Study of corn kernel breakage susceptibility as a function of its moisture content by using a laboratory grinding method

The rate of corn kernel breakage in the grain combine harvesters is a crucial factor affecting the quality of the grain shelled in the field. The objective of the present study was to determine the susceptibility of corn kernels to breakage based on the kernel moisture content in order to determine the moisture content that corresponds to the lowest rate of breakage.In addition, we evaluated the resistance to breakage of various corn cultivars. A total of 17 different corn cultivars were planted at two different sowing dates at the Beibuchang Experiment Station, Beijing and the Xinxiang Experiment Station(Henan Province) of the Chinese Academy of Agricultural Sciences. The corn kernel moisture content was systematically monitored and recorded over time, and the breakage rate was measured by using the grinding method. The results for all grain samples from the two experimental stations revealed that the breakage rate y is quadratic in moisture content x,y=0.0796 x^(2)-3.3929 x+78.779;R^(2)0=0.2646, n=512. By fitting to the regression equation, a minimum corn kernel breakage rate of 42.62% was obtained, corresponding to a corn kernel moisture content of 21.31%. Furthermore, in the 90% confidence interval, the corn kernel moisture ranging from 19.7 to 22.3% led to the lowest kernel breakage rate, which was consistent with the corn kernel moisture content allowing the lowest breakage rate of corn kernels shelled in the field with combine grain harvesters. Using the lowest breakage rate as the critical point, the correlation between breakage rate and moisture content was significantly negative for low moisture content but positive for high moisture content. The slope and correlation coefficient of the linear regression equation indicated that high moisture content led to greater sensitivity and correlation between grain breakage and moisture content. At the Beibuchang Experiment Station, the corn cultivars resistant to breakage were Zhengdan 958(ZD958) and Fengken 139(FK139), and the corn cultivars non-resistant to breakage were Lianchuang 825(LC825), Jidan 66(JD66), Lidan 295(LD295), and Jingnongke 728(JNK728). At the Xinxiang Experiment Station, the corn cultivars resistant to breakage were HT1, ZD958 and FK139, and the corn cultivars non-resistant to breakage were ZY8911, DK653 and JNK728. Thus, the breakage classifications of the six corn cultivars were consistent between the two experimental stations. In conclusion, the results suggested that the high stability of the grinding method allowed it to be used to determine the corn kernel breakage rates of different corn cultivars as a function of moisture content, thus facilitating the breeding and screening of breakage-resistant corn.

Change of Digestive Physiology in Sea Cucumber Apostichopus japonicus(Selenka) Induced by Corn Kernels Meal and Soybean Meal in Diets

The present study was conducted to determine the change of digestive physiology in sea cucumber Apostichopus japonicus(Selenka) induced by corn kernels meal and soybean meal in diets. Four experimental diets were tested, in which Sargassum thunbergii was proportionally replaced by the mixture of corn kernels meal and soybean meal. The growth performance, body composition and intestinal digestive enzyme activities in A. japonicus fed these 4 diets were examined. Results showed that the sea cucumber exhibited the maximum growth rate when 20% of S. thunbergii in the diet was replaced by corn kernels meal and soybean meal, while 40% of S. thunbergii in the diet can be replaced by the mixture of corn kernels meal and soybean meal without adversely affecting growth performance of A. japonicus. The activities of intestinal trypsin and amylase in A. japonicus can be significantly altered by corn kernels meal and soybean meal in diets. Trypsin activity in the intestine of A. japonicus significantly increased in the treatment groups compared to the control, suggesting that the supplement of corn kernels meal and soybean meal in the diets might increase the intestinal trypsin activity of A. japonicus. However, amylase activity in the intestine of A. japonicus remarkably decreased with the increasing replacement level of S. thunbergii by the mixture of corn kernels meal and soybean meal, suggesting that supplement of corn kernels meal and soybean meal in the diets might decrease the intestinal amylase activity of A. japonicus.

Corn kernel classification from few training samples

This article presents an efficient approach to classify a set of corn kernels in contact,which may contain good,or defective kernels along with impurities.The proposed approach consists of two stages,the first one is a next-generation segmentation network,trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances.An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories(ie good,defective,and impurities).The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation.Regarding the classification stage,the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions.Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided.Finally,the segmentation and classification approach proposed can be easily adapted for use with other cereal types.

