Search Results (205)

Parameters of reflected light, measured in narrow or broad spectral bands, are widely analyzed for remote and proximal sensing of plant responses to stressors. Specifically, parameters of reflectance in red (R), green (G), and blue (B) spectral bands measured using simple color images can be sensitive to characteristics of plants. The conventional view is that RGB reflectance primarily reveals long-term changes in plants (days, weeks, etc.). In this study, we investigated light-induced changes in RGB reflectance in wheat (Triticum aestivum L.) and pea (Pisum sativum L.) leaves. Illumination increased this reflectance for about 10 min in wheat and about 15–20 min in pea; these changes relaxed after light intensity was decreased. The changes in RGB reflectance were strongly related to the effective quantum yield of photosystem II and non-photochemical quenching of chlorophyll fluorescence under high light intensity; these relations were absent under low light intensity. We hypothesized that changes in both RGB reflectance and photosynthetic parameters were related to the light-induced changes in chloroplast localization. A simple mathematical model of optical properties and photosynthesis in leaves was developed; results of the model-based analysis supported the proposed hypothesis. Experimental analysis of the dynamics of light transmittance additionally supported this hypothesis. Our results thus show that RGB imaging can be sensitive to fast changes in plants. Full article

Coloured and variegated leaves are common in shade-tolerant ornamentals. However, it remains unclear whether their photosynthetic performance is determined mainly by pigment abundance or by the organisation of chloroplasts and thylakoids. We tested this in three Epipremnum aureum phenotypes (‘Neon’, ‘Golden’ and ‘Jade’) that share a genetic background but contrast in leaf colour, chloroplast density and thylakoid membrane abundance. Plants were grown in a greenhouse and assessed by hyperspectral and thermal imaging, infrared gas exchange analysis, chlorophyll a fluorescence measurements, and structural, ultrastructural and biochemical analyses. Traits were integrated by principal component analysis, with the quantum yield of CO₂ assimilation per absorbed photon (αCO_2,abs) as the response variable. ‘Neon’ leaves had high specific leaf area and approximately 55% lower maximum Rubisco carboxylation (Vc_MAX) and electron transport capacity (J_MAX) than ‘Jade’, as well as reduced chloroplast and thylakoid abundance and warmer canopies, despite carotenoid enrichment. JIP-test parameters and fluorescence light–response curves showed high absorption and dissipation per PSII reaction centre, elevated excitation pressure, modest non-photochemical quenching (NPQ), low αCO_2,abs, small carbohydrate pools and low intrinsic water-use efficiency. ‘Jade’ leaves developed thick mesophyll with dense chloroplast populations, extensive thylakoid networks, highest NPQ, cool canopies and large carbohydrate reserves, whereas ‘Golden’ leaves combined thin laminae and intermediate chloroplast–thylakoid organisation with early light saturation of CO₂ assimilation and the highest intrinsic water-use efficiency. Principal component analysis revealed a structural axis of chloroplast and thylakoid organisation that better predicted αCO_2,abs, net carbon gain and canopy temperature than pigment abundance. In variegated E. aureum, ‘photon economy’ is therefore governed primarily by chloroplast and thylakoid membrane organisation and abundance rather than by carotenoid accumulation. Full article

