While these initiatives enriched breeding pipelines, challenges remain. Linkage drag and poor agronomic performance of some landraces hindered exotic germplasm utilization. Their regional focus limited global applicability, and conventional population development methods proved resource intensive and slow.

Southern corn rust (SCR) is a significant foliar disease, which can result in substantial corn yield losses. Unmanned aerial vehicle (UAV)-based optical remote sensing presents a promising method for efficiently monitoring SCR in field conditions. Nevertheless, its performance can be challenged by SCR's bottom-up pathogenesis and canopy occlusion in field conditions. In contrast, multi-view imaging spectral measurements (MISM) can mitigate the effects of canopy occlusion by collecting complementary information from different corn layers. However, a method to optimize the fusion of MISM for precise and efficient monitoring of SCR remains absent. In this study, an attention-based deep learning method was introduced for the automated extraction of critical spectral and spatial information from MISM to monitor SCR. First, the optimal spectral features for SCR were explored and identified. Second, the improvements in SCR monitoring were evaluated by comparing MISM with the sole use of nadir-view. Finally, the performances of two attention-based deep learning algorithms (i.e., attention-based Fully Connected Network (FCN) and attention-based Convolutional Neural Network (CNN)) were assessed against the baseline Random Forest (RF) algorithm. These results indicated that: (1) spectral indices based on green-red-red edge bands were most effective for SCR monitoring across various views; (2) MISM significantly improved the accuracy of SCR monitoring compared to that of nadir-view imaging spectral measurement (-10 % improvements in both OA and Macro F1); (3) the integration of an attention mechanism with FCN or CNN, which considers the varied importance of multi-view spectral features, further improved the accuracy of SCR monitoring. The highest accuracy in SCR monitoring (OA: 82.1 %, Macro F1: 0.82) was achieved by the attention-based FCN, with a -7 % improvement in both OA and Macro F1 relative to the RF. Overall, this study provides a promising method for efficiently monitoring SCR in field conditions, which could assist in making smart agriculture decisions and reducing pesticide inputs.

ObjectiveIn recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields.MethodsA method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study.Results and DiscussionThe research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB.ConclusionsThe aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture.

BACKGROUNDSpatial-explicit weed information is critical for controlling weed infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV)-based remote sensing presents an unprecedented opportunity for efficient, timely weed mapping. Spectral, textural, and structural measurements have been used for weed mapping, whereas thermal measurements-for example, canopy temperature (CT)-were seldom considered and used. In this study, we quantified the optimal combination of spectral, textural, structural, and CT measurements based on different machine-learning algorithms for weed mapping. RESULTSCT improved weed-mapping accuracies as complementary information for spectral, textural, and structural features (up to 5% and 0.051 improvements in overall accuracy [OA] and Marco-F1, respectively). The fusion of textural, structural, and thermal features achieved the best performance in weed mapping (OA = 96.4%, Marco-F1 = 0.964), followed by the fusion of structural and thermal features (OA = 93.6%, Marco-F1 = 0.936). The Support Vector Machine-based model achieved the best performance in weed mapping, with 3.5% and 7.1% improvements in OA and 0.036 and 0.071 in Marco-F1 respectively, compared with the best models of Random Forest and Naive Bayes Classifier. CONCLUSIONThermal measurement can complement other types of remote-sensing measurements and improve the weed-mapping accuracy within the data-fusion framework. Importantly, integrating textural, structural, and thermal features achieved the best performance for weed mapping. Our study provides a novel method for weed mapping using UAV-based multisource remote sensing measurements, which is critical for ensuring crop production in precision agriculture. (c) 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Southern corn leaf blight (SCLB) seriously threatens corn production. The timely and accurate monitoring of SCLB conditions (e.g., detection during the asymptomatic stage and severity classification during the symptomatic stage) is valuable for precision agriculture, because the application of pesticides depends on disease conditions. Compared with time-consuming and laborious field surveys, spectroscopy is a promising tool for plant disease monitoring. The unique advantages of combining multiple spectral enhancement features for monitoring rice and wheat diseases have been recognized. However, physiological and biochemical differences between maize leaves and rice and wheat leaves, along with the specific spectral response of SCLB, are likely to affect the performance of combining multiple spectral enhancement features. In addition, similar previous studies have not combined spectral slope features, i.e., first-order spectral derivatives (FSDs), with spectral bands (SBs) and spectral indices (SIs) and wavelet features (WFs) to improve plant disease detection. Thus, the performance of a method that combines FSDs, WFs, SBs, and SIs for SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification should be evaluated further. Here, the utility of combining SBs, SIs, WFs, and FSDs was quantified and evaluated in the asymptomatic detection, symptomatic detection, and symptomatic severity classification of SCLB. Various forms of spectral enhancement features that were sensitive to SCLB infection from the asymptomatic stage to the severe stage were first identified and combined using the RELIEF-F and sequential floating forward selection algorithms on the basis of two independent inoculation experiments. Finally, SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification models were developed and evaluated using the support vector machine algorithm. Results showed that combining FSDs with SBs, SIs, and WFs achieved the best performance in SCLB spectroscopic monitoring. (1) SCLB asymptomatic detection and symptomatic detection were moderately improved, i.e., overall accuracy (OA) and macro F1 (MF1) improved by ~ 1% to 2%. The OA of SCLB asymptomatic detection was 87.1% with an MF1 of 0.87, and that of symptomatic detection was 93.1% with an MF1 of 0.93. (2) SCLB symptomatic severity classification was significantly improved, i.e., OA and MF1 improved by ~ 7%. The OA of severity classification was 81.8% with am MF1 of 0.82. This study demonstrated that the complementary relationships among SBs, SIs, WFs, and FSDs could effectively improve SCLB spectroscopic monitoring. The proposed method provides a novel tool for large-scale SCLB spectroscopic monitoring. It has broad implications for assisting management decisions (i.e., when and where to apply pesticides and how much to apply) in precision agriculture.

