Search Results (452)

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. 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

Sheath blight (ShB), caused by the necrotrophic fungus Rhizoctonia solani (R. solani), poses severe threats to global rice production. Developing a resistant variety with an ShB-resistance gene is one of most efficient and economical approaches to control the disease. Here, we identified a highly conserved chloroplast-localized stem-loop-binding protein encoding gene (OsCSP41b), which shows great potential in developing an ShB-resistant variety. OsCSP41b-knockout mutants exhibit chlorotic leaves and increased ShB susceptibility, whereas OsCSP41b-overexpressing lines (CSP41b-OE) display significantly enhanced resistance to R. solani, as well as to drought, and salinity stresses. Notably, CSP41b-OE lines present a completely comparable grain yield to the wild type (WT). Transcriptomic analyses reveal that chloroplast transcripts and photosynthesis-associated genes maintain observably elevated stability in CSP41b-OE plants versus WT plants following R. solani infection, which probably accounts for the enhanced ShB resistance of CSP41b-OE. Our findings nominate the OsCSP41b gene as a promising molecular target for developing a rice variety with stronger resistance to both R. solani and multi-abiotic stresses. Full article

Straw returning inhibits tillering at the early stage of rice growth and thus affects grain yield. Sulfur-coated urea (SCU) has been expected to increase nitrogen use efficiency (NUE) and yield, save labor input, and reduce environmental pollution in crop production. Nevertheless, the sulfur coatings of SCU are easy to break and then shorten the nutrient release cycle. Whether there was a complementary effect between straw returning and SCU in NUE and grain yield had remained elusive. To investigate the effects of straw returning combined with the application of SCU on NUE and rice yield, a two-year field experiment was conducted from 2022 to 2023 with three treatments (straw returning combined with conventional urea (SRU), no straw returning combined with SCU (NRS), straw returning combined with SCU (SRS)). We found that straw returning combined with the application of SCU increased rice yield and NUE significantly. Compared with SRU and NRS, SRS treatments significantly increased grain yield by 14.61–16.22%, and 4.14–7.35%, respectively. Higher effective panicle numbers per m² and grain numbers per panicle were recorded in NRS and SRS treatments than SRU. SRS treatment increased nitrogen recovery efficiency by 79.53% and 22.97%, nitrogen agronomic efficiency by 18.68% and 17.37%, and nitrogen partial factor productivity by 10.51% and 9.81% compared with SRU and NRS treatment, respectively. The enhanced NUE in SRS was driven by higher leaf area index, SPAD value, net photosynthetic rate, carbon metabolic enzyme (RuBP and SPS) activity, nitrogen metabolic enzyme (NR, GS, and GOGAT) activity, sucrose and nitrogen content in leaves, and nitrogen accumulation in plant during grain filling. Moreover, the improved yield in SRS was closely related to superior NUE. In conclusion, straw returning combined with application of SCU boosted grain yield and NUE via enhanced carbon–nitrogen metabolism during the late growth period in rice. Full article

Plastid division regulatory genes play a crucial role in the morphogenesis of chloroplasts and amyloplasts. Chloroplasts are the main sites for photosynthesis and metabolic reactions, while amyloplasts are the organelles responsible for forming and storing starch granules. The proper division of chloroplasts and amyloplasts is essential for plant growth and yield maintenance. Therefore, this study aimed to examine the J175 (FtsZ2-2) gene, cloned from an ethyl methanesulphonate (EMS) mutant involved in chloroplast and amyloplast division in maize, through map-based cloning. We found that J175 encodes a cell division protein, FtsZ (filamentous temperature-sensitive Z). The FtsZ family of proteins is widely distributed in plants and may be related to the division of chloroplasts and amyloplasts. The J175 protein is localized in plastids, and its gene is expressed across various tissues. From the seedling stage, the leaves of the j175 mutant exhibited white stripes, while the division of chloroplasts was inhibited, leading to a significant increase in volume and a reduction in their number. Measurement of the photosynthetic rate showed a significant decrease in the photosynthetic efficiency of j175. Additionally, the division of amyloplasts in j175 grains at different stages was impeded, resulting in irregular polygonal starch granules. RNA-seq analyses of leaves and kernels also showed that multiple genes affecting plastid division, such as FtsZ1, ARC3, ARC6, PDV1-1, PDV2, and MinE1, were significantly downregulated. This study demonstrates that the maize gene j175 is essential for maintaining the division of chloroplasts and amyloplasts and ensuring normal plant growth, and provides an important gene resource for the molecular breeding of maize. Full article