Effects of typical corn kernel shapes on the forming of repose angle by DEM simulation

Corn kernel shape is an important feature to improve the accuracy of simulation results,also the repose angle is often used to calibrate model parameters in simulation.In this study,the effects of the corn kernel shapes on the behavior during corn kernels accumulation were investigated in detail.Firstly,the DEM models of three typical shapes of corn kernels were developed.Secondly,the influences of the corn kernel shapes on the process of forming repose angle were investigated,such as corn kernels distance change,energy conversion,and the contact number between corn kernels.Results show that the corn kernel shape has a significant effect on the repose angle formation.The irregular shape of corn kernels limit their rolling when adding the corn kernel length in one or two directions.In addition,the irregular shapes of corn kernels increase the contact number and extend simulation time.Regular shape corn kernels need to be mixed with irregular shape corn kernels to improve their flowability.Finally,observation of the trajectory of corn kernel repose angles indicates that spherical corn kernels contact the bottom plate early,and forming small cone first,then the cone becomes bigger to change the direction of other corn kernels from top to bottom.

Investigation of interaction effect between static and rolling friction of corn kernels on repose formation by DEM

The coefficient of static friction(SF),the coefficient of rolling friction(RF)for particles are two key parameters affecting the repose angle formation and flow characteristics.In this paper,the interaction effects of SF and RF on the formation process of corn repose angle was investigated by the discrete element method.Firstly,five shape kinds of corn models(horse tooth,spherical cone,spheroid,oblate,and irregular shape)were established.Secondly,aluminum cylinder and organic glass box were used to conduct the simulation experiments with taking SF and RF as independent factors and seeing the repose angle as dependent value.Based on simulation results the regression equations were established.Simulation results showed the relation between two factors and the rotational kinetic energy is not nonlinear,and SF does not significantly restrict the flow of corn models after increasing the flow direction,and the effect of SF on the contact number between corns and the bottom plate is remarkable,while the effect of RF on the contact number is not remarkable.Finally,the interaction effect of two factors on the repose angle was analyzed by variance analysis and results showed SF and RF all have a significant impact on the repose angle.Moreover,their interaction effect has an impact on the repose angle.

基于LSTM算法的玉米籽粒储藏温度预测

为减少储粮损失和虫霉等的发生,该文利用自制试验仓及检测系统检测储藏玉米籽粒不同位置的粮温,分析粮堆温度变化及整个粮堆热量的传递过程。试验结果表明,仓外环境温度对粮温影响较大,仓内粮食温度变化与仓外环境温度变化相比较为滞后,粮堆第一层2号位置温度在检测周期中一直处于较低状态,温度最高位置出现在第四层12号位置。基于粮堆温度变化分析,该文开展了基于长短时记忆网络(LSTM)算法的玉米籽粒储藏粮温预测研究。结果表明:(1)对比预测值与试验值可知,粮堆第一、二、三、四层测试集的粮温准确率分别为0.62、0.89、0.83、0.79;(2)位于粮堆第二层和第三层的预测结果精度较高,试验仓粮堆底层和顶层温度易受环境温度影响,粮堆热量交换速度快,温度变化迅速,导致第一层和第四层预测结果精度偏低。该研究可为粮食储藏温度预测研究提供新思路。

基于形态特征的破碎玉米籽粒识别及检测装置设计

基于K-Means聚类分割算法、颜色空间转换和形态学特征,通过对辽沈地区广泛种植的玉米的面积、面积与周长比、短轴与长轴比、圆形与矩形及五种形态特征的分析,识别出玉米碎粒。同时,设计开发了一种基于同步带轮的玉米籽粒破碎率在线检测装置,用于玉米籽粒破碎率的在线检测与识别。研究结果:①利用K平均算法分割算法将图像分割为目标区域和背景区域;②利用K平均算法分割算法将图像分割为二值化图像,进一步对二值化图像采用形态闭运算填充目标区域的非连通区域,对目标区域进行统计分析,计算出形态特征;③通过多组试验,玉米籽粒破碎的识别率可达94%。

