Search Results (413)

Maize–soybean intercropping is widely practised to improve land use efficiency, but shading from maize often limits soybean growth and productivity. Melatonin, a plant signaling molecule with antioxidant and growth-regulating properties, has shown potential in mitigating various abiotic stresses, including low light. This study investigated the efficacy of applying foliar melatonin (MT) to enhance shade tolerance and yield performance of soybean under intercropping. Four melatonin concentrations (0, 50, 100, and 150 µM) were applied to soybean grown under mono- and intercropping systems. The results showed that intercropping significantly reduced growth, photosynthetic activity, and yield-related traits. However, the MT application, particularly at 100 µM (MT₁₀₀), effectively mitigated these declines. MT₁₀₀ improved plant height (by up to 32%), leaf area (8%), internode length (up to 41%), grain yield (32%), and biomass dry matter (30%) compared to untreated intercropped plants. It also enhanced SPAD chlorophyll values, photosynthetic rate, stomatal conductance, chlorophyll fluorescence parameters such as Photosystem II efficiency (ɸPSII), maximum PSII quantum yield (Fv/Fm), photochemical quenching (qp), electron transport rate (ETR), Rubisco activity, and soluble protein content. These findings suggest that foliar application of melatonin, especially at 100 µM, can improve shade resilience in soybean by enhancing physiological and biochemical performance, offering a practical strategy for optimizing productivity in intercropping systems. Full article

Optimizing irrigation and the targeted use of plant growth regulators are key strategies to improve productivity in citrus systems under water-limited conditions. This study evaluated the effects of three irrigation levels (4.44, 5.18, and 7.77 mm day⁻¹) combined with variable doses of naphthaleneacetic acid (NAA) and gibberellic acid (GA₃) on physiological and productive responses in Citrus aurantiifolia. The treatment with 7.77 mm irrigation and moderate doses of NAA (100 mg L⁻¹) and GA₃ (80 mg L⁻¹) increased yield by 38% (6.2 kg/plant), and it enhanced photosystem II photochemical efficiency (F_v/F_m = 0.82), chlorophyll index (SPAD = 62), and fruit weight by 15%. In contrast, high hormone doses under water deficit reduced leaf water potential and impaired physiological performance, leading to lower productivity. These findings support the combined use of regulated deficit irrigation and hormonal biostimulation as a sustainable strategy to enhance key lime yield and resource efficiency in semi-arid environments. Full article

Accurately estimating leaves’ relative chlorophyll contents (widely represented by Soil and Plant Analysis Development (SPAD) values) across growth stages is crucial for assessing crop health, particularly in regions characterized by varying sowing dates. Unlike previous studies focusing on high-resolution UAV imagery or specific growth stages, this research incorporates satellite-derived texture indices (TIs) into a SPAD value estimation model applicable across multiple growth stages (from tillering to grain-filling). Field experiments were conducted in Jiangsu Province, China, where winter wheat sowing dates varied significantly from field to field. Sentinel-2 imagery was employed to extract vegetation indices (VIs) and TIs. Following a two-step variable selection method, Random Forest (RF)-LassoCV, five machine learning algorithms were applied to develop estimation models. The newly developed model (SVR-RBF_VIs+TIs) exhibited robust estimation performance (R² = 0.8131, RMSE = 3.2333, RRMSE = 0.0710, and RPD = 2.3424) when validated against independent SPAD value datasets collected from fields with varying sowing dates. Moreover, this optimal model also exhibited a notable level of transferability at another location with different sowing times, wheat varieties, and soil types from the modeling area. In addition, this research revealed that despite the lower resolution of satellite imagery compared to UAV imagery, the incorporation of TIs significantly improved estimation accuracies compared to the sole use of VIs typical in previous studies. Full article

Calcareous soils, characterized by high pH and calcium carbonate content, often limit the availability of essential nutrients for crops such as rice (Oryza sativa L.), reducing yield and nutritional quality. In this study, we evaluated the effect of the halotolerant yeast Debaryomyces hansenii on the growth, nutrient uptake, and phosphorus acquisition mechanisms of rice plants cultivated in calcareous soil under controlled greenhouse conditions. Plants inoculated with D. hansenii, particularly via root immersion, exhibited significantly higher SPAD chlorophyll index, plant height, and grain yield compared to controls. A modest increase (~4%) in dry matter content was also observed under sterilized soil conditions. Foliar concentrations of Fe, Zn, and Mn significantly increased in plants inoculated with D. hansenii via root immersion in non-sterilized calcareous soil, indicating improved micronutrient acquisition under these specific conditions. Although leaf phosphorus levels were not significantly increased, D. hansenii stimulated acid phosphatase activity, as visually observed through BCIP staining, and upregulated genes involved in phosphorus acquisition under both P-sufficient and P-deficient conditions. At the molecular level, D. hansenii upregulated the expression of acid phosphatase genes (OsPAP3, OsPAP9) and a phosphate transporter gene (OsPTH1;6), confirming its influence on P-related physiological responses. These findings demonstrate that D. hansenii functions as a plant growth-promoting yeast (PGPY) and may serve as a promising biofertilizer for improving rice productivity and nutrient efficiency in calcareous soils, contributing to sustainable agricultural practices in calcareous soils and other nutrient-limiting environments. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

