Humic Acid Effects on Reducing Corn Leaf Burn Caused by Foliar Spray of Urea-Ammonium Nitrate at Different Humic Acid/Urea-Ammonium Nitrate Ratios

Technologies for reducing corn leaf burn caused by foliar spray of urea-ammonium nitrate (UAN) during the early growing season are limited. A field experiment was carried out to evaluate the effects of humic acid on corn leaf burn caused by foliar spray of undiluted UAN solution on corn canopy at Jackson, TN in 2018. Thirteen treatments of the mixtures of UAN and humic acid were evaluated at V6 of corn with different UAN application rates and different UAN/humic acid ratios. Leaf burn during 1 2, 3, 4, 5, 6, 7, and 14 days after UAN foliar spray significantly differed between with or without humic acid addition. The addition of humic acid to UAN significantly reduced leaf burn at each UAN application rate (15, 25, and 35 gal/acre). The reduction of leaf burn was enhanced as the humic acid/UAN ratio went up from 10% to 30%. Leaf burn due to foliar application of UAN became severer with higher UAN rates. The linear regression of leaf burn 14 days after application with humic acid/UAN ratio was highly significant and negative. However, the linear regression of leaf burn 14 days after application with the UAN application rate was highly significant and positive. In conclusion, adding humic acid to foliar-applied UAN is beneficial for reducing corn leaf burn during the early growing season.

Effects of Different Pretreatment Modes on the Enzymatic Digestibility of Corn Leaf and Corn Stalk

Corn leaf and corn stalk were pretreated with only hot water and 0.1% sulfuric acid at 160℃ or 200℃, respectively. For hot water pretreatment, the pH of corn stalk hydrolysate decreased more rapidly than that of corn leaf as the reaction time increased. On the contrary, the pH of corn leaf hydrolysate increased more than that of corn stalk with diluted acid addition. Increasing temperature enhanced the xylose dissolution rate and increased cellulose digestibility. Compared with hot water, 0.1% sulfuric acid addition improved the xylan removal and the enzymatic hydrolysis of both corn leaf and corn stalk residue. Much less xylan must be removed to achieve the same cellulose digestibility for the corn leaf as that for the corn stalk; 55% digestibility was obtained when only 32% xylan was removed from corn leaf, whereas corn stalk required removal of about 50% of the xylan to achieve the same di- gestibility. Overall, the descending order of enzymatic digestibility was: dilute acid hydrolysate of corn leaf > dilute acid hydrolysate of corn stalk > water-only hydrolysate of corn leaf > water-only hydrolysate of corn stalk. Finally, one separate pretreatment strategy was developed to transfer corn leaf and corn stalk to fermentable sugars for fur- ther bioenergy production.

Intelligent diagnosis of northern corn leaf blight with deep learning model

Maize(Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight(NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica(Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks(DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, Google Net, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model Google Net, some of the recent loss functions developed for deep facial recognition tasks such as Arc Face, Cos Face, and A-Softmax were applied to detect NCLB. We found that a pre-trained Google Net architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.

Distribution,Etiology,Molecular Genetics and Management Perspectives of Northern Corn Leaf Blight of Maize(Zea mays L.)

Maize is cultivated extensively throughout the world and has the highest production among cereals.However,Northern corn leaf blight(NCLB)disease caused by Exherohilum turcicum,is the most devastating limiting factor of maize production.The disease causes immense losses to corn yield if it develops prior or during the tasseling and silking stages of crop development.It has a worldwide distribution and its development is favoured by cool to moderate temperatures with high relative humidity.The prevalence of the disease has increased in recent years and new races of the pathogen have been reported worldwide.The fungus E.turcicum is highly variable in nature.Though different management strategies have proved effective to reduce economic losses from NCLB,the development of varieties with resistance to E.turcicum is the most efficient and inexpensive way for disease management.Qualitative resistance for NCLB governed by Ht genes is a race-specific resistance which leads to a higher level of resistance.However,some Ht genes can easily become ineffective under the high pressure of virulent strains of the pathogen.Hence,it is imperative to understand and examine the consistency of the genomic locations of quantitative trait loci for resistance to NCLB in diverse maize populations.The breeding approaches for pyramiding resistant genes against E.turcicum in maize can impart NCLB resistance under high disease pressure environments.Furthermore,the genome editing approaches like CRISPR-cas9 and RNAi can also prove vital for developing NCLB resistant maize cultivars.As such this review delivers emphasis on the importance and current status of the disease,racial spectrum of the pathogen,genetic nature and breeding approaches for resistance and management strategies of the disease in a sustainable manner.