基于注意力机制的轻量化VGG玉米籽粒图像识别模型

玉米是重要的生产资料,为实现对玉米种子的识别与保护,实验采集了5个玉米品种,经处理后共获得1778张玉米籽粒图像,建立胚面与胚乳面混合的数据集。按7∶2∶1的比例划分训练集、验证集和测试集。首先基于迁移学习选取DenseNet121、MobileNetV2、VGG16和GoogLeNet对玉米籽粒图像进行识别,在测试集上的准确率分别是94.32%、93.18%、95.45%和92.61%,由于在VGG16上的准确率最高,所以选择对VGG16进行改进,在对模型进行轻量化处理的同时引入通道注意力SE模块,构建一个新的网络模型L-SE-VGG,并与未预训练的VGG16、迁移学习的VGG16和不加SE模块的L-VGG进行对比,最终在L-SE-VGG上的识别准确率高达98.86%。研究为深度学习技术在玉米籽粒品种识别中的应用提供了新的有效策略和实验方法,为玉米籽粒品种的识别和检测提供了参考。

射频联合热风加热对玉米籽粒中黄曲霉毒素B1降解及玉米品质的影响

为研究射频-热风联合处理玉米中的黄曲霉毒素B1(aflatoxin B1,AFB1)降解效果,分析不同初始水分质量分数(19.05%、22.25%、25.55%)、射频加热温度(55、65、75、85℃)和加热持续时间(10、15、20、25 min)对玉米籽粒中AFB1降解效果及玉米品质的影响。结果表明:射频-热风联合处理可有效降解AFB1,在高水分含量下,加热温度和时间一定,初始水分含量越高,AFB1残留量越高;初始水分含量一定,随温度增加和时间延长,AFB1残留量随之减少,且加热持续时间对降解效果的影响大于加热温度;射频-热风联合处理玉米籽粒过程中,对其品质有一定影响,随温度升高和时间延长,与对照组相比,蛋白质、脂肪含量无显著差异(P>0.05),黏度系数显著降低(P<0.05);低场核磁共振分析表明,射频加热过程中水分迁移效果明显,且水分迁移特性与玉米的糊化特性、蛋白质、脂肪含量显著相关(P<0.05)。

基于FSLYOLO v8n的玉米籽粒收获质量在线检测方法研究

玉米籽粒破碎率和含杂率是评价玉米收获质量的关键指标。针对当前玉米籽粒直收机缺少适用于复杂田间作业环境的收获质量在线检测方法的问题,提出一种适用于小目标、多数量检测目标的玉米籽粒破碎率、含杂率轻量化检测方法。首先,根据图像中完整籽粒、破碎籽粒、玉米芯和玉米叶个体数量与个体质量的关系建立数量-质量回归模型,提出了籽粒破碎率和含杂率评估方法。其次,针对籽粒及杂质大小相近,检测物数量多,检测物面积小的特点,提出一种改进的FSLYOLO v8n算法。算法通过FasterBlock模块和无参数注意力机制SimAM改进主干网络结构,并通过使用共享卷积结合Scale模块对检测头进行改进。此外,使用SlidLoss函数替代YOLO v8n的原类别分类损失函数。FSLYOLO v8n模型的mAP@50为97.46%、帧速率为186.4 f/s,与YOLO v8n相比提高6.35%和45 f/s,且网络参数量、浮点运算量分别压缩到YOLO v8n的66.50%、64.63%,模型内存占用量仅为4.0 MB,其性能优于目前常用的轻量化模型。台架试验结果表明,提出的检测方法能够精准检测玉米籽粒破碎和含杂情况,检测准确率高达95.33%和96.15%。将改进后的模型部署在Jetson TX2开发板上,配合检测装置安装到玉米联合收获机上开展田间试验,结果表明,模型能够精准区分籽粒和杂质,满足田间工作需求。