Nitrogen (N) critically regulates wheat growth and grain quality, yet the molecular mechanisms underlying foliar nitrogen application remain unclear. This study evaluated the effects of foliar nitrogen application (12.25 kg ha⁻¹) on the growth, grain yield, and quality of spring wheat, as well as its molecular mechanisms. The results indicated that N was absorbed within 3 h post-application, with leaf nitrogen concentration peaking at 12 h. The N treatment increased whole-plant dry matter accumulation and grain protein content by 11.34% and 6.8%, respectively. Amino acid content peaked 24 h post-application, increasing by 25.3% compared to the control. RNA-sequencing analysis identified 4559 and 3455 differentially expressed genes at 3 h and 24 h after urea treatment, respectively, these DEGs being primarily involved in nitrogen metabolism, photosynthetic carbon fixation, amino acid biosynthesis, antioxidant systems, and nucleotide biosynthesis. Notably, the plastidic glutamine synthetase gene (GS2) is crucial in the initial phase of urea application (3 h post-treatment). The pronounced downregulation of GS2 initiates a reconfiguration of nitrogen assimilation pathways. This downregulation impedes glutamine synthesis, resulting in a transient accumulation of free ammonia. In response to ammonia toxicity, the leaves promptly activate the GDH (glutamate dehydrogenase) pathway to facilitate the temporary translocation of ammonium. This compensatory mechanism suggests that GS2 downregulation may be a key switch that redirects nitrogen metabolism from the GS/GOGAT cycle to the GDH bypass. Additionally, the upregulation of the purine and pyrimidine metabolic routes channels nitrogen resources towards nucleic acid synthesis, and thereby supporting growth. Amino acids are then transported to the seeds, culminating in enhanced seed protein content. This research elucidates the molecular mechanisms underlying the foliar response to urea application, offering significant insights for further investigation. Full article

Rice is a staple food for over half of the world’s population, and it is grown in over 100 countries. Rice blast disease can cause 10% to 30% crop loss, enough to feed 60 million people. Breeding for resistance can help farmers avoid costly fungicides. This study assessed the relationship between rice blast disease and zinc or anthocyanin content in biofortified rice. Susceptibility to foliar and panicle blast was assessed in a rice panel which differed on grain zinc content and pigmentation. A rice panel (n = 23) was challenged with inoculum of two isolates of Magnaporthe oryzae in a screenhouse-based assay. The zinc content with foliar blast severity was analyzed in the leaves and grain of a subset of non-inoculated rice plants. The effect of foliar zinc supplementation on seedlings was assessed by varying levels of zinc fertilizer solution on four blast susceptible cultivars at 14 days after planting (DAP), followed by inoculation with the blast pathogen at 21 DAP. Foliar blast severity was scored on a 0–9 scale at 7 days after inoculation. The rice panel was scored for anthocyanin content, and the data were correlated with foliar blast severity. The panel was grown in the field, and panicle blast, grain yield and yield-related agronomic traits were measured. Significant differences were observed in foliar blast severity among the rice genotypes, with IRBLK-KA and IR96248-16-2-3-3-B having mean scores greater than 4, as well as BASMATI 370 (a popular aromatic variety), while the rest of the genotypes were resistant. Supplementation with foliar zinc led to a significant decrease in susceptibility. A positive correlation was observed between foliar and panicle blast. The Zn in the leaves was negatively correlated with foliar blast severity, and had a marginally positive correlation with panicle blast. There was no relationship between foliar blast severity and anthocyanin content. Grain yield had a negative correlation with panicle blast, but no correlation was observed between Zn in the grain and grain yield. This study shows that Zn biofortification in the grain may not enhance resistance to foliar and panicle blast. Furthermore, the zinc-biofortified genotypes were not agronomically superior to the contemporary rice varieties. There is a need to apply genomic selection to combine promising alleles into adapted rice genetic backgrounds. Full article

