Search Results (188)

Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha⁻¹ and 71 kg P ha⁻¹, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation. Full article

Fungal diseases such as soybean stem canker (SSC), frogeye leaf spot (FLS), and sudden death syndrome (SDS) cause substantial yield losses in soybean worldwide. This study aimed to pyramid major resistance genes and QTLs against these diseases through marker-assisted backcrossing (MABC). Diagnostic SSR markers, linked to Rdm4 (SSC), Rcs3 (FLS), and SDS resistance QTLs, were validated and successfully employed for foreground and background selection in crosses between the elite cultivar A8100RR and the resistant donor ‘Forrest’. Molecular analyses confirmed the effective introgression and fixation of multiple resistance loci in BC₂F₅ lines. Under artificial inoculation, lines R30-11 and R25-13 displayed high resistance levels to Diaporthe aspalathi, Cercospora sojina, Fusarium virguliforme, and F. tucumaniae. Genotype R30-11 exhibited the most consistent resistance across pathogens, while R25-13 combined multi-disease resistance with glyphosate tolerance and stable agronomic performance under field conditions comparable to commercial cultivars. These results represent, to our knowledge, the first report of successful pyramiding genes and QTLs against three distinct fungal diseases (SSC, FLS, and SDS) in soybean through MABC. The developed lines constitute valuable germplasm for breeding programs designed to achieve broad-spectrum, durable and sustainable disease management. Full article

Low-temperature stress during the jointing stage severely disrupts the coordination of the source–flow–sink system in winter wheat. To elucidate the underlying mechanism, three wheat cultivars with different winter habits (Zhenmai 12, Jimai 22, and Shannong 38) were selected and subjected to six temperature levels (−6 °C to 8 °C) and three stress durations (2–6 days). The effects of vascular bundle traits on the transport of photosynthetic products, dry matter distribution, and yield formation were analyzed. The results showed that Zhenmai 12 and Jimai 22 completely ceased photosynthesis under 0 °C and −3 °C, respectively. The leaf vascular bundle area continuously decreased with increasing low-temperature stress, while the proportion of xylem and phloem initially increased by approximately 15% and 10%, respectively, before rapidly decreasing to 65% of the control value. In the stem, the three vascular bundle parameters initially increased by 20%, 25%, and 20%, respectively, before quickly decreasing to 50%. Changes in the vascular bundle structure weakened the transport capacity of assimilates, with dry matter in leaves and stems decreasing by 15–20% and 10%, respectively, while the root dry matter increased by 20–30%. Correlation analysis revealed highly significant relationships (p < 0.001) between vascular bundle parameters and yield components. Principal component and cluster analyses indicate that the area of leaf and stem vascular bundles, maximum net photosynthetic rate, and water use efficiency may be key indicators in explaining the variation in yield. Radar plots further validated this finding, showing that Zhenmai 12 and Jimai 22 are more sensitive to changes in the maximum net photosynthetic rate, while Shannong 38 exhibits a greater sensitivity to changes in water use efficiency. Based on existing research on photosynthetic pathways and dry matter distribution, this study innovatively investigates the potential relationship between material transport and yield formation under low-temperature stress during the jointing stage from the perspective of anatomical structure and functional coupling. The findings provide new insights into understanding the structural impact of low-temperature stress on crop yield formation and offer theoretical support for identifying the structural basis of limited material transport under stress and for developing disaster diagnostic models driven by structural parameters. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (${\mathcal{L}}_{align}$), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

Idesia polycarpa Maxim. is an important woody oilseed species and is dioecious; however, systematic evidence for sex-specific differences in leaf physico-chemical traits and their spectral responses remains limited. In this study, mature female and male trees were investigated. Leaves were sampled throughout the growing season (May–October), and FTIR-ATR spectra were acquired to derive peak height and peak area metrics for diagnostic bands. In parallel, leaf antioxidant enzyme activities (SOD, CAT, POD, and APX), biomass-related traits, leaf nutrient concentrations, and rhizosphere soil nutrient indices were measured. Differences between sexes, seasonal dynamics, and spectrum–trait coupling were evaluated using repeated-measures analysis and correlation analyses. The results showed that the positions of major absorption bands were largely consistent between sexes, indicating broadly similar chemical composition, whereas the male plants lacked an obvious band near 1671 cm⁻¹ in May. Several spectral peak parameters were significantly correlated with leaf pH, leaf dry matter content, total phosphorus, and APX activity. Female and male plants exhibited month-dependent differences in enzyme activities, dry matter content, and leaf N and K, and leaf–soil nutrient linkages were also detected, suggesting sex-specific resource allocation patterns. Overall, FTIR-ATR peak metrics provide a rapid means to characterize seasonal variation in leaf physico-chemical properties of I. polycarpa and offer supporting evidence for studies of sexual dimorphism. Full article

