Search Results (148)

Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP₅₀ for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation. Full article

Chaperonin 60 proteins plays an important role in plant growth and development as well as the response to abiotic stress. As part of the protein homeostasis system, molecular chaperones have attracted increasing attention in recent years due to their involvement in the folding and assembly of key proteins in photosynthesis. However, little is known about the function of maize chaperonin 60 protein. In the study, a gene encoding the chaperonin 60 proteins was cloned from the maize inbred line B73, and named ZmCpn60-3. The gene was 1, 818 bp in length and encoded a protein consisting of 605 amino acids. Phylogenetic analysis showed that ZmCpn60-3 had high similarity with OsCPN60-1, belonging to the β subunits of the chloroplast chaperonin 60 protein family, and it was predicted to be localized in chloroplasts. The ZmCpn60-3 was highly expressed in the stems and tassels of maize, and could be induced by exogenous plant hormones, mycotoxins, and pathogens; Overexpression of ZmCpn60-3 in Arabidopsis improved the resistance to Pst DC3000 by inducing the hypersensitive response and the expression of SA signaling-related genes, and the H₂O₂ and the SA contents of ZmCpn60-3-overexpressing Arabidopsis infected with Pst DC3000 accumulated significantly when compared to the wild-type controls. Experimental data demonstrate that flg22 treatment significantly upregulated transcriptional levels of the PR1 defense gene in ZmCpn60-3-transfected maize protoplasts. Notably, the enhanced resistance phenotype against Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) in ZmCpn60-3-overexpressing transgenic lines was specifically abolished by pretreatment with ABT, a salicylic acid (SA) biosynthetic inhibitor. Our integrated findings reveal that this chaperonin protein orchestrates plant immune responses through a dual mechanism: triggering a reactive oxygen species (ROS) burst while simultaneously activating SA-mediated signaling cascades, thereby synergistically enhancing host disease resistance. Additionally, yeast two-hybrid assay preliminary data indicated that ZmCpn60-3 might bind to ZmbHLH118 and ZmBURP7, indicating ZmCpn60-3 might be involved in plant abiotic responses. The results provided a reference for comprehensively understanding the resistance mechanism of ZmCpn60-3 in plant responses to abiotic or biotic stress. Full article

Drought severely limits crop yield every year, making it critical to clarify the genetic basis of drought tolerance for breeding of improved varieties. As drought tolerance is a complex quantitative trait, we analyzed three phenotypic groups: (1) agronomic traits under well-watered (WW) and water-deficit (WD) conditions, (2) stress tolerance indices of these traits, and (3) phenotypic plasticity, using a multi-parent doubled haploid (DH) population assessed in multi-environment trials. Genome-wide association studies (GWAS) identified 130, 171, and 71 quantitative trait loci (QTL) for the three groups of phenotypes, respectively. Only one QTL was shared among all trait groups, 25 between stress indices and agronomic traits, while the majority of QTL were specific to their group. Functional annotation of candidate genes revealed distinct pathways of the three phenotypic groups. Candidate genes under WD conditions were enriched for stress response and epigenetic regulation, while under WW conditions for protein synthesis and transport, RNA metabolism, and developmental regulation. Stress tolerance indices were enriched for transport of amino/organic acids, epigenetic regulation, and stress response, whereas plasticity showed enrichment for environmental adaptability. Transcriptome analysis of 26 potential candidate genes showed tissue-specific drought responses in leaves, ears, and tassels. Collectively, these results indicated both shared and independent genetic mechanisms underlying drought tolerance, providing novel insights into the complex phenotypes related to drought tolerance and guiding further strategies for molecular breeding in maize. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), $mA{P}_{50}$, and $mA{P}_{50:95}$ increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops. Full article

Accurately extracting the phenology of maize, one of the three major staple crops, is crucial for assessing regional suitability under climate change, optimizing field management, predicting yield fluctuations, and ensuring food security. This study compares and validates the accuracy of various vegetation indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), solar-induced chlorophyll fluorescence (SIF), and kernel NDVI (kNDVI), in extracting the phenological phases of summer maize at the sixth leaf (V6), tasseling (VT), and maturity (R6). Additionally, explainable machine learning methods were employed to elucidate how climate and stress factors influence the phenological sequences of summer maize. The results show that compared to NDVI and EVI, SIF and kNDVI are more suitable for extracting the summer maize phenological phase. SIF achieved the highest phenological extraction precision at the V6 and R6 phases, with root mean square errors (RMSEs) of 7.86 and 8.22 days, respectively. kNDVI provided the highest extraction accuracy for the VT phase, with an RMSE of 5 days. SHapley Additive exPlanations (SHAP) analysis revealed that temperature and radiation are the primary meteorological factors influencing maize phenology in the study area. Regarding stress factors, drought and heat stress delayed phenology at the V6 and VT phases, while heat stress prior to maturity accelerated summer maize maturation. In conclusion, this study reveals the potential of emerging vegetation indices for extracting maize phenology, offering both data and theoretical support for regional crop adaptability assessments. Full article