Growing cover crop mixtures is a sustainable agriculture tool that helps to reduce fertilizer use and, at the same time, ensures lower environmental pollution. The aim of this research is to assess the biomass of the aboveground part of cover crop mixtures and the nutrients accumulated in it and to determine their influence on the soil properties and productivity of spring oats (Avena sativa L.). The biomass of the aboveground part of cover crop mixtures of different botanical compositions varied from 2.33 to 2.67 Mg ha⁻¹. As the diversity of plant species in cover crop mixtures increased, the accumulation of nutrients in the aboveground part biomass increased, and the risk of nutrient leaching was reduced. The post-harvest cover crop mixture TGS GYVA 365, consisting of eight short-lived and two perennial plant species, significantly reduced the mineral nitrogen content in the soil in spring and had the strongest positive effect on organic carbon content. Post-harvest cover crop mixtures TGS GYVA 365 and TGS D STRUKT 1 did not affect the content of available potassium in the soil but significantly reduced the content of available phosphorus. All tested cover crop mixtures, including the undersown TGS BIOM 1 and the post-harvest mixtures TGS D STRUKT 1 and TGS GYVA 365, reduced soil shear strength and improved soil structure, although the reduction was not statistically significant for TGS D STRUKT 1. Cover crop mixtures left on the soil surface as mulch had a positive effect on the chlorophyll concentration in oat leaves, number of grains per panicle, and oat grain yield. A significant positive correlation was found between oat grain yield and several yield components, including crop density, plant height, number of grains per panicle, and grain mass per panicle. These findings highlight the potential of diverse cover crop mixtures to reduce fertilizer dependency and improve oat productivity under temperate climate conditions. Full article

Unregulated irrigation with partially industrial effluents may lead to heavy metal contamination in crops and pose significant human health risks, especially in developing countries like India. Therefore, the present study aimed to quantify six heavy metals (Cd, Cr, Cu, Fe, Mn, and Zn) in soil and wheat irrigated with paper mill effluent, assess plant responses, and evaluate associated health risks for consumers. For this, a field study was conducted across ten sites (five effluent-irrigated, five borewell-irrigated as control), analyzing soil and wheat tissues for metal concentrations and calculating risk indices including bioaccumulation factor (Bf), translocation factor (Tf), Dietary Intake of Metals (DIM < 1), Health Risk Index (HRI < 1), and Target Hazard Quotient (THQ < 1). Results indicated high concentrations of Cd and Cr in effluent-irrigated soils and wheat tissues (root > stem > leaves > grains) compared to control sites, with some values exceeding permissible limits. Although the THQ values for heavy metals were below 1, indicating a low immediate health risk, concentrations of Cd and Cr in both soil and crop tissues exceeded acceptable safety standards. This study provides empirical evidence supporting the need for effluent treatment and policy interventions to mitigate agricultural contamination from the use of industrial effluents and protect public health. Full article