Effects of Different Pretreatment Modes on the Enzymatic Digestibility of Corn Leaf and Corn Stalk

Corn leaf and corn stalk were pretreated with only hot water and 0.1% sulfuric acid at 160℃ or 200℃, respectively. For hot water pretreatment, the pH of corn stalk hydrolysate decreased more rapidly than that of corn leaf as the reaction time increased. On the contrary, the pH of corn leaf hydrolysate increased more than that of corn stalk with diluted acid addition. Increasing temperature enhanced the xylose dissolution rate and increased cellulose digestibility. Compared with hot water, 0.1% sulfuric acid addition improved the xylan removal and the enzymatic hydrolysis of both corn leaf and corn stalk residue. Much less xylan must be removed to achieve the same cellulose digestibility for the corn leaf as that for the corn stalk; 55% digestibility was obtained when only 32% xylan was removed from corn leaf, whereas corn stalk required removal of about 50% of the xylan to achieve the same di- gestibility. Overall, the descending order of enzymatic digestibility was: dilute acid hydrolysate of corn leaf > dilute acid hydrolysate of corn stalk > water-only hydrolysate of corn leaf > water-only hydrolysate of corn stalk. Finally, one separate pretreatment strategy was developed to transfer corn leaf and corn stalk to fermentable sugars for fur- ther bioenergy production.

Identification and Fine Mapping of rhm1 Locus for Resistance to Southern Corn Leaf Blight in Maize

rhml is a major recessive disease resistance locus for Southern corn leaf blight （SCLB）. To further narrow down its genetic position, F2 population and BCIFI population derived from the cross between resistant （H95rhm） and susceptible parents （H95） of maize （Zea mays） were constructed. Using newly developed markers, rhml was initially delimited within an interval of 2.5 Mb, and then finally mapped to a 8.56 kb interval between InDel marker IDP961-503 and simple sequence repeat （SSR） marker A194149--1. Three polymorphic markers IDP961-504, IDP B2-3 and A194149-2 were shown to be co-segregated with the rhml locus. Sequence analysis of the 8.56 kb DNA fragment revealed that it contained only one putative gene with a predicted amino acid sequence identical to lysine histidine transporter 1 （LHT1）. Comparative sequence analysis indicated that the LHT1 in H95rhrn harbors a 354 bp insertion in its third exon as compared with that of susceptible alleles in B73, H95 and Mo17. The 354 bp insertion resulted in a truncation of the predicted protein of candidate resistance allele （LHT1-H95rhm）. Our results strongly suggest LHTI as the candidate gene for rhml against SCLB. The tightly linked molecular markers developed in this study can be directly used for molecular breeding of resistance to Southern corn leaf blight in maize.

Ascorbate peroxidase 1 confers resistance to southern corn leaf blight in maize

Southern corn leaf blight (SCLB), caused by Bipolarismaydis, is one of the most devastatingdiseases affecting maize production. However,only one SLCB resistance gene, conferring partialresistance, is currently known, underscoring theimportance of isolating new SCLB resistancerelatedgenes. Here, we performed a comparativeproteomic analysis and identified 258 proteinsshowing differential abundance during the maizeresponse to B. maydis. These proteins included anascorbate peroxidase (Zea mays ascorbate peroxidase1 (ZmAPX1)) encoded by a gene locatedwithin the mapping interval of a previously identifiedquantitative trait locus associated with SCLBresistance. ZmAPX1 overexpression resulted inlower H_(2)O_(2) accumulation and enhanced resistanceagainst B. maydis. Jasmonic acid (JA)contents and transcript levels for JA biosynthesisand responsive genes increased in ZmAPX1-overexpressing plants infected with B. maydis,whereas Zmapx1 mutants showed the oppositeeffects. We further determined that low levels of H_(2)O_(2) are accompanied by an accumulation of JAthat enhances SCLB resistance. These resultsdemonstrate that ZmAPX1 positively regulatesSCLB resistance by decreasing H_(2)O_(2) accumulationand activating the JA-mediated defensesignaling pathway. This study identified ZmAPX1as a potentially useful gene for increasing SCLBresistance. Furthermore, the generated datamay be relevant for clarifying the functions ofplant APXs.