Accurately quantifying the inequality of plant organ size distributions, such as leaf area, is essential for understanding plant resource allocation strategies, and this is commonly achieved using Lorenz curves. Previous studies have shown that the performance equation (PE) and its generalized form (GPE) effectively describe Lorenz curves that are rotated 135° counterclockwise around the origin and shifted rightward by $\sqrt{2}$ units. However, few studies have compared the fitting performance of PE (and GPE) with other traditional equations generating Lorenz curves in modeling empirical leaf area distributions, and even fewer have considered the validity of linear approximation assumptions in these nonlinear models. To address this gap, we quantified the inequality of leaf area distributions in Semiarundinaria densiflora, a bamboo species for which the abundant and measurable leaves per culm provide an ideal system for examining the ecological strategies underlying leaf allocation patterns. Five nonlinear models were employed to fit the leaf area distribution: PE, GPE, the Sarabia equation (SarabiaE), the Sarabia–Castillo–Slottje equation (SCSE), and the Sitthiyot–Holasut equation (SHE). Model performance was assessed using root-mean-square error (RMSE) and Akaike information criterion (AIC), while nonlinearity curvature measures were applied to evaluate the close-to-linear behavior of parameter estimates. In addition, the Lorenz asymmetry coefficient (LAC) was used to quantify the asymmetry of the Lorenz curves. Our results showed a clear trade-off between predictive accuracy and linear approximation behavior. Among the five models, GPE achieved the best fit, with the lowest RMSE and AIC values, yet did not show good close-to-linear behavior. In contrast, SHE provided the poorest fit but demonstrated the strongest close-to-linear properties. LAC values indicated that relatively abundant, larger leaves disproportionately contributed to the inequality in leaf area distribution. These findings highlight an inherent trade-off in using Lorenz-based models to describe leaf area frequency distributions: predictive accuracy does not necessarily align with statistical validity. By integrating model fit, nonlinearity diagnostics, and asymmetry assessment, this study provides new perspectives and methodological tools for future investigations into inequality in plant organ size distributions and their ecological significance. Full article

Background/Objectives: Tangent screen perimetry is a valuable tool for detecting functional visual loss (FVL), which is suspected when the visual field fails to expand as expected with distance. However, there is currently a lack of research documenting the specific tangent screen patterns produced by patients with organic visual loss (OVL), defined as visual field loss caused by identifiable structural or neurologic pathology. This study aims to characterize the visual field patterns observed in patients with organic and functional pathologies during tangent screen perimetry and evaluate its diagnostic efficacy in confirming FVL. Methods: Medical records of patients from Seoul National University Bundang Hospital between August 2009 and August 2019 were reviewed. All subjects underwent a comprehensive neuro-ophthalmologic examination with additional testing to confirm the diagnosis of OVL or FVL. A total of 126 eyes from 76 patients exhibiting visual field constriction within 30 degrees were included. The tangent ratio (TR) was defined as the average visual field (in radians) at a far distance (e.g., 2 m) divided by the average visual field at a near distance (e.g., 1 m). The visual field patterns and TR were analyzed, and the diagnostic value of TR in detecting FVL was determined. Results: The clover leaf pattern and reversal pattern were observed in 8.8% and 12.7% of FVL cases, respectively, whereas no such patterns were found in OVL cases (p = 0.002, p < 0.001). The TR varied from 0.50 to 1.06 (mean 0.77 ± 0.16) in OVL and from 0.33 to 1.03 (mean 0.65 ± 0.15) in FVL (p < 0.001). Younger age, a clover leaf pattern or reversal pattern on tangent screen perimetry, and a lower TR were significantly associated with FVL. Conclusions: Tangent screen perimetry is an effective adjunct for differentiating functional from organic visual field loss, particularly in cases of visual field constriction. Full article