The postharvest quality of lettuce (Lactuca sativa) is significantly influenced by the lighting environment during storage. This study evaluated the effects of green LEDs at 500 nm and 530 nm, white LEDs (400–700 nm), and dark storage on lettuce quality over 14 days at 5 °C. All treatments were applied at 10 µmol m⁻² s⁻¹ under a 12 h photoperiod. Quality parameters measured included moisture loss, relative water content (RWC), photosynthetic rate, chlorophyll content (SPAD), total soluble solids (TSSs), electrolyte leakage (EL), color change (∆E), texture (crispness), and overall visual quality (OVQ). Lettuce stored under green LEDs, particularly 530 nm, exhibited superior postharvest quality. Compared to dark storage, 530 nm reduced moisture loss by 7.1%, increased RWC by 9.2%, and reduced transpiration rate. The green light preserved photosynthetic activity (43% decline vs. 77% in the dark), increased TSS, reduced color change by 42%, improved crispness by 46.1%, and limited EL to 54.5%. Shelf life was extended by approximately four days. The 500 nm treatment showed notable improvements, including an 8.4% reduction in moisture loss, 8.2% higher RWC, a smaller photosynthesis decline (25%), and the lowest EL (53.1%). It improved color retention (∆E reduced by 45.3%) and crispness (46.8%). Both green wavelengths effectively maintained lettuce quality during cold storage, with 530 nm being the most effective overall. These results suggest that targeted green LED lighting is a promising, energy-efficient strategy to preserve postharvest quality and extend shelf life in leafy greens. Full article

Integrating organic soil amendments such as vermicompost (VC) and vermicompost tea (VCT) in agriculture has received increasing attention as a sustainable strategy to improve soil fertility, enhance plant growth, and suppress pest infestations. This study aimed to evaluate the effects of varying concentrations of VCT (10%, 20%, and 40%), alone and in combination with VC (2.47 ton/ha), on the development and yield of corn (Zea mays), and suppression of fall armyworm (FAW, Spodoptera frugiperda) infestation. The experiment was conducted in seven raised beds with seven treatments: V0 (control), VCT10, VCT20, VCT40, VC1 + VCT10, VC1 + VCT20, and VC1 + VCT40. Six weekly applications of VCT were applied starting at the V2 stage, and soil and plant nutrient contents were determined post-harvest. Additionally, relative chlorophyll content, height, cob yield, dry biomass, and FAW infestations were assessed. Results show that both VC and VCT significantly enhanced soil nutrient content compared to the control treatment (V0). VCT20 and VC1 + VCT10 improved plant N, K, and micronutrient uptake. Corn treated with VCT10 and VC1 + VCT10 had the highest biomass (6.52 and 6.57 tons/ha, respectively), while VCT20 produced the highest cob yield (6.0 tons/ha), which was more than eight times that of V0. SPAD values and corn height were significantly high across all treatments, with VCT20 achieving the highest SPAD readings while the control achieved the lowest. For FAW infestation, the control treatment experienced moderate infestation. At the same time, there was complete suppression in VCT20 and VCT40 treatments and a reduction in VC + VCT treatments, likely due to the bioactive compounds and beneficial microbes in VC and VCT that strengthened plant immunity. The results suggest that VCT20 is a cost-effective, eco-friendly amendment for improving corn performance and FAW resistance. This study contributes to sustainable agriculture by demonstrating how organic amendments can enhance crop resilience while supporting environmentally friendly farming practices. Full article