Identification and Evaluation of Particular Corn Hybrids with Resistance against Corn Northern Leaf Blight

[ Objective] The paper was to evaluate the resistance of particular corn hybrids against northern leaf blight. [ Method ] Using artificial inoculation meth- od, the resistance of 238 copies of particular corn hybrids including silage corn, high oil corn, waxy corn and sweet corn against northern leaf blight was evaluated. [ Result] The corn samples with high resistance, resistance, moderate resistance, susceptibility and high susceptibility to northern leaf blight among 238 copies of materials in identification accounted for 0.8%, 20.6%, 44. 1%, 24.8% and 9.7%, respectively. Different types of varieties had significant difference in resist- ance. Among cern varieties with moderate resistance or higher level, silage corn accounted for 87.8% ; high oil corn and waxy corn accounted for 73.3% and 61.3 %, respectively; sweet corn was less, accounting for 44.2%. Sixteen of 30 approved particular corn varieties showed resistance, accounting for 53.3 % of total approved varieties. [Condusion] The paper provided theoretical basis for breeding and planting of particular corn hybrids with resistance against northern leaf blight.

Research Progress on Northern Leaf Blight in Corn

The northem corn leaf blight is one of the most serious diseases in maize production. The research progress on etiology, generation law and control strategy, antigen identification, resistance mechanism and inheritance of this disease was discussed. And the research work which should be enhanced in China was pointed out, such as mechanism of resistance inheritance, developing function marker, gene mining, screening resistance resource and dominant physiological race in different areas.

Relationship of late-season ear leaf nitrogen with early- to mid-season plant height of corn

Nitrogen concentration in the ear leaf is a good indicator of corn (Zea mays L.) N nutrition status during late growing season. This study was done to examine the relationship of late-season ear leaf N concentration with early- to mid- season plant height of corn at Milan, TN from 2008 to 2010 using linear, quadratic, square root, logarithmic, and exponential models. Six N rate treatments (0, 62, 123, 185, 247, and 308 kg·N·ha-1) repeated four times were implemented each year in a randomized complete block design under four major cropping systems: corn after corn, corn after soybean [Glycine max (L.) Merr.], corn after cotton [Gossypium hirsutum (L.)], and irrigated corn after soybean. The relationship of ear leaf N concentration determined at the blister growth stage (R2) with plant height measured at the 6-leaf (V6), 10-leaf (V10), and 12-leaf (V12) growth stages was statistically significant and positive in non-irrigated corn under normal weather conditions. However, the strength of this relationship was weak to moderate with the determination coefficient (R2) values ranging from 0.21 to 0.51. This relationship was generally improved as the growing season progressed from V6 to V12. Irrigation and abnormal weather seemed to have adverse effects on this relationship. The five regression models performed similarly in the evaluation of this relationship regardless of growth stage, year, and cropping system. Our results suggest that unlike the relationship of corn yield at harvest with plant height measured during early- to mid-season or the relationship of leaf N concentration with plant height when both are measured simultaneously during early- to mid-season, the relationship of late-season ear leaf N concentration with early- to mid-season plant height may not be strong enough to be used to develop algorithms for variable-rate N applications on corn within a field no matter which regression model is used to describe this relationship.