Phenology can confound rice nitrogen diagnosis based on SPAD readings because leaf greenness and nitrogen concentration change nonlinearly with development. We tested whether physiological time, expressed as growing degree days (GDD), can reduce this developmental bias and improve the portability of SPAD-based diagnosis. We analyzed 1141 observations from 20 independent field experiments across five sites, spanning japonica, indica, and hybrid cultivars and nitrogen fertilizer treatments (0–300 kg N ha⁻¹). SPAD was measured on up to five leaf-from-top positions (LFT1–LFT5) and used to predict leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), and nitrogen nutrition index (NNI). Across group-wise cross-validation by experiment, adding GDD to SPAD consistently improved cross-environment accuracy (mean R² up to 0.75 for LNC and 0.79 for PNC) and markedly weakened residual trends along GDD. Multiplicative SPAD×GDD degraded performance, while explicit interaction terms provided little gain over a simple additive SPAD + GDD form. Interpretable analyses further showed that diagnostic information is concentrated in mid-canopy leaves and shifts with physiological time. Combining GDD with a two-leaf SPAD protocol retained most accuracy for concentration targets, supporting a time-aligned and field-practical approach for robust nitrogen diagnosis. Full article

Tomato leaf diseases exhibit subtle inter-class differences and substantial intra-class variability, making accurate identification challenging for conventional deep learning models, especially under real-world conditions with diverse lighting, occlusion, and growth stages. Moreover, most existing approaches rely solely on visual features and lack the ability to incorporate semantic descriptions or expert knowledge, limiting their robustness and interpretability. To address these issues, we propose LPDiag, a multimodal prototype-attention diagnostic framework that integrates large language models (LLMs) for fine-grained recognition of tomato diseases. The framework first employs an LLM-driven semantic understanding module to encode symptom-aware textual embeddings from disease descriptions. These embeddings are then aligned with multi-scale visual features extracted by an enhanced Res2Net backbone, enabling cross-modal representation learning. A set of learnable prototype vectors, combined with a knowledge-enhanced attention mechanism, further strengthens the interaction between visual patterns and LLM prior knowledge, resulting in more discriminative and interpretable representations. Additionally, we develop an interactive diagnostic system that supports natural-language querying and image-based identification, facilitating practical deployment in heterogeneous agricultural environments. Extensive experiments on three widely used datasets demonstrate that LPDiag achieves a mean accuracy of 98.83%, outperforming state-of-the-art models while offering improved explanatory capability. The proposed framework offers a promising direction for integrating LLM-based semantic reasoning with visual perception to enhance intelligent and trustworthy plant disease diagnostics. Full article

Aerosol dry deposition is an important sink for particulate matter and a source of uncertainty in air quality modeling. Using the Weather Research and Forecasting model coupled with CUACE (WRF-CUACE), we quantified how three aerosol dry deposition schemes and satellite-based leaf area index (LAI) information affected PM_2.5 dry removal and near-surface PM_2.5 over central and eastern China in January 2022. The schemes were abbreviated as Z01, E20, and PZ10, respectively. A fourth simulation (PZ10_MLAI) used PZ10 but replaced the baseline LAI dataset with a Moderate Resolution Imaging Spectroradiometer (MODIS) constrained LAI field. Hourly PM_2.5 was evaluated with the China National Environmental Monitoring Center network. The schemes produced pronounced, size-dependent differences in deposition velocities, with a pronounced spread in the 0 to 2.5 µm average and more than one order of magnitude spread in the accumulation mode diagnostic, leading to distinct regional mean PM_2.5 dry deposition fluxes. The mean PM_2.5 flux increased by 5.9% in E20 relative to Z01 and decreased by 54.4% in PZ10. The MODIS LAI adjustment changed the PZ10 mean flux by 0.42%. The flux contrasts yielded coherent PM_2.5 responses, with E20 reducing near-surface concentrations by about 10 to 30% and PZ10 increasing them by about 20 to 60%, reaching about 80 to 100% in parts of southern China. Domain mean correlations ranged from 0.61 to 0.65 and PZ10-based simulations exhibited near-zero mean bias. Although MODIS LAI effects were modest for this winter month, local PM_2.5 differences commonly remained within about 4% and approached 6 to 10%, indicating that satellite LAI constraints can be important for multi-year and decadal applications. Full article