Due to climate change, seedling damage caused by drought stress is expected to increase in both afforestation sites and nurseries. Therefore, to ensure stable seedling production under high-temperature conditions and to cultivate seedlings with enhanced drought tolerance through hardening treatments, the development of an effective irrigation system is required. Conventional physiological methods for non-destructive drought detection, such as chlorophyll fluorescence and leaf temperature measurements, require expensive and manual operation, thereby limiting their real-time applicability in forest nurseries. This study evaluated the applicability of using image-based leaf angle measurements for drought stress detection in Quercus acutissima Carruth. seedlings. One-year-old seedlings were grown under two water regimes—well-watered (CT: control) and unwatered (DT: drought)—through Day 8. Statistical analyses (RMANOVA) revealed that changes in the leaf angle parameter PMD–MD (the difference between the previous and current measurement days) showed treatment effects similar to those of the physiological responses ΦNO (quantum yield of non-regulated energy dissipation) and qL (fraction of open PSII reaction centers) to drought on Day 6. Leaf angle reflected drought stress but did not precede physiological changes, indicating its role as a complementary rather than an early indicator. Multiple regression models identified AT (air temperature), SM (soil moisture), Fm′ (maximum fluorescence in the light-adapted state), and VPD (vapor pressure deficit) as the main factors influencing leaf angle variation. Although leaf angle was affected by combined environmental stresses such as high temperature, it was less sensitive to heat stress than physiological responses based on RMANOVA results. These results indicate the potential of image-based leaf angle measurements for drought stress detection. To establish plant-based smart irrigation systems, future studies should validate and refine this approach using larger datasets. Full article

Chloroplast development plays a crucial role in plant de-etiolation, a process in which plants switch from growth in darkness to light-driven development, known as photomorphogenesis. This study provides evidence that CIA2 (Chloroplast Import Apparatus 2) and CIL (CIA2-Like) contribute to chloroplast biogenesis, likely by affecting and regulating PSII activity and related gene expression. Although their precise molecular roles remain unclear, our findings support their possible involvement in chloroplast development. This is indicated by downregulation of foliar chlorophyll content, chlorophyll a fluorescence parameters, chloroplast size, and gene expression of PSII molecular markers in the cia2cil double mutant during de-etiolation. Chlorophyll a fluorescence and quantitative gene expression analysis during de-etiolation revealed a significant reduction in PSII maximal efficiency and non-photochemical quenching, as well as deregulated expression of genes such as LHCB2.1 and psbA. According to the immunoblotting and microscopy imaging results, there is an impaired function of PSII and a compromised ultrastructure of the chloroplast membranes in cia2cil plants. However, in CIA2p::CIA2_cia2cil and 35Sp::CIA2_cia2cil complementation lines, reversion of this phenotype was observed. These results suggest a supporting role for CIA2 and CIL in the plant de-etiolation process, expanding our understanding of chloroplast biogenesis regulation. Full article

Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp²) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products. Full article

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article

Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture. Full article

Photosynthesis (PS) is the cornerstone of crop productivity, directly influencing yield potential. Photosynthesis remains an underexploited target in soybean breeding, partly because field-based photosynthetic traits are difficult to measure at scale. Also, it is unclear which reproductive stage(s) provide the most informative physiological signals for yield. Few studies have evaluated soybean PS in elite germplasm under field conditions, and the integration of chlorophyll fluorescence (CF) with UAV imaging for PS traits remains largely unexplored. This study evaluated genotypic variation in photosynthetic and canopy traits among elite soybean germplasm across environments and developmental stages using CF and UAV imaging. Linear mixed-model analysis revealed significant genotypic and G×E effects for yield, canopy and several photosynthetic parameters. Broad-sense heritability (H²) estimates indicated dynamic genetic control, ranging from 0.12 to 0.77 at the early stage (S1) and 0.20–0.81 at the mid-reproductive stage (S2). Phi2, SPAD and FvP/FmP exhibited the highest heritability, suggesting their potential as stable selection targets. Correlation analyses showed that while FvP/FmP and SPAD were modestly associated with yield at S1, stronger positive relationships with Phi2, PAR and FvP/FmP emerged during S2, underscoring the importance of sustained photosynthetic efficiency during pod formation. Principal component analysis identified photosynthetic efficiency and leaf structural traits as key axes of physiological variation. UAV-derived indices such as NDRE, MTCI, SARE, MExG and CIRE were significantly correlated with CF-based traits and yield, highlighting their utility as high-throughput proxies for canopy performance. These findings demonstrate the potential of integrating CF and UAV phenotyping to enhance physiological selection and yield improvement in soybean breeding. Full article