Maize is vital for global and Chinese food security. Yet, in Henan Province, a key maize-growing region in China, water scarcity, uneven rainfall, and inefficient irrigation and fertilization limit its yield and quality. This study combines a two-year field experiment (2023–2024) with the DSSAT model to optimize irrigation and fertilization for typical hydrological years (wet, normal, and dry). After calibration and validation with field data, the DSSAT model showed strong performance. Results indicate that optimal irrigation timing and volume vary with hydrological years: no irrigation is needed in wet years, one 30 mm irrigation at the tasseling (VT) stage in normal years, and three irrigations (total 90 mm) at the emergence (VE), jointing (VT), and grain filling (R2) stages in dry years. The optimal nitrogen fertilizer is 240 kg·ha⁻¹ in water-rich and normal years and 180 kg·ha⁻¹ in dry years. These optimized schemes can achieve 98–100% of maximum potential maize yields across hydrological years, offering practical insights for enhancing agricultural water and nutrient management in central Henan to support sustainable development and reduce environmental impacts. Full article

This research investigates the influence of water–nitrogen coupling on soil water content, nitrogen dynamics, and root distribution in farmland, along with the interactions among soil water, nitrogen transport, root distribution, and crop yield. A field experiment was conducted under moderate drought stress (50–60% of field capacity) and three nitrogen application rates (100, 200, and 300 kg·ha⁻¹, split-applied at 50% during sowing and 50% at the jointing stage, labeled as N₁, N₂, and N₃) at the two critical growth stages (jointing stage P₁ and tasseling-silking stage P₂) of maize (Denghai 605). The results demonstrated that maize root morphological parameters exhibited the trend N₂ > N₁ > N₃ under different nitrogen treatments. Compared to N₂, low nitrogen (N₁) decreased root morphological parameters by 35.01–49.60% on average, whereas high nitrogen (N₃) led to a reduction of 49.93–61.37%. The N₂ treatment consistently maintained greater water uptake, with the highest yield of 13,336 kg·ha⁻¹ observed under the CKN₂ treatment, representing increases of 16.1% and 9.2% compared to the P₁N₂ and P₂N₂ treatments, respectively. Drought stress at the jointing stage (P₁) inhibited root development more severely than at the tasseling-silking stage (P₂), demonstrating a bidirectional adaptation strategy characterized by deeper vertical penetration under water stress and increased horizontal expansion under nitrogen imbalance. Correlation analysis revealed a positive correlation between soil nutrient content and maize yield indicators. At the same time, root characteristic values were significantly negatively correlated with yield (p < 0.05). Appropriate water–nitrogen management effectively stimulated root growth, mitigated nitrogen leaching risks, and improved yield. These findings offer a theoretical foundation for optimizing water and nitrogen management in maize production within the Yellow River Basin. Full article

In Northeast China, waterlogging has emerged as a significant challenge due to climate change, particularly during the June–August period when spring maize (Zea mays L.), at the post-tasseling phase, impedes a comprehensive understanding of responses and the development of resistance technologies. 2-(3,4-dichlorophenoxy) triethylamine (DCPTA) is suitable for the entire lifecycle of various economic and food crops, improving crop quality and enhancing stress resistance. The study investigated the ear leaf photosynthesis in relation to the root antioxidant systems’ differential responses of spring maize to waterlogging among the tasseling (VT), vesicle (R2) and dough (R4) stages, and the exogenous DCPTA regulating effect. Results revealed that waterlogging inhibited root physiological activity due to oxidative damage. Consequently, the stomatal restriction and non-stomatal restriction on photosynthesis appeared successively, and R4 was the most sensitive stage. Pretreatment with DCPTA reduced stomatal restriction by maintaining water transfer to the leaf through maintaining root physiological activity via enhanced ascorbate–glutathione cycle. Delayed non-stomatal restriction appeared due to relatively stable chlorophyll content and photosynthetic activities, and VT stage exhibited the highest susceptibility to DCPTA. The study provides a necessary theoretical foundation for comprehending the physiological mechanisms underlying yield formation of spring maize under waterlogging stress in Northeast China, and offers valuable insights for the development of chemical regulation technology. Full article

