², 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.

Effects of invasive Rosa rugosa on Baltic coastal dune communities depend on dune age

Rosa rugosa Thunb. (Japanese Rose) is one of the most invasive species in Europe. It spreads spontaneously in coastal areas of western, central and northern Europe, posing a threat to dune habitats, including those indicated in the EU Habitats Directive as particularly valuable. R. rugosa has already been reported to displace native plants and alter soil properties. However, little is known about how these effects are mediated by the habitat context or the invader condition (health, ontogenetic stage). This study addressed that gap by examining vegetation and soil in 22 R. rugosa-invaded sites, half of which were in yellow dunes and the other half in grey dunes, i.e. two habitats representing the earlier and later stages of dune succession. The study was conducted on the Hel Peninsula (Poland’s Baltic coast). R. rugosa had a significant impact on dune vegetation, but the impact was strongly dependent on the habitat type. In the yellow dune sites, R. rugosa outcompeted most resident plant species, which translated into a strong decline in their total cover and richness. The invasion was almost not accompanied by changes in soil properties, suggesting that it affected the resident vegetation directly (through space takeover and shading). In the grey dunes, R. rugosa caused a shift in species composition, from that characteristic of open communities to that typical of forests. In this habitat, a significant increase in the soil organic layer thickness under R. rugosa was also observed, which means that both direct and indirect effects of the invasion on the vegetation should be assumed. Finally, a negative relationship was found between the total chlorophyll content in R. rugosa leaves and the parameters of resident plant communities, showing that the invasion effects can vary not only across habitats, but also with the condition of the invader. The results may have practical implications for managing R. rugosa invasions in coastal sand dune systems. Since R. rugosa accelerates grey dune succession, protecting this habitat may be more urgent and, at the same time, more complicated than protecting dunes at the earlier stages of development.

Design and Implementation of a Low-Cost Chlorophyll Content Meter

Chlorophyll meters are portable devices used to assess and improve plants' nitrogen management and to help farmers in the determination of the health condition of plants through leaf greenness measurements. These optical electronic instruments can provide an assessment of chlorophyll content by measuring the light passing through a leaf or by measuring the light radiation reflected from its surface. However, independently of the main principle of operation and use (e.g., absorbance vs. reflectance measurements), commercial chlorophyll meters usually cost hundreds or even thousands of euros, making them inaccessible to growers and ordinary citizens who are interested in self-cultivation, farmers, crop researchers, and communities lacking resources in general. A low-cost chlorophyll meter based on light-to-voltage measurements of the remaining light after two LED light emissions through a leaf is designed, constructed, evaluated, and compared against two well-known commercial chlorophyll meters, the SPAD-502 and the atLeaf CHL Plus. Initial tests of the proposed device on lemon tree leaves and on young Brussels sprouts plant leaves revealed promising results compared to the commercial instruments. The coefficient of determination, R2, was estimated to be 0.9767 for the SPAD-502 and 0.9898 for the atLeaf-meter in lemon tree leaves samples compared to the proposed device, while for the Brussels sprouts plant, R2 was estimated to be 0.9506 and 0.9624, respectively. Further tests conducted as a preliminary evaluation of the proposed device are also presented.

Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae

This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R² of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R² of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.

Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf

Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect and distinguish every weed species with high accuracy. Training convolutional neural networks for detecting weeds susceptible to herbicides can offer a new strategy for implementing site-specific weed detection in turf. DenseNet, EfficientNet-v2, and ResNet showed high F₁ scores (≥0.986) and MCC values (≥0.984) to detect and distinguish the sub-images containing dollarweed, goosegrass, old world diamond-flower, purple nutsedge, or Virginia buttonweed growing in bermudagrass turf. However, they failed to reliably detect crabgrass and tropical signalgrass due to the similarity in plant morphology. When training the convolutional neural networks for detecting and distinguishing the sub-images containing weeds susceptible to ACCase-inhibitors, weeds susceptible to ALS-inhibitors, or weeds susceptible to synthetic auxin herbicides, all neural networks evaluated in this study achieved excellent F₁ scores (≥0.995) and MCC values (≥0.994) in the validation and testing datasets. ResNet demonstrated the fastest inference rate and outperformed the other convolutional neural networks on detection efficiency, while the slow inference of EfficientNet-v2 may limit its potential applications. Grouping different weed species growing in turf according to their susceptibility to herbicides and detecting and distinguishing weeds by herbicide categories enables the implementation of herbicide susceptibility-based precision herbicide application. We conclude that the proposed method is an effective strategy for site-specific weed detection in turf, which can be employed in a smart sprayer to achieve precision herbicide spraying.