To investigate interspecific variation in heat tolerance and underlying adaptation mechanisms in Rhododendron, three-year-old potted seedlings of eight taxa, representing four subgenera within the genus Rhododendron, were subjected to 40 °C high-temperature stress. Heat tolerance was comprehensively assessed using phenotypic observation, chlorophyll fluorescence imaging, microscopic examination, and physiological measurements. Results revealed that leaf damage in Rhododendron oldhamii and Rhododendron × pulchrum reached grade III, whereas Rhododendron latoucheae exhibited only grade II injury with rapid recovery. Chlorophyll fluorescence analysis showed a significant decrease in the maximum quantum efficiency of PSII (F_v/F_m) in R. liliiflorum and R. × pulchrum, followed by rapid recovery, while R. latoucheae maintained stable F_v/F_m values. Stomatal closure occurred in all taxa post-stress; stomatal characteristics of R. liliiflorum and R. simiarum remained stable, and leaf tissue structure was least affected in R. kiangsiense. R. × pulchrum demonstrated the most pronounced structural recovery. Physiologically, R. oldhamii exhibited the greatest increases in electrolyte leakage (EL) and malondialdehyde (MDA) content. R. simiarum accumulated the highest proline content under stress, while R. latoucheae showed the most significant proline reduction during recovery. By integrating multiple indicators through principal component analysis (PCA) and a membership function, and assigning weights based on variance contribution, the heat tolerance was comprehensively evaluated and ranked as follows: R. latoucheae > R. simiarum > R. oldhamii > R. ovatum > R. fortunei > R. liliiflorum > R. kiangsiense > R. × pulchrum. These findings demonstrate significant differences in heat tolerance among Rhododendron taxa at the subgenus level, with the subgenus Azaleastrum generally possessing stronger short-term heat tolerance compared to the subgenus Tsutsusi. This study provides a theoretical basis for heat-tolerant cultivar breeding and landscape application of Rhododendron. Full article

Lithium is a metal of particular interest due to its growing industrial use. However, concerns have been raised about its potential impact on the environment. A notable demand for sustainable technologies to remove Li from environmental matrices and possibly recover it for re-utilization is occurring. Plants can be successfully targeted for this purpose, but further research is needed to expand knowledge. In this regard, laboratory studies under full control of the parameters affecting plant performances are very helpful to obtain insight on the matter. This study investigated the potential of hemp (Cannabis sativa L.) plants to tolerate and accumulate Li in their organs under hydroponic conditions, evaluating morphological, physiological and ionomic parameters. Hemp plants were exposed for 10 days to different LiCl concentrations (0, 50, 150 and 300 mg L⁻¹). The results show the toxicity of the metal at the highest concentration tested (150 and 300 mg L⁻¹ LiCl), causing a reduction in biomass and pigment content (evaluated by spectral reflectance), as well as an uneven impairment of the photosynthetic processes across the leaf lamina (highlighted by the imaging of chlorophyll fluorescence). The ionomic analysis revealed the increase in some micronutrients (Na, Mn, Zn, Mo and Co), which may be involved in the plant’s response to stress conditions at the highest tested Li concentration. Despite accumulating up to 500 mg kg⁻¹ of Li in their aerial organs, hemp plants exposed to 50 mg L⁻¹ LiCl did not exhibit any toxic effects at biometric and physiological levels. These results open up interesting perspectives for the use of this plant species for phytoremediation and metal recovery from biomass, in line with the EU regulations requiring environmentally sustainable practices. Full article