As the global food crisis worsens, enhancing crop yields on saline–alkali soils has become a critical measure for ensuring global food security. Wheat (Triticum aestivum L.), one of the world’s most important staple crops, is particularly sensitive to phosphorus availability, making appropriate phosphorus fertilization a key and manageable strategy to optimize yield. Although many studies have explored phosphorus fertilization strategies, most have focused on non-saline soils or generalized conditions, leaving a critical gap in understanding how phosphorus application affects wheat yield, soil nutrient dynamics, and nutrient uptake efficiency under saline–alkali stress. Therefore, further investigation is required to establish phosphorus management practices specifically adapted to saline–alkali environments for sustainable wheat production. To address this gap, the experiment was designed with varying phosphorus fertilizer application rates based on P₂O₅ content (0, 60 kg/hm², 120 kg/hm², 180 kg/hm², and 240 kg/hm²), considering only the externally applied phosphorus without accounting for the inherent phosphorus content of the soil. The results indicated that as the phosphorus application rate increased, the wheat yield first increased and then decreased. The highest yield (6355 kg·hm⁻²) was achieved when the phosphorus application rate reached 120 kg/hm², with an increase of 47.2–63.5% compared to the control (no fertilizer). Similarly, biomass, thousand-grain weight, and the absorption of nitrogen, phosphorus, and potassium in both straw and grains exhibited the same increasing-then-decreasing trend. Mechanistic analysis revealed that phosphorus fertilization enhanced soil alkali–hydrolyzable nitrogen, available phosphorus, and available potassium, thereby promoting nutrient uptake and ultimately improving grain yield. The innovations of this study lie in its focus on phosphorus management specifically under saline–alkali soil conditions, its integration of soil nutrient changes and plant physiological responses, and its identification of the optimal phosphorus application threshold for balancing yield improvement and nutrient efficiency. These findings provide a scientific basis for refining phosphorus fertilization strategies to sustainably boost wheat productivity in saline–alkali environments. Full article

Cadmium (Cd) pollution in rice and selenium (Se) deficiency in humans have attracted widespread attention. In this study, we investigated the effects of the combined application of biological nanoselenium (B-SeNPs) foliar spray and biochar (BC) on Se enrichment and Cd content reduction in rice. A pot experiment was established by designing four levels each of BC and B-SeNPs to be applied to rice plants. The results revealed that soil Cd bioavailability decreased by 3.26–16.67%, while soil Se bioavailability increased by 0.76–7.63% in the combined BC and B-SeNPs treatments, with rice photosynthesis showing significant enhancement during each growth period. Both BC and B-SeNPs treatments significantly enhanced the levels of antioxidant components (glutathione, phytochelatins, catalase, peroxidase, and superoxide dismutase) while reducing oxidative stress markers (malondialdehyde and superoxide anion radical) in rice leaves. Additionally, these treatments effectively modulated the subcellular distribution of Se and Cd, demonstrating their potential in alleviating Cd toxicity and enhancing Se homeostasis. These changes were accompanied by a marked reduction in lipid peroxidation (indicated by malondialdehyde) and superoxide radical accumulation, suggesting that BC and B-SeNPs treatments strengthened the antioxidative defense system in rice leaves. Additionally, compared with the BC0Se0 treatment, the combined application of BC and B-SeNPs significantly enhanced grain Se content by 7.14–221.43% while significantly reducing Cd content by 30.77–76.92%. The efficacy of grain Se enrichment and Cd reduction followed the sequence B-SeNPs + BC > Se only > BC only, where the BC5Se20 treatment demonstrated the most pronounced effects on both Se accumulation and Cd decrease in grains. Therefore, the combined application of foliar-applied B-SeNPs and biochar not only reduces Cd bioavailability in soil but also effectively suppresses Cd uptake by rice while simultaneously enhancing Se enrichment. Full article

Coal gangue is a fine-grained mineral with nutrient content, which can be used as a potential soil amendment. Nevertheless, current research on using coal gangue to improve soil water and support plant growth is still insufficient. In this study, coal gangue powder (CGP) was added to aeolian sandy soil. We compared the soil hydraulic properties and plant growth of original aeolian sandy soil (CK) and different CGP application rates (10% and 20%). The results indicated that the application of CGP transformed the soil texture from sandy to loamy, significantly reduced soil bulk density and saturated hydraulic conductivity (Ks) values, altered the soil water characteristic curve, enhanced soil water-holding capacity, and increased plant-available water. Compared with the CK group, the emergence rate of alfalfa seeds increased from approximately 50% to over 70% after CGP application. During the growth process, CGP application significantly elevated the net photosynthetic rate, transpiration rate, and stomatal conductance of alfalfa leaves. Rapid fluorescence kinetics monitoring of leaves demonstrated that alfalfa treated with CGP had a higher efficiency in light energy utilization. However, the photosynthetic capacity of leaves did not improve as the CGP application rate increased from 10% to 20%, suggesting that excessive CGP addition did not continuously benefit plant gas exchange. In conclusion, CGP application can improve the soil hydraulic properties of aeolian sandy soil and support plant growth and development, which is conducive to reducing the accumulated amount of coal gangue, alleviating plant water stress, and promoting ecological restoration in arid mining areas. We recommend a 10% addition of coal gangue powder as the optimal amount for similar soils. Full article