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

The demand for sustainable agriculture has prompted the exploration of alternative methods to boost crop growth and yield. Microbial biostimulants offer effective solutions to enhance plant performance and reduce reliance on chemical fertilizers. This study investigated the effects of Bacillus pumelo (B. pumilus), applied individually and in combination with a mycorrhizal fungi complex, on the growth, yield, and photosynthetic activity of pea (Pisum sativum). Pea seeds were grown in sterilized soil under four treatment conditions, including a non-inoculated control, inoculation with 2.5 mL of B. pumilus culture per seedling, inoculation with an indigenous mycorrhizal fungal complex, and a combined treatment of B. pumilus and the mycorrhizal complex. The biostimulant treatments significantly influenced all measured photosynthetic and growth parameters. The results showed that B. pumilus substantially promoted pea growth, leading to notable improvements in biomass, plant height, and photosynthetic efficiency. When combined with the mycorrhizal fungi complex, these growth-promoting effects were significantly amplified, resulting in a ~69.7% increase in shoot fresh weight, a ~72.7% rise in root dry weight, and a ~73.6% boost in flower production. Additionally, the chlorophyll content increased by ~180% and photosynthetic yield (Fv/Fm) improved by ~18.5%. The combined treatment also produced the highest SPAD index value, reflecting a ~57% increase. The synergistic interaction between B. pumilus and mycorrhizal fungi enhances photosynthetic efficiency and overall plant performance. The study highlights the potential of using these microbial inoculants as biostimulants to improve pea cultivation in agroecosystems, offering a sustainable alternative to chemical fertilizers. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

The fig (Ficus carica L.) is one of the oldest fruit crops cultivated in arid and semi-arid regions, valued for both its nutritional and economic importance; thus, ensuring sustainable fig production under climate change conditions is very important, as water scarcity increasingly affects fruit quality and production. Selecting and preserving resilient varieties among traditional varieties, representing centuries of local adaptation, is a vital strategy for addressing the challenges driven by climate change. In this context, this study assessed the physiological and biochemical parameters of the leaves of four fig landrace varieties (Fassi, Ghouddane, Nabout, and Ounq Hmam) grown in three different Mediterranean transitional zones of northern Morocco (Chefchaouen, Taounate, and Taza), during a single timepoint assessment conducted in late August 2023. The combined effects of location, variety, and their interactions on chlorophyll fluorescence (F_v/F_m), Soil Plant Analysis Development (SPAD) index, total chlorophyll content (ChlT), canopy temperature depression (CTD), proline content, protein content, total soluble sugar (TSS), hydrogen peroxide (H₂O₂), and malondialdehyde (MDA) were determined. Significant variation was observed among varieties and locations, with the location effect being observed for proline content, protein content, TSS, CTD, and ChlT, while variety had a stronger influence on SPAD, F_v/F_m, H₂O₂, and MDA. The results showed that Nabout and Ounq Hmam varieties had the greatest photosynthetic efficiency, as indicated by their elevated SPAD index, ChlT, and F_v/F_m values, and showed lower sensitivity to oxidative stress (low proline content, H₂O₂, and MDA levels). In contrast, Ghouddane and Fassi displayed better stress tolerance, presenting higher levels of oxidative stress markers. Among locations, Chefchaouen showed the highest protein, TSS, H₂O₂, and MDA levels, reflecting active stress tolerance mechanisms. These variations were confirmed by principal component analysis, which revealed a clear separation between photosynthetically efficient varieties (Nabout and Ounq Hmam) and stress-tolerant varieties (Ghouddane and Fassi). More than a conventional crop physiology study, this work highlights the adaptive strategies in traditional Mediterranean fig germplasm that could be crucial for climate change adaptation. While our findings are limited to a single season, they offer valuable, practical insights that can inform grower decision-making in the near term, especially when considered alongside local knowledge and additional research. Full article

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article

Crop chlorophyll contents affect growth, and accurate assessment aids field management. SPAD (Soil Plant Analysis Development) values of leaves were mainly used to estimate chlorophyll content. Background interference affects the accuracy of SPAD value inversion. To address this issue, a rice leaf SPAD inversion method combining deep learning and feature selection is proposed. First, a leaf segmentation model based on U-Net was established. Then, the color features of leaf images were extracted. Seven color features highly correlated with SPAD were selected via the Pearson correlation coefficient and recursive feature elimination optimization. Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. The results demonstrated that the U-Net could achieve accurate segmentation of leaves with a maximum mean intersection over union (MIoU) of 88.23. The coefficients of determination R² between the anticipated and observed SPAD values of the four models were 0.819, 0.829, 0.896, and 0.721, and the root mean square errors (RMSEs) were 2.223, 2.131, 1.564, and 2.906. Through comparison, the method can accurately predict SPAD in different low-definition and saturation images, showing a certain robustness. It can offer technical support for accurate, nondestructive, and expedited evaluation of crop leaves’ chlorophyll content via machine vision. Full article

Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R² values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management. Full article