Triphasic partitioning of mixed Scenedesmus and Desmodesmus for nutrients' extraction and chlorophyll composition prediction for algae bloom

Overgrowth of microalgae will result in harmful algae blooms that can affect the aquatic ecosystem and human health. Therefore, the quantitation of chlorophyll pigments can be used as an indicator of algae bloom. However, it is difficult to monitor the geographical and temporal distribution of chlorophyll in the aquatic environment. Accordingly, an innovative and inexpensive method based on the red-green-blue (RGB) image analysis was utilized in this study to estimate the microalgae chlorophyll content. The digital images were acquired using a smartphone camera. The colour index was then evaluated using software and associated with chlorophyll concentration significantly. A regression model, using RGB colour components as independent variables to estimate chlorophyll concentration, was developed and validated. The Green in the RGB index was the most promising way to estimate chlorophyll concentration in microalgae. The result showed that acetone was the best extractant solvent with a high R-squared value among the four extractant solvents. Next, the isolation of useful biomolecules, such as proteins, fatty acids, polysaccharides and antioxidants from the microalgae, has been recognized as an alternative to regulating algae bloom. Microalgae are shown to produce bioactive compounds with a variety of biological activities that can be applied in various industries. This study evaluates the biochemical composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp. using the liquid triphasic partitioning (TPP) system. The findings from analytical assays revealed that the biomass consisted of varied concentrations of carbohydrates, protein, and lipids. Phenolic compounds and antioxidant activity were at 60.22 mg/L and 90.69%, respectively.

Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples.

OsbHLHq11, the Basic Helix-Loop-Helix Transcription Factor, Involved in Regulation of Chlorophyll Content in Rice

Simple Summary

R-ice is one of the world’s most important staples; a growing population and declining rates of growth in rice yields will present significant challenges ahead. After the heading stage, the photosynthetic ability of the flag leaf has a great effect on the yield of rice, and this ability can be evaluated by leaf color, chlorophyll content, quantum yield, etc. Our purpose was to screen candidate genes that affect photosynthetic efficiency through QTL mapping analysis and predict their function through protein interaction and homology sequence analysis. The results suggest that OsbHLHq11 may be involved in chlorophyll accumulation and enhancing photosynthetic efficiency, which may lead to high yields.

Abstract

Photosynthesis is an important factor in determining the yield of rice. In particular, the size and efficiency of the photosynthetic system after the heading has a great impact on the yield. Research related to high-efficiency photosynthesis is essential to meet the growing demands of crops for the growing population. Chlorophyll is a key molecule in photosynthesis, a pigment that acts as an antenna to absorb light energy. Improvement of chlorophyll content characteristics has been emphasized in rice breeding for several decades. It is expected that an increase in chlorophyll content may increase photosynthetic efficiency, and understanding the genetic basis involved is important. In this study, we measured leaf color (CIELAB), chlorophyll content (SPAD), and chlorophyll fluorescence, and quantitative trait loci (QTL) mapping was performed using 120 Cheongcheong/Nagdong double haploid (CNDH) line after the heading date. A major QTL related to chlorophyll content was detected in the RM26981-RM287 region of chromosome 11. OsbHLHq11 was finally selected through screening of genes related to chlorophyll content in the RM26981-RM287 region. The relative expression level of the gene of OsbHLHq11 was highly expressed in cultivars with low chlorophyll content, and is expected to have a similar function to BHLH62 of the Gramineae genus. OsbHLHq11 is expected to increase photosynthetic efficiency by being involved in the chlorophyll content, and is expected to be utilized as a new genetic resource for breeding high-yield rice.