Summary Southern corn leaf blight (SLB), caused by the necrotrophic pathogen Cochliobolus heterostrophus , is one of the maize foliar diseases and poses a great threat to corn production around the world. Identification of genetic variations underlying resistance to SLB is of paramount importance to maize yield and quality. Here, we used a random‐open‐parent association mapping population containing eight recombinant inbred line populations and one association mapping panel consisting of 513 diversity maize inbred lines with high‐density genetic markers to dissect the genetic basis of SLB resistance. Overall, 109 quantitative trait loci (QTLs) with predominantly small or moderate additive effects, and little epistatic effects were identified. We found 35 (32.1%) novel loci in comparison with the reported QTLs. We revealed that resistant alleles were significantly enriched in tropical accessions and the frequency of about half of resistant alleles decreased during the adaptation process owing to the selection of agronomic traits. A large number of annotated genes located in the SLB‐resistant QTLs were shown to be involved in plant defence pathways. Integrating genome‐wide association study, transcriptomic profiling, resequencing and gene editing, we identified ZmFUT1 and MYBR92 as the putative genes responsible for the major QTLs for resistance to C. heterostrophus . Our results present a comprehensive insight into the genetic basis of SLB resistance and provide resistant loci or genes as direct targets for crop genetic improvement.

Ear length (EL), which is controlled by quantitative trait loci (QTLs), is an important component of grain yield and as such is a key target trait in maize breeding. However, very few EL QTLs have been cloned, and their molecular mechanisms are largely unknown. Here, using a genome wide association study (GWAS), we identified a QTL, YIGE1, which encodes an unknown protein that regulates EL by affecting pistillate floret number. Overexpression of YIGE1 increased female inflorescence meristem (IM) size, increased EL and kernel number per row (KNPR), and thus enhanced grain yield. By contrast, CRISPR/Cas9 knockout and Mutator insertion mutant lines of YIGE1 displayed decreased IM size and EL. A single-nucleotide polymorphism (SNP) located in the regulatory region of YIGE1 had a large effect on its promoter strength, which positively affected EL by increasing gene expression. Further analysis shows that YIGE1 may be involved in sugar and auxin signal pathways to regulate maize ear development, thus affecting IM activity and floret production in maize inflorescence morphogenesis. These findings provide new insights into ear development and will ultimately facilitate maize molecular breeding.