不同颗粒形态玉米粮堆单轴压缩变形的试验研究

仓储玉米粮堆装粮过程中籽粒易破碎和分级,粮堆易产生不均匀沉降。针对破碎玉米籽粒颗粒形态差异对粮堆压缩变形的影响问题,以完整籽粒、半粒和粉末状玉米粮堆为研究对象,通过单轴压缩试验,研究了竖向压力为200 kPa下3种颗粒形态玉米粮堆的压缩变形。结果表明:随着荷载的增加,粮堆压缩变形增大,恒定荷载下,玉米粮堆的应变随时间的延长而增加。压缩过程中,玉米粮堆密度增加,孔隙率减小,体积模量在荷载增加阶段增大,荷载不变时逐渐减小,完整籽粒玉米粮堆体积模量较大。Hooke弹簧与Maxwell单元串联,并与Kelvin模型单元串联组成的模型能较好地预测玉米粮堆的压缩变形。研究结果可为仓储粮堆压缩变形的研究提供理论支撑。

基于北方苍鹰优化核极限学习机的玉米品种鉴别研究

玉米作为我国种植最为广泛的农作物,其产量对于我国粮食安全具有重大意义,由于不同品种具有不同的特性,根据种植条件科学选种能够很大限度上提高产量并且降低生产成本,但不同玉米种子外观极其相似,导致科学选种工作产生了一定难度。该研究基于近红外光谱技术结合核极限学习机(KELM)针对玉米品种分类问题构建鉴别模型,利用甜糯黄玉米、甜妃、昌甜、金色超人、香甜5号五种玉米种子,每种取(13±0.5)g作为一份样品,共计126个样品作为研究对象,对采集的近红外光谱数据进行标准正态变量变换(SNV)处理后采用竞争性自适应重加权采样法(CARS)对数据集进行降维。按照5∶1的比例将样本随机分为训练集和测试集,探讨北方苍鹰优化算法(NGO)对KELM模型性能的影响。分别使用NGO算法、粒子群算法(PSO)和灰狼算法(GWO)对KELM模型的两个重要参正则化参数C和高斯核函数γ进行寻优,选择五折交叉验证识别准确率最高时对应的C和γ作为建模参数,建立KELM分类模型。将各算法寻优后建立的KELM模型性能进行对比。实验发现,通过NGO算法寻优后建立的KELM模型性能高于其他两种算法优化的KELM模型,测试集识别准确率可达100%。在CARS降维的基础上分别建立CARS-NGO-KELM、CARS-PSO-KELM和CARS-GWO-KELM模型,结果表明,在面对降维后的数据时NGO算法仍能表现较好的性能,其测试集准确率和F 1值均达到了100%。为了验证样本数量对模型的影响,使用各品种样品数量同步后的共计90个样品重新训练KELM模型。结果表明,在同步各类样品数量后,各个模型在训练集和测试集上的表现均有提升。该研究在近红外光谱的基础上引入多种优化算法构建核极限学习机模型并将识别准确率提升至100%,实现了对玉米种子快速、无损、准确的品种鉴别,研究结果为玉米品种快速鉴别提供了一种新方法,同时也对监管部门具有一定的指导意义。

Impact of Nitrogen Fertilization on the Oil, Protein, Starch, and Ethanol Yield of Corn （Zea mays L,） Grown for Biofuel Production

Nitrogen fertilization is one of the greatest challenges associated with the production of biofuel from corn grain. The objective of this research was to determine the effect of N fertilization on the content and yield of oil, protein, and starch in corn grain. The project was done in Southeast Missouri （USA）, from 2007 to 2009 in a silt loam soil. Corn grain contains 3.8-4.2% oil, 6.7%-8.9% protein, 68.0%-70.4% extractable starch, and 76.0%-77.7% total starch. The total starch yield ranged from 2.8 to 7.8 mg.ha1 whereas the extractable starch varied between 2.5 to 7.1 mg-ha1. As the N rate went up, the oil and starch content of the grain decreased, whereas the protein content and the protein, starch, and oil yields increased, reaching their maximum at the N rate corresponding to 179.0 kg N.ha~. The potential ethanol yield varied between 616.2 and 7,035.1 L-ha1 depending on the method of conversion of the starch into ethanol, the year and the N rate （P 〈 0.0001）. The negative correlation between N fertilization rate and starch content suggested that when farmers add too much N to their soil to increase grain yield, they reduce the starch content in those grains, and consequently the conversion into bioethanol. Therefore, for biofuel production to be beneficial for both farmers and the power plant owners, an agreement needs to be made with regard to the use of fertilizers.