玉米秸秆青贮、玉米秸秆、桑叶青贮三种饲料体外发酵组合效应研究

为了研究玉米秸秆青贮、玉米秸秆与桑叶青贮在体外的组合效应,试验将玉米秸秆青贮、玉米秸秆与桑叶青贮3种饲料以不同比例(分别为100∶0∶0、80∶10∶10、70∶10∶20、70∶20∶10、60∶10∶30、60∶20∶20、60∶30∶10、50∶10∶40、50∶20∶30、50∶30∶20、50∶40∶10、0∶100∶0、0∶0∶100)组合进行发酵(每个组合3次重复),检测不同组合的体外产气量、干物质降解率、氨态氮浓度、pH值和挥发性脂肪酸浓度并分析不同饲料组合的组合效应。结果表明:随着发酵时间的延长,各组合的产气量呈逐渐上升趋势,发酵第2,4小时,组合12(玉米秸秆比例为100%)产气量最高;发酵第8~72小时,组合13(桑叶青贮比例为100%)产气量最高。发酵第72小时,各组合产气量随着桑叶青贮比例的提高而增加,其中组合8(桑叶青贮比例为40%)产气量显著高于其他组合(除组合13外,P<0.05);不同饲料组合对干物质降解率和氨态氮浓度有明显影响,但对pH值影响不明显。当玉米秸秆青贮比例固定时,对应组合干物质降解率随着桑叶青贮比例的增加而增加,组合13的干物质消化率和氨态氮浓度均最高。各组合pH值在6.72~6.81之间,其中组合1(玉米秸秆青贮比例为100%)的pH值最低,组合11(玉米秸秆和桑叶青贮比例分别为40%和10%)最高;乙酸、丙酸、丁酸、总挥发性脂肪酸浓度随着桑叶青贮比例的减少而降低,除组合13外,组合8的乙酸、丁酸、总挥发性脂肪酸浓度最高,各组合丙酸浓度和乙酸/丙酸差异不显著(P>0.05)。多项综合效应指数评定最优组合是组合8。说明玉米秸秆青贮、玉米秸秆、桑叶青贮的比例为50∶10∶40时,体外发酵效果最佳。

4种生物制剂对玉米2种病害的田间防治效果

为有效防治玉米小斑病和玉米锈病,筛选有效防控的生物制剂,选用复合微生物菌剂(3种有效菌种:枯草芽孢杆菌+解淀粉芽孢杆菌+胶冻样类芽孢杆菌)20亿CFU/mL、微生物菌剂哈茨木霉粉剂5亿CFU/g、20%春雷霉素水分散粒剂和4%井冈霉素A水剂4种杀菌剂进行田间药效试验。结果表明,复合微生物菌剂(3种有效菌种:枯草芽孢杆菌+解淀粉芽孢杆菌+胶冻样类芽孢杆菌)对玉米田2种病害的田间防效最优,哈茨木霉粉剂5亿CFU/g对玉米小斑病的防效较好,20%春雷霉素水分散粒剂对玉米锈病的防效较理想。该研究结果可为玉米绿色生产提供参考。

春玉米叶倾角与叶绿素含量变化及其关系研究

叶倾角和叶绿素含量是玉米重要的表型特征。基于对呼和浩特市春玉米不同生育时期叶倾角、叶绿素相对含量(SPAD值)的观测,探讨叶倾角、叶绿素的变化及其关系,以期为玉米生长发育状态监测和生产管理提供理论依据。研究表明,随着生育进程的推进,玉米全株平均叶倾角和相同叶位叶倾角呈逐渐降低趋势;生长发育前期植株叶倾角随着叶位的升高呈S形曲线变化,而在生育后期则呈单峰曲线。叶绿素含量随着生育时期的推进呈现先增加后降低趋势,同一叶片叶绿素含量以叶尖最高转变为以叶中最高。全株叶倾角与SPAD值由生育前期的负相关转变为生育后期的正相关。玉米叶倾角、叶绿素含量及其关系变化符合理想株型冠层特征,反映植株生长中心转变和对环境条件变化的适应,研究结果可为玉米的生长发育状况评估提供技术支撑,为农业生产决策提供科学依据。

基于改进YOLOv5的玉米叶枯萎检测

叶片枯萎是玉米生长过程中最常见的症状之一,利用视觉传感与模式识别技术实现玉米叶枯萎的自动检测可极大提高玉米的产量和生产效率,是智慧农业发展的重要方向。针对玉米叶遮挡场景下的枯萎检测问题,通过YOLOv5单阶段目标检测框架对叶片进行建模,并结合通道和空间注意力机制对模型架构进行改进,增强其骨干网络的特征提取能力,提高小目标玉米叶枯萎的检测精度。试验结果表明,提出的方法在Stewart_NLBimages_2019数据集上检测性能优于YOLOv5,检测平均精度均值达到86.84%,具有广阔的应用前景。