Husk number (HN) trait is an important factor affecting maize kernel dehydration rate after the physiological maturity stage. In general, a reasonable reduction in HN is a key target sought for breeding maize varieties that are suitable for mechanized harvesting. In this study, the HN of a maize natural population panel containing 232 inbred lines was analyzed, and the results showed a broad-sense heritability of 0.89, along with a wide range of phenotypic variation. With the best linear unbiased prediction (BLUP) values across the three environments, a genome-wide association study (GWAS) was conducted using 995,106 single-nucleotide polymorphism (SNP) markers. A total of 16 SNPs significantly associated with HN were identified by the mixed linear model and general linear model using the TASSEL 5.0 software program. A local linkage disequilibrium (LD) study was performed to infer the candidate interval around the lead SNPs. A total of 19 functionally annotated genes were identified. The candidate genes were divided into multiple functional types, including transcriptional regulation, signal transduction, and metabolic and cellular transport. These results provide hints for the understanding of the genetic basis of the HN trait and for the breeding of maize varieties with fewer HN and faster dehydration rate. Full article

Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. By extracting vegetation indices, image coverage, and texture features and integrating them with ground-truth data, the study examines the variation in LWC estimation accuracy across different growth stages. The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. Moreover, the RFR model consistently outperforms multiple linear regression (MLR) and ridge regression (RR) models throughout the growing season, demonstrating lower errors on both training and testing datasets. Notably, the RFR model exhibits significantly reduced errors in the training dataset compared to both MLR and RR models. Following particle swarm optimization (PSO), the prediction accuracy of the RFR model is notably enhanced, with the RRMSE on the training dataset decreasing from 1.46% to 1.19%. This study provides an effective approach for estimating maize LWC across different growth stages, supporting crop water management and precision agriculture, and offering valuable insights for the estimation of water content in other crops. Full article

Understanding the imbalance between precipitation and crop-water requirements (water deficits) is vital for adaptive water management and ensuring food security. This study examines the water deficits in China’s three main maize-cropping regions—the Northern Spring Maize Region (NS), the Huanghuaihai Summer Maize Region (HS), and the Southwest Mountain Maize Region (SWM). Using meteorological and crop data from 1981 to 2017, effective precipitation, water requirements, and water deficit rates are calculated. The results show that the average water deficit rate across all regions was 33%, with only 15.4% of precipitation meeting maize-water needs. NS had the highest deficits, especially during the jointing–tasseling stage (average: 54%), while HS had the lowest deficits, with sufficient precipitation at 54% of stations. In drought years, water deficits were significant across all regions, with NS experiencing the most severe challenges (average: 63%). Trends indicate declining effective precipitation in NS and SWM, while water requirements in NS have increased. These findings reveal critical regional disparities in the maize-water supply–demand balance and emphasize the need for targeted water management strategies to enhance the resilience of maize production to climate change. Full article

Maize (Zea mays L.) is an important crop grown for food, feed, and energy. In general, maize yield is decreased due to drought stress during the reproductive stages, and, hence, it is critical to improve the grain yield under drought. A field experiment was conducted with a split-plot design. The main factor was the irrigation regime viz. well-irrigated conditions and withholding irrigation from tasseling to grain filling for 21 days. The subplots include six treatments, namely, (i) the control (water spray), (ii) zinc oxide @ 100 ppm, (iii) manganese oxide @ 20 ppm, (iv) nZnO @ 100 ppm + nMnO @ 20 ppm, (v) Tamil Nadu Agricultural University (TNAU) Nano Revive @ 1.0%, and (vi) zinc sulfate 0.25% + manganese sulfate 0.25%. During drought stress, the anthesis–silking interval (ASI), chlorophyll a and b content, proline, starch, and carbohydrate fractions were recorded. At harvest, the grain-filling rate and duration, per cent green leaf area, and yield traits were recorded. Drought stress increased the proline (38.1%) and anthesis–silking interval (0.45 d) over the irrigated condition. However, the foliar application of ZnO (100 ppm) and nMnO (20 ppm) lowered the ASI and increased the green leaf area, leaf chlorophyll index, and proline content over water spray. The seed-filling rate (17%), seed-filling duration (11%), and seed yield (19%) decreased under drought. Nevertheless, the seed-filling rate (90%), seed-filling duration (13%), and seed yield (52%) were increased by the foliar spraying of nZnO (100 ppm) and nMnO (20 ppm) over water spray. These findings suggest that nZnO and nMnO significantly improve the grain yield of maize under drought stress conditions. Full article