Lateral flow immunoassays (LFAs) are widely used for on-site testing; however, their use for the rapid detection of plant viruses in the field is often limited by inconvenient sample preparation. Here, we present a new sampling method and a simplified dipstick LFA format for the detection and monitoring of cowpea chlorotic mottle virus (CCMV) as a model plant pathogen. The assay employs a monoclonal mouse antibody for capture and a poly-clonal rabbit antibody conjugated to 80 nm gold nanoparticles for detection. Conventional sample and conjugate pads are omitted, allowing the test strips to be dipped directly into wells containing plant extract and antibody–gold conjugate. No plastic casing was required, which could lead to a reduction in waste. It was shown that CCMV concentrations as low as 3.5 µg/L or 350 pg per sample could be reliably detected in 15 min. Specificity tests confirmed that other plant viruses, cowpea mosaic virus (CPMV) and tobacco mosaic virus (TMV), did not produce false-positive results. In addition, we describe a new method for on-site sampling using a manual punch and a syringe equipped with a frit. This step combines grinding the sample, extraction, filtration, and reconstitution and mixing of the antibody-gold conjugate, enabling the analysis of punched leaf disks without laboratory equipment. When applied to CCMV-infected cowpea plants, the assay revealed systemic infection before visual symptoms became apparent. This work demonstrates that simplified LFAs combined with innovative sampling techniques can provide sensitive, specific, and rapid diagnostics for crop monitoring and support early intervention strategies in agriculture. Full article

Forest cover condition is a key determinant of ecosystem functioning and ecological resilience, yet its spatial variability across large and environmentally heterogeneous regions remains insufficiently understood. Leaf area index (LAI) provides a continuous and physically meaningful indicator of forest canopy condition, reflecting variations in canopy density associated with climate and landscape structure. Here, we develop a spatially explicit and interpretable analytical framework to diagnose the dominant climatic and landscape controls on forest cover condition across mainland China during 2000–2020. By integrating machine-learning modelling with SHapley Additive exPlanations, GeoDetector interaction analysis, and nonlinear dependence diagnostics, we quantify the relative contributions and interactions of precipitation, temperature, topography, and forest landscape structure to spatial patterns in forest LAI. The results reveal pronounced spatial heterogeneity in forest cover control regimes. Precipitation dominates forest cover condition in humid regions but exhibits nonlinear saturation, whereas forest fragmentation strongly constrains canopy development and moderates climate-LAI relationships in arid and semi-arid forested landscapes. In high-elevation regions, topographic and thermal factors exert primary control. Overall, the findings demonstrate that forest cover condition reflects climate-conditioned and landscape-dependent control regimes, providing a transparent basis for large-scale forest cover assessment and ecological monitoring. Full article

Cephalotaxus hainanensis, a valuable medicinal and endangered conifer, requires scientific conservation and precision management to ensure the sustainable utilization of its genetic and ecological resources. Nitrogen (N) is a key nutrient that regulates plant growth and metabolism; rapid and accurate nitrogen diagnosis is vital for optimizing fertilization, reducing nutrient losses, and promoting healthy plant development. This study employed a combined approach integrating stepwise regression, correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify leaf color features strongly correlated with leaf nitrogen content (LNC). A support vector regression (SVR) model, suitable for small-sample datasets, was then employed to accurately estimate LNC across canopy layers. Nine color variables were found to be highly associated with LNC, among which the Green Minus Blue index (GMB) consistently appeared across all correlation methods, demonstrating strong robustness and generality. Color features effectively reflected LNC variations among nitrogen treatments—especially between N1 and N4—and across canopy layers, with the most pronounced contrasts observed between upper and lower leaves. The Spearman-based SVR model revealed that the middle canopy maintained the highest and most stable LNC. However, the lower leaves were most sensitive to nitrogen deficiency, while the upper leaves were more sensitive to nitrogen excess. Comprehensive analysis identified N2 as the optimal nitrogen treatment, representing a balanced nutrient state. Overall, this study confirms the reliability of color features for LNC estimation and highlights the importance of vertical canopy LNC distribution in nitrogen diagnostics, providing a theoretical and methodological foundation for color-based nitrogen diagnosis and precision nutrient management in evergreen conifers. Full article

Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves ($C_b=0.90$) than for monocot leaves ($C_b=0.79$), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article