Plants exposed to combined abiotic and biotic stresses often exhibit complex physiological responses that cannot be predicted from single stress factors. In this study, we evaluated the interactive effects of temperature stress and Erwinia amylovora infection on pear (Pyrus pyrifolia) leaves under five temperature conditions (10, 15, 25, 30, and 35 °C) with or without pathogen inoculation, using chlorophyll fluorescence analysis and RGB imaging over a 7-day period. Photosynthetic performance remained optimal at 25 °C under single temperature conditions, whereas pathogen inoculation alone caused PSII damage and reduced energy dissipation. Under combined stress, PSII responses exhibited temperature-dependent patterns: at 10, 15 °C, photoprotective mechanisms were partially maintained; at 25, 30 °C, severe structural and functional damage occurred; and at 35 °C, pathogen activity was suppressed while partial recovery of PSII was observed. By integrating chlorophyll fluorescence analysis with a linear mixed-effect model (LMM), distinct patterns of sensitivity were identified among fluorescence parameters, with ΦNO responding to single stress factors, and Fm, Fv, Fp, Fv/Fo, and qL showing significant three-way interactions. These findings highlight temperature-dependent strategies of pear leaves to cope with fire blight and emphasize the utility of chlorophyll fluorescence analysis for evaluating photosynthetic resilience. From an applied perspective, chlorophyll fluorescence could serve as a rapid, non-destructive tool for screening pear cultivars with enhanced tolerance to bacterial fire blight, contributing to more efficient orchard management strategies. Full article

Background/Objectives: Global maize production is considerably affected by drought aggravated by climate change. No genome-wide association study (GWAS) or candidate gene analysis has been performed using chlorophyll fluorescence (ChlF) and hyperspectral (HS) indices measured in young plants challenged by a water deficit. Our objective was to conduct a GWAS of nine ChlF and HS indices measured in a diversity panel of drought-stressed young plants grown in a controlled environment using a maize single nucleotide polymorphism (SNP) 50k chip. Methods: A total of 165 inbred lines were genotyped using the Infinium Maize50K SNP array and association mapping was carried out using a mixed linear model. Results: The GWAS detected 37 respective SNP markers significantly associated with the maximum quantum yield of the primary photochemistry of a dark-adapted leaf (Phi_Po), the probability that a trapped exciton moves an electron into the electron transport chain further than QA (Psi_o), the normalized difference vegetation index (NDVI), the Zarco–Tejada and Miller Index (ZMI), greenness, modified chlorophyll absorption in reflectance (MCARI), modified chlorophyll absorption in reflectance 1 (MCARI1), and Gitelson and Merzlyak indices 1 and 2 (GM1 and GM2). Conclusions: Our results contribute to a better understanding of the genetic dissection of the ChlF and HS indices, which is directly or indirectly related to physiological processes in maize, supporting the use of HS imaging in the context of maize breeding. Full article

Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact. Full article

Among different formations, inorganic/inorganic assemblies can be considered “two in one” systems offering collective and/or new physical-chemical properties and substantial activity. Herein, a post-synthetic approach involving the assembly through Van der Waals forces and/or hydrogen bonding of the preformed ZnO@OAm NPs and Ca(OH)₂@OAm NPs of non-uniform sizes (9 nm and 27 nm, respectively), albeit coated with the same surfactant (oleylamine-OAm), is reported. The resulting semiconductor hetero-nanostructure (named CaZnO) has been physicochemically characterized. The X-ray diffraction (XRD) peaks correspond to both ZnO and Ca(OH)₂, confirming the successful formation of a dual-phase system. Field emission scanning electron microscopy coupled with energy-dispersive spectroscopy (FESEM-EDS) of CaZnO indicated the formation of Ca(OH)₂ NPs decorated with irregular-shaped ZnO NPs. The synthesized hetero-nanostructure was evaluated by assessing any negative effects on the photosynthetic function of tomato plants as well as for the generation of reactive oxygen species (ROS). The impact of the CaZnO hetero-nanostructure on photosystem II (PSII) photochemistry was evaluated under both the growth light intensity (GLI) and a high light intensity (HLI) at a short (90 min) and long (96 h) duration exposure. An enhancement of photosystem II (PSII) function of tomato plants by 15 mg L⁻¹ CaZnO hetero-nanostructure right after 90 min was evidenced, indicating its potential to be used as a photosynthetic biostimulant, improving photosynthetic efficiency and crop yield, but pending further testing across various plant species and cultivation conditions. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article