Breeding and cultivating low-N-efficient maize varieties to obtain high yields with less N fertilizer is important for addressing food demands and environmental pollution. However, few studies have investigated the physiological characteristics of low-N-efficient maize varieties. Therefore, we performed an experiment over four years to test two maize varieties (low-N-efficient variety: JNK728, and high-N-efficient variety: XY335) and five N application rates (N120: 120 kg·ha⁻¹, N180: 180 kg·ha⁻¹, N240: 240 kg·ha⁻¹, N300: 300 kg·ha⁻¹, and N360: 360 kg·ha⁻¹). The optimal N application rates for JNK728 and XY335 were N180 and N300, which obtained the highest yields (11,754 and 12,752 kg·ha⁻¹, respectively), N uptake efficiencies (1.32 and 0.93 kg·kg⁻¹), and N harvest index (67.94% and 61.98%), compared with other N application rates. The key period for plant N accumulation was the R1–R6 stage, which contributed 35.2–49.7% and 40.8–53.8% to plant N accumulation at the maturation stage in JNK728 and XY335, respectively. In addition, N accumulation in the grain accounted for more than half (51.8–63.2%) of the total N accumulation in plants, and the leaf N transport amount after the post-silking stage was the primary source of grain N accumulation in both JNK728 and XY335. We also explored the key enzymes and genes related to the N transport amount and efficiency in leaves in the two maize varieties, and found that GOGAT was the key enzyme and GOGAT2 was the key gene for JNK728, whereas the AS enzyme and AS1 and AS3 genes were most important for XY335. Therefore, we suggest that molecular breeding programs should focus on the GOGAT2 gene in low-N-efficient maize varieties, and cultivation techniques should aim to improve the GOGAT enzyme activity after the post-silking period to achieve high yields and N utilization efficiencies with less N fertilizer. Full article

Under the principles of the circular economy and sustainability, consumers, the food industry and health authorities have interest in new natural food preservatives to prevent foodborne diseases and increase produce shelf life. This work aimed to evaluate the antimicrobial properties of cowpea plant extracts. Grain, pod and leaf extracts from five Portuguese cowpea accessions were characterized in terms of their phenolic content. The values of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined against pathogenic and non-pathogenic bacteria, as well as on post-harvest pathogenic filamentous fungi. In general, the phenolic content of pod extracts was higher than that of grains and leaves, although leaf extracts had the highest content of a broad-spectrum antibacterial flavonoid, quercetin. Grain extracts exhibited no detectable antimicrobial activity. In contrast, leaf and pod extracts from all five accessions generally displayed bactericidal effects. For bacteria, pod extracts showed MICs ranging from 5.1 to 87.7 mg/mL and MBCs from 20.3 to 87.7 mg/mL. Leaf extracts showed the most promising results, with MICs and MBCs ranging from 1.1 to 9.1 mg/mL. The results against fungi were not so expressive; nevertheless, P. expansum was inhibited by 9 L leaf extract even if at a higher concentration (MIC = 18 mg/mL) than those obtained for bacteria. The Portuguese variety Fradel (1E) showed very promising antibacterial activity, with leaf extracts showing low MBC values (from 2.3 to 9.1 mg/mL). The obtained results indicate that cowpea pods and leaves have antimicrobial properties and could potentially be used as a source of compounds for food preservation. Full article

Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R² of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton. Full article