This investigation examines the effects of straw-based nitrogen fertilization on soil hydrological properties and biomass partitioning in maize under arid zone conditions. A biennial field investigation was conducted during the 2016–2017 cropping seasons, with an equal nitrogen content of 225 kg ha⁻¹, and a total of 5 treatments, 100% fertilizer nitrogen (CK), 25% straw nitrogen + 75% fertilizer nitrogen (S25), 50% straw nitrogen + 50% fertilizer nitrogen (S50), 75% straw nitrogen + 25% fertilizer nitrogen (S75), 100% straw nitrogen (S100). The data demonstrated that in 2017, in comparison with CK, the soil water storage in the 0–60 cm soil layer of S25 and S50 in the large trumpet stage (V12) increased significantly by 23.32% and 25.14% (p < 0.05), respectively. In the two-year experiment, stratified moisture reserves (0–200 cm) in different treatment groups exhibited a fluctuating pattern characterized by successive increase-decrease-increase transitions along the soil profile, and overall S25 and S50 were larger than CK. In 2016, the biomass accumulation of the S50 treatment at the maturity stage (R6) was the highest, which increased by 18.11% and 19.49% compared with the CK and S75 (p < 0.05), respectively. There was statistical parity in water use efficiency between treatments. Soil moisture retention capacity of 180–200 cm soil was positively correlated with yield at the jointing (V6) and maturity (R6) stages, and soil water storage of 160–180 cm soil was positively correlated with yield at the tasselling stage (VT). Water consumption during the presowing–to–jointing phase demonstrated the strongest correlation with final grain yield. In summary, the S25 treatment in this experiment significantly enhanced the optimization of soil hydrological properties, increasing soil moisture storage, fully utilizing soil moisture, increasing dry matter accumulation in each growth period of maize, and replacing chemical nitrogen fertilizer with 25% of straw equivalent N fertilizers was beneficial to soil moisture storage. Full article

The improper application of phosphorus (P) fertilizers not only leads to resource wastage and environmental concerns but also disrupts the normal growth and yield formation of maize. This study aims to explore the effects of varying P application rates on the growth, yield, photothermal response characteristics, P accumulation dynamics, and P recovery efficiency (PRE) in summer maize, which provides a theoretical foundation for the efficient and scientific application of P fertilizers. Field experiments were conducted over two growing seasons (2021−2022) in Wen County, Henan Province, with P application rates set at 0, 30, 60, 90, and 120 kg·P₂O₅·ha⁻¹. At maturity, maize yield and its components were quantified. During key growth stages—jointing, tasseling, silking, and grain filling—plant height, leaf area, Soil and Plant Analyzer Development (SPAD) value, the fraction of photosynthetically active radiation (FPAR), canopy temperature, acid phosphatase activity (ACP), and P accumulation were measured. The results indicated that maize grain yield initially increased with P application, peaking at an average increase of 7.92–15.88%, before decreasing. The optimal P application rates were determined to be 113 kg·P₂O₅·ha⁻¹ and 68 kg·P₂O₅·ha⁻¹, respectively. P application significantly lowered canopy temperature and leaf ACP activity while significantly increasing the SPAD value and FPAR at 90 kg·P₂O₅·ha⁻¹. Logistic regression analysis of P accumulation revealed that increasing P rates enhanced the maximum (V_max) and mean (V_mean) accumulation rates, as well as the total P accumulation. Moderate P application also improved P absorption in various plant tissues and promoted the transfer of P to the grains. However, PRE, partial factor productivity from P fertilizer (PPFP), and P agronomic efficiency (PAE) declined at higher P rates. In conclusion, P fertilization enhanced maize yield, promoted growth, improved P utilization, and optimized photothermal response characteristics across different growth stages. Based on these findings, the recommended P application rate for summer maize is between 70 and 110 kg·P₂O₅·ha⁻¹. Full article