4种粗饲料经纤维素酶发酵后产物的瘤胃降解特性研究

试验旨在研究甘蔗尾叶、玉米秸秆、象草、木薯枝4种粗饲料经纤维素酶(CE)发酵后的产物在水牛瘤胃中的降解特性,为水牛养殖中粗饲料的选择利用提供参考。试验以3头安装永久性瘤胃瘘管的水牛为试验动物,采用尼龙袋法测定4种粗饲料及其经CE发酵后产物的营养成分及瘤胃降解特性。结果显示:发酵前,甘蔗尾叶与象草粗蛋白(CP)含量显著高于玉米秸秆和木薯枝(P<0.05);木薯枝的中性洗涤纤维(NDF)含量为最低(P<0.05),酸性洗涤纤维(ADF)含量为最高(P<0.05)。经CE处理后,发酵组CP含量不同程度地提高,NDF和ADF含量不同程度地下降。发酵前,各营养成分有效降解率由高到低均为:象草>甘蔗尾叶>玉米秸秆>木薯枝,象草的DM、NDF和ADF瘤胃有效降解率显著高于其他组(P<0.05),象草的CP有效降解率显著高于玉米秸秆和木薯枝叶(P<0.05),木薯枝的各营养成分瘤胃有效降解率显著低于其他组(P<0.05)。经CE处理后,与对照组对比,发酵组的DM、CP、NDF和ADF有效降解率均不同程度地提高。研究表明,4种粗饲料饲用价值排序为:象草>甘蔗尾叶>玉米秸秆>木薯枝,添加CE能够改善4种粗饲料的瘤胃降解率。

基于深度学习的玉米叶片病害识别方法研究

玉米叶片病害的及时识别和防治对保障玉米产量和质量具有重要意义。提出一种基于深度学习的玉米叶片病害识别方法,并开发了相应的移动应用。使用YOLOv5s、YOLOv5m、YOLOv5l、ResNet18、ResNet34、ResNet50、EfficientNet_b0、EfficientNet_b1、EfficientNet_b2共9个深度学习模型,对包含普通锈病、枯萎病、灰色叶斑和健康叶片四类的玉米叶片病害图像进行训练和识别,比较不同模型在识别准确率、识别速度和模型大小方面的性能,结果表明EfficientNet_b0模型具有最高的平衡准确率(96.3%)和较低的模型复杂度。基于EfficientNet_b0模型开发了安卓平台的移动应用,该应用能够实现对玉米叶片病害的实时识别或加载相册图片识别,并给出相应的类别名称和置信度、病害介绍以及建议防治措施,为玉米种植者提供了一种便捷、高效和智能的玉米叶片病害诊断工具,有助于提高玉米生产的效率和质量。

基于无人机多光谱遥感的玉米LAI监测研究

[目的]探究更高效估测玉米LAI的无人机多光谱遥感监测模型,实现对玉米叶面积指数(Leaf area index,LAI)的快速预测估算。[方法]以全生长周期的玉米植株为研究对象,通过多光谱遥感无人机获取玉米植株影像并实地采集玉米LAI,利用多光谱信息研究植被指数与玉米LAI之间的定量关系,并选择相关的植被指数;分别使用多元线性逐步回归、支持向量机回归算法(Support vector machine regression,SVM)、随机森林回归算法(Random forest regression,RF)和基于鲸鱼算法(Whale optimization algorithm,WOA)优化的随机森林算法(WOA-RF)构建玉米LAI预测模型,通过分析对比,选择最优预测模型。[结果]筛选出的植被指数NDVI、NDRE、EVI、CIG与LAI呈极显著相关(P<0.01),构建了多元线性回归模型、SVM模型、RF模型和WOARF模型的预测模型,R2分别为0.873 2、0.878 0、0.917 7和0.940 8,RMSE分别为0.277 5、0.236 5、0.209 0和0.128 7。[结论]基于WOA-RF的玉米LAI预测模型的预测精度能够满足玉米生产的需要,对玉米生长期间的种植管理具有指导意义。