Unilateral cross incompatibility (UCI) occurs between popcorn and dent corn, and represents a critical step towards speciation. It has been reported that ZmGa1P, encoding a pectin methylesterase (PME), is a male determinant of the Ga1 locus. However, the female determinant and the genetic relationship between male and female determinants at this locus are unclear. Here, we report three different types, a total of seven linked genes underlying the Ga1 locus, which control UCI phenotype by independently affecting pollen tube growth in both antagonistic and synergistic manners. These include five pollen-expressed PME genes (ZmGa1Ps-m), a silk-expressed PME gene (ZmPME3), and another silk-expressed gene (ZmPRP3), encoding a pathogenesis-related (PR) proteins. ZmGa1Ps-m confer pollen compatibility. Presence of ZmPME3 causes silk to reject incompatible pollen. ZmPRP3 promotes incompatibility pollen tube growth and thereby breaks the blocking effect of ZmPME3. In addition, evolutionary genomics analyses suggest that the divergence of the Ga1 locus existed before maize domestication and continued during breeding improvement. The knowledge gained here deepen our understanding of the complex regulation of cross incompatibility.

Broad-spectrum resistance has great values for crop breeding. However, its mechanisms are largely unknown. Here, we report the cloning of a maize NLR gene, RppK , for resistance against southern corn rust (SCR) and its cognate Avr gene, AvrRppK , from Puccinia polysora (the causal pathogen of SCR). The AvrRppK gene has no sequence variation in all examined isolates. It has high expression level during infection and can suppress pattern-triggered immunity (PTI). Further, the introgression of RppK into maize inbred lines and hybrids enhances resistance against multiple isolates of P. polysora , thereby increasing yield in the presence of SCR. Together, we show that RppK is involved in resistance against multiple P. polysora isolates and it can recognize AvrRppK, which is broadly distributed and conserved in P. polysora isolates.

metodos de identificacao, selecao e producao de safras resistentes a ferrugem polissora. o campo e relacionado a reproducao de planta e metodos para identificar e selecionar plantas com resistencia a ferrugem polissora. sao fornecidos metodos para identificar genes inovadores que codificam proteinas proporcionando resistencia de plantas a ferrugem polissora e usos dos mesmos. estes genes resistentes a doencas sao uteis na producao de plantas resistentes atraves de melhoramento, modificacao transgenica, ou edicao do genoma.

The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well-characterized. Here, we developed a maize advanced backcross-nested association mapping population in Henan Agricultural University (HNAU-NAM1) comprised of 1,625 BC1F4/BC2F4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18-88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome-wide association studies. Our analyses enabled the identification of ten QTL hot-spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU-NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.

Natural alleles that control multiple disease resistance (MDR) are valuable for crop breeding. However, only one MDR gene has been cloned in maize, and the molecular mechanisms of MDR remain unclear in maize. In this study, through map-based cloning we cloned a teosinte-derived allele of a resistance gene, Mexicana lesion mimic 1 (ZmMM1), which causes a lesion mimic phenotype and confers resistance to northern leaf blight (NLB), gray leaf spot (GLS), and southern corn rust (SCR) in maize. Strong MDR conferred by the teosinte allele is linked with polymorphisms in the 3' untranslated region of ZmMM1 that cause increased accumulation of ZmMM1 protein. ZmMM1 acts as a transcription repressor and negatively regulates the transcription of specific target genes, including ZmMM1-target gene 3 (ZmMT3), which functions as a negative regulator of plant immunity and associated cell death. The successful isolation of the ZmMM1 resistance gene will help not only in developing broad-spectrum and durable disease resistance but also in understanding the molecular mechanisms underlying MDR.

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.

Southern corn rust (SCR) is a prevalent foliar disease in maize. Deployment of resistant cultivars is an effective way to control SCR. In this study, resistance to SCR was evaluated in a BC1RIL population comprising 118 lines grown under three different field conditions. Combined with a genetic map constructed from 1635 SNP markers obtained from the maize 9.4 K SNP Affymetrix® Axiom® Genotyping Array, single quantitative trait loci (QTL) were mapped on chromosomes 4, 9, and 10, respectively. The QTL on chromosome 4 (qSCR4.08) and chromosome 9 (qSCR9.04) were stable across multiple environments, and each explained more than 10% of the phenotypic variation. The stable QTL detected could be desirable sources of SCR resistance in maize-breeding programs.