玉米籽粒次生代谢物质分布及其抗氧化活性

基于玉米分层剥皮技术,结合靶向代谢组学检测方法,对玉米不同部位的类胡萝卜素等特征性成分及酚类物质等次生代谢物质进行分析,并探讨其抗氧化活性。结果表明:类胡萝卜素主要存在于玉米籽粒的糊粉层和外胚乳中;玉米酚类物质主要以结合态形式存在于果皮、种皮及糊粉层部位,各部位共检出单体特征酚17种,其中香草醛、对羟基苯甲酸、阿魏酸等含量较高;玉米籽粒各部位的抗氧化活性与其酚类物质含量极显著正相关(P<0.01),其中单体特征酚的贡献主要来自于香草醛、对羟基苯甲酸和丁香醛,玉米内皮层的抗氧化活性相对更高。

分层破胚剥皮玉米不同部位营养成分富集特征

为明确玉米籽粒营养成分的分布差异及不同部位富集特征,应用快速缓苏、微量着水半湿法分层破胚剥皮技术,结合靶向代谢组学方法,对郑单958玉米不同部位的营养成分及基础代谢物质进行分析与比较。结果表明玉米籽粒不同部位的淀粉、脂肪、矿物元素和膳食纤维等营养物质含量存在显著差异(P<0.05)。该研究中的玉米内皮层可能主要由种皮、糊粉层及部分外胚乳构成,该部位营养成分的种类及含量均较为丰富,其中水溶性膳食纤维含量显著高于其他部位(P<0.05),可作为玉米水溶性膳食纤维的提取分离来源。K、P和Mg元素是玉米中含量最高的矿物元素,主要存在于胚芽中,Fe、Zn、Mn和Cu元素在胚芽和玉米皮层中均有较多分布,精制加工会导致这些矿物元素的损失。玉米胚芽中水解氨基酸种类较其他部位丰富且含量较高(P<0.05),甜味氨基酸占总游离氨基酸含量的24.49%,高于玉米皮层部位、显著高于胚乳部位。研究结果为玉米营养健康食品的创制、玉米精深加工及相关专用装备的研发提供参考。

How plant density affects maize spike differentiation, kernel set, and grain yield formation in Northeast China?

A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhongdan 909(ZD909) as tolerant hybrid to crowding stress, Jidan 209(JD209) and Neidan 4(ND4) as intolerant hybrids to crowding stress) in Northeast China. Plant densities of 4.50×104(D1), 6.75×104(D2), 9.00×104(D3), 11.25×104(D4), and 13.50×104(D5) plants ha-1had no significant effects on initial time of tassel and ear differentiation of maize. Instead, higher plant density delayed the tassel and ear development during floret differentiation and sexual organ formation stage, subsequently resulting in ASI increments at the rate of 1.2–2.9 days on average for ZD909 in 2013–2014, 0.7–4.2 days for JD209 in 2013, and 0.5–3.7 days for ND4 in 2014, respectively, under the treatments of D2, D3, D4, and D5 compared to that under the D1 treatment. Total florets, silking florets, and silking rates of ear showed slightly decrease trends with the plant density increasing, whereas the normal kernels seriously decreased at the rate of 11.0–44.9% on average for ZD909 in 2013–2014, 2.0–32.6% for JD209 in 2013, and 9.7–28.3% for ND4 in 2014 with the plant density increased compared to that under the D1 treatment due to increased florets abortive rates. It was also observed that 100-kernel weight of ZD909 showed less decrease trend compared that of JD209 and ND4 along with the plant densities increase. As a consequence, ZD909 gained its highest grain yield by 13.7 t ha-1on average at the plant density of 9.00×104 plants ha-1, whereas JD209 and ND4 reached their highest grain yields by 11.7 and 10.2 t ha-1at the plant density of 6.75×104 plants ha-1, respectively. Our experiment demonstrated that hybrids with lower ASI, higher kernel number potential per ear, and relative constant 100-kernel weight(e.g., ZD909) could achieve higher yield under dense planting in high latitude area(e.g., Northeast China).