Influence of late emerging weeds in glyphosate-resistant corn

Fifteen field trials were conducted from 2009 to 2011 in Ontario, Canada and Michigan, USA to determine how long glyphosate-resistant corn needs to be kept weed-free after emergence to prevent yield loss. Data were separated into two environments based on when yield loss first occurred after glyphosate application. In Environment 1 (4/15 sites) yield was not reduced when corn was kept weed-free until the 4-leaf stage. However, in Environment 2 (11/15 sites) there was no yield loss when corn was kept weed-free up to the 2-leaf stage. The most prominent weeds were velvetleaf, redroot pigweed, common ragweed, common lambsquarters and foxtail species. While later emerging weeds did not necessarily impact corn yield, weeds emerging after the 2- and 4-leaf corn stage likely produced seed that was added to the soil seed bank. Weeds emerging after 6-, 8-, and 10-leaf corn growth stages were small (low biomass/seedlings) and most likely did not reach reproductive maturity. Based on this research, corn must be maintained weed-free up to the 4-leaf stage. Any weeds emerging after that did not influence corn yield.

Spectral Characteristics Comparison of Two Summer Corn Cultivars under Different Fertilization Treatments

This study is aimed to explore the spectrum reflection characteristics of summer corn leaves in different fertilization conditions.Using hyperspectral remote sensing technology,the experiments were conducted in fields to collect the hyperspectral images of Denghai 605( DH605) and Ludan 981( LD981) in different growth period under five fertilization treatments,and then the reflectance of corn ear leaves was extracted by ENVI software. The five fertilization treatments included the control( CK) with no fertilization,40 kg and 30 kg of controlled-release fertilizer per 666. 67 m2 as base( K40 and K30),50 kg and 40 kg of compound fertilizer per 666. 67 m2 as base with 15 kg urea as seed fertilizer( F50 + N and F40 + N). The reflectance spectrums of the two corn cultivars under different fertilization treatments showed the approximately same changing trend with a reflection peak at green band( 550 nm) and a higher reflection platform at near infrared band( 760 nm-1050 nm). At the heading to filling stage,the reflectance of DH605 and LD981 was the highest under the CK,followed by the K30 and F40 + N respectively. At the filling to dough stage,the reflectance of DH605 and LD981 was the highest under the treatment K30 and F40 + N respectively,which was obviously higher than that of the other treatments. In the conditions of compound fertilizer,except the late filling stage,LD981 had little higher reflectance than DH605 at the other stages. In the conditions of controlled-release fertilizer and at dough to mature stage,LD981 had obviously higher reflectance compared to the other stages,and also higher than that of DH605; there was not obvious difference in reflectance LD981 and DH605 at the other stages.

玉米生长铜铅污染信息光谱辨别研究

为辨别农作物所受重金属胁迫种类,以受重金属铜(Cu)、铅(Pb)胁迫的玉米叶片为研究对象,利用ASD地物光谱仪获得叶片高光谱数据,通过分数阶微分(FD)对原始光谱数据进行处理,采用竞争性自适应重加权采样法(CARS)提取特征波段,最后通过多层感知机(MLP)、K-最近邻(KNN)、支持向量机(SVM) 3种模型对受胁迫的叶片光谱进行辨别,选择最优的MLP构建的FD-CARS-MLP模型,进行玉米生长铜铅污染信息光谱辨别。结果表明,FD-CARS-MLP模型对于受胁迫叶片光谱辨别的能力相较于传统方式有所提高,试验集辨别精度均可达到98%以上,0.1、0.2阶分数阶微分辨别精度可达到99%以上。选取苗期与抽穗期的玉米叶片,对其进行FD-CARS-MLP模型的可行性测试,经验证可得,FD-CARS-MLP模型辨别受重金属胁迫玉米叶片光谱数据的精度更高且更稳定,可为监测谷类作物不同重金属胁迫提供技术与方法。