1 Running title: Genetic architecture of maize kernel traits 1 2 Correspondence to: Jianbing Yan (email: yjianbing@mail.hzau.edu.cn) and Qing Li (email: 3 qingli@mail.hzau.edu.cn) 4 Tel:+86-27-87280110 5 Fax:+86-27-87384670 6 National Key Laboratory of Crop Genetic Improvement 7 Huazhong Agricultural University 8 No.1 Shizishan Street, Hongshan District 9 Wuhan 430070, China. 10 11 Article title: The conserved and unique genetic architecture of kernel size and weight in 12 maize and rice 13 14 Authors: Jie Liu, Juan Huang, Huan Guo, Liu Lan, Hongze Wang, Yuancheng Xu, 15 Xiaohong Yang, Wenqiang Li, Hao Tong, Yingjie Xiao, Qingchun Pan, Feng Qiao, 16 Mohammad Sharif Raihan, Haijun Liu, Xuehai Zhang, Ning Yang, Xiaqing Wang, Min 17 Deng, Minliang Jin, Lijun Zhao, Xin Luo, Yang Zhou, Xiang Li, Wei Zhan, Nannan 18 Liu, Hong Wang, Gengshen Chen, Qing Li and Jianbing Yan 19 20 1 National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 21 Wuhan 430070, China. 22 2 National Maize Improvement Center of China, Beijing Key Laboratory of Crop Genetic 23 Improvement, China Agricultural University, Beijing, China. 24 25 Author contributions: J.Y. and Q.L designed and supervised this study. J.L., H.G. and J.H. 26 analyzed the data. J.L., J.H., H.G., L.L, H.W., Y.X, X.Y., W.L., H.T., Y.X., Q.P., F.Q., M.S.R., 27 H.L., X.Z., N.Y., X.W., M.D., M.J., L.Z., X.L., Y.Z., X.L., W.Z., N.L., H.W. and G.C. 28 Plant Physiology Preview. Published on August 15, 2017, as DOI:10.1104/pp.17.00708

Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis.

Sugarcane mosaic virus (SCMV) is one of the most damaging virus diseases in maize. Two major loci, Scmv1 and Scmv2, have been identified in many studies across a broad spectrum of germplasm by linkage mapping. In this study, we undertook a genome-wide association study (GWAS) in a maize association panel comprising 504 diverse inbred lines with over 556,000 SNP markers in three environments. Extensive phenotypic variation of resistance to SCMV was observed in the association panel. GWAS identified five genes significantly associated with resistance to SCMV. A cluster of four genes was detected near or within the Scmv2 region on chromosome 3 (bin 3.05). GRMZM2G116204, which encoded auxin binding protein and resided within the fine-mapped Scmv2 region, was predicted as the most likely candidate gene for Scmv2. In conclusion, we found that GWAS along with linkage analyses are effective approaches to the mapping of resistance to SCMV and to the subsequent identification of causal genes.

Northern corn leaf blight (NCLB) is a prevalent foliar disease in maize. Deployment of resistant cultivars is an effective way to control NCLB. In this study, 207 recombinant inbred lines derived from a K22 × By815 cross were planted in Yangling, China, in 2012 and 2013. NCLB score and lesion size were investigated after artificial inoculation. Significant phenotypic variation in NCLB resistance was observed in both years. Using a genetic map containing high-density single-nucleotide polymorphisms with average genetic distance of 0.74 cM, quantitative trait loci (QTL) for NCLB score and lesion size were analyzed. For NCLB score, four and three QTL were identified in 2012 and 2013, respectively. Two stable QTL were identified in both years. Of these, qNCLB5.04, located on chromosome 5 (bin 5.04), had the largest resistance effect, accounting for 19 and 20 % of the phenotypic variation in 2012 and 2013, respectively. For lesion size, six QTL were identified. Of these, one consensus QTL was associated with both lesion length and width, and the other five were associated only with lesion width. Among all QTL identified, only qNCLB5.04 was associated with both NCLB score and lesion size. Thus, our mapping results suggest that qNCLB5.04 could be a desirable target for marker-assisted selection for NCLB resistance in maize breeding programs.

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.