基于深度学习的玉米籽粒破损在线检测技术

玉米籽粒破损是制约中国玉米籽粒直收技术推广应用的瓶颈问题,如何快速准确地获取玉米收获过程中籽粒损伤情况是玉米智能化收获的关键。为了解决这一问题,该研究提出一种基于深度学习的玉米籽粒破损检测装置及方法,该方法采用籽粒单层化装置不断获取高质量玉米籽粒集图像数据,并通过深度学习分割、分类两阶段模型实现破损玉米籽粒检测。图像分割阶段通过深度学习经典实例分割模型(Mask R-CNN)完成区域内玉米籽粒单体分割;而图像分类则由该研究基于残差模块提出的新型网络模型(BCK-CNN)实现。为了评价BCK-CNN分类模型的有效性,将其和其他典型深度学习分类模型进行对比测试,并利用可视化的技术评估了不同模型对玉米籽粒的分类性能。结果表明:BCK-CNN模型对完整、破损玉米籽粒的分类准确性分别达到96.5%、94.2%。另外,选取平均相对误差为评价指标,通过模拟试验对比验证了该检测方法对破损玉米籽粒的检测性能。结果表明:相较于人工计算籽粒破损率,该研究提出的破损玉米籽粒检测方法计算得到的平均相对误差仅4.02%;且将其部署在移动工控机上对单周期玉米籽粒集图像检测时间可以控制在1.2 s内,研究结果为玉米收获过程中破损籽粒高效精准检测提供参考。

机器视觉在农作物种子检测中的研究进展

农作物种子是农业生产的基础。种子检测作为一种重要的手段,在种子生产、贸易和利用的各个环节都扮演着不可或缺的角色。然而传统的农作物种子识别方法效率低,需要人力以及专业检测设备的支持。相比之下,机器视觉技术能够通过模拟人的视觉功能来实现对目标的无损检测,效率高、准确度高,有助于实现农作物种子的品种识别、分级、分类的自动化、智能化。首先简单叙述了机器视觉技术中图像采集、预处理的方法,并以玉米种子为例给出了目前主流的处理流程,然后具体叙述了机器视觉技术中传统机器学习和深度学习两种检测方式在农作物种子检测中的应用,最后针对玉米不完善粒的研究,在分为以上两种检测方式进行具体叙述的同时,指出了目前存在的问题以及玉米不完善粒检测未来的研究方向。

黑玉米粒花青素提取工艺及其生物活性研究进展

黑玉米粒花青素为黄酮类化合物,其生物活性广泛,有超强的抗氧化、抗癌、降血脂、降血糖、调节胃肠道菌群、减轻心脏毒性等生物活性,在食品、药品和保健品开发方面具有广阔的应用前景。本文对黑玉米粒花青素的提取工艺及其生物活性进行综述,为黑玉米粒花青素的深入研究及黑玉米资源合理开发利用提供参考。

植物乳杆菌ST3.5的分离鉴定及其对霉菌的抑制作用

乳酸菌因具有拮抗霉菌等有害微生物的潜力,有望成为下一代安全、稳定的生物抗菌剂。本研究筛选获得了一株抑制霉菌活性较好的菌株ST3.5,基于16S rRNA测序鉴定为植物乳杆菌,并对其耐酸耐胆盐、抑制病原菌、耐药等特性进行分析。进一步对植物乳杆菌ST3.5的无细胞上清液进行酸处理、热处理及酶处理,分析其中主要的抑霉菌物质,通过高效液相色谱(HPLC)测定上清液中有机酸含量,扫描电镜(SEM)观察其对霉菌菌丝的破坏情况,并以玉米粒为实际样本进行生物防治试验。结果表明,植物乳杆菌ST3.5具有良好的耐酸特性,耐胆盐能力较弱,对庆大霉素、卡那霉素等抗生素具有耐药性,对氨苄西林、氯霉素等抗生素敏感,可以抑制致病菌的生长。有机酸分析发现植物乳杆菌ST3.5无细胞上清液中乳酸含量最高(22.02±0.23)g/L,其次是柠檬酸(4.99±0.04)g/L和乙酸(3.67±0.06)g/L。SEM结果显示上清液对霉菌菌丝有破坏作用。此外,生物防治实验证实了植物乳杆菌ST3.5无细胞上清液能够抑制玉米表面霉菌的生长。综上所述,植物乳杆菌ST3.5能够抑制霉菌生长,可用于开发生物防治制剂,以最大限度地减少霉菌污染并保障食品安全。