Biochar alleviates apple replant disease by reducing the growth of Fusarium oxysporum and regulating microbial communities

Apple replant disease(ARD)negatively affects plant growth and reduces yields in replanted orchards.In this study,biochar was applied to apple replant soil with Fusarium oxysporum.Our aim was to investigate whether biochar could promote plant growth and alleviate apple replant disease by reducing the growth of harmful soil microorganisms,changing soil microbial community structure and improving the soil environment.This experiment included five treatments:apple replant soil(CK),methyl bromide fumigation apple replant soil(FM),replant soil with biochar addition(2%),replant soil with F.oxysporum spore solution(8×10^(7)spores·mL^(-1)),and replant soil with biochar and F.oxysporum spore solution addition.Seedling biomass,the activity of antioxidant enzymes in the leaves and roots,and soil environmental variables were measured.Microbial community composition and community structure were analyzed using 16SrDNA and ITS2 gene sequencing.Biochar significantly reduced the abundance of F.oxysporum and increased soil microbial diversity and richness.Biochar also increased the soil enzyme activities(urease,invertase,neutral phosphatase,and catalase),the biomass(plant height,fresh weight,dry weight)and the activity of antioxidant enzymes(superoxide dismutase,peroxidase,and catalase).The root indexes of apple seedlings was also increased in replant soil by biochar.In sum,biochar promoted the growth of plants,improved the replant soil environment,and alleviated apple replant disease.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Isolation of Trichoderma virens 6PS-2 and its effects on Fusarium proliferatum f. sp. Malus domestica MR5 related to apple replant disease in China

Apple replant disease(ARD) is primarily caused by biotic factors that seriously inhibits the development of apple industry. Therefore, the use of biological control measures to inhibit the main pathogens(such as Fusarium spp.) that cause ARD is of great significance to the sustainable development of the apple industry. Trichoderma virens 6PS-2, which exhibited antagonism toward a variety of pathogens, was screened from the rhizosphere soils of healthy apple trees(Malus robusta) in different replanted orchards in the Yantai and Zibo Cities, Shandong Province, China. Its fermentation extract inhibited the growth of pathogenic Fusarium proliferatum f. sp. Malus domestica MR5, which was proportional to the concentration. These substances also increased the hairy root volume and growth of Arabidopsis thaliana lateral roots. The phenotype of Malus hupehensis seedlings and microbial community structure in rhizosphere soils in greenhouse experiment using Highthroughput sequencing were analyzed, and the field experiment with grafted apple trees were used for further verification. Compared with the application of potato dextrose broth(PDB) medium, application of 6PS-2 spore suspension directly to replanted soils could improve the growth of M. hupehensis seedlings as well as the elongation of grafted apple trees. Concomitant decreases in the gene copy number of Fusarium and increases in the culturable bacteria/fungi were also observed in the greenhouse and field experiments. The abundance of Trichoderma,Bacillus, and Streptomyces increased significantly, but that of Fusarium, Pseudarthrobacter, and Humicola decreased. The content of esters, phenols,furans, and amino acids in root exudates of M. hupehensis seedlings increased, which significantly inhibited the multiplication of Fusarium, but was positively correlated with Bacillus and Trichoderma. In summary, T. virens 6PS-2 not only directly inhibits the activity of pathogenic Fusarium but also secrets secondary metabolites with antifungal and growth-promoting potential. In addition, 6PS-2 spore suspension can also promote the growth of plants to a certain extent, and change the soil microbial community structure of rhizosphere soils. It is believed that T. virens 6PS-2 has the potential for the alleviation of apple replant disease(ARD) in China.

Research on Identification Method of Apple Diseases in Southern Xinjiang Based on Deep Learning and Its System Implementation

Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through training and optimization algorithms,and a complete apple disease identification system was developed with the model as the core,and evaluated for its performance in terms of accuracy,recall rate and speed.This study provides a reliable AI-based apple disease diagnosis solution for the apple planting industry in the southern Xinjiang,hoping to help farmers better manage and protect crop health.

Effects of Trichosporon sp. in Combination with Calcium and Fungicide on Biocontrol of Postharvest Diseases in Apple Fruits

The capability of yeast Trichosporon sp., an antagonist isolated from peach fruit, in biological control was evaluated in apple ( Malus domestica Borkh. cv. Fuji) fruits, when inoculated with different concentrations of Botrytis cinerea Pers. and Penicillium expansum (Link) Thom, as well as in combination with calcium and fungicide. The concentrations of the yeast cells and pathogen spores obviously influenced disease incidence and lesion development in apples. There was a significant negative correlation between concentrations of the yeast cells and infectivity of the pathogens. When the yeast cell suspensions reached the concentration of 10 8 colony_forming units (CFU) /mL, there was no infection caused by B. cinerea and P. expansum with spore concentrations below 10 6 spores/mL in apple fruits. The yeast at concentrations of 10 6-10 7 CFU/mL in combination with fungicide (iprodione at 50 μL/L) provided control of decay caused by B. cinerea and P. expansum better than separate application. Effect of controlling gray mould and blue mould rots was enhanced when Trichosporon sp., even at low concentration of 10 5 CFU/mL, was applied in the presence of 1%-2% CaCl 2 in an aqueous suspension.

Occurrence Regularity and Control Research of Apple Early Defoliation Disease in West Sichuan Plateau

[ Objective ] The research aimed to discuss the occurrence regularity of apple early defoliation disease and control effects of fungicides in west Sichuan plateau. [ Method ] Apple early defoliation disease in west Sichuan plateau was systematically investigated during 2009 and 2011, and the control effects of different fungicides during different spraying periods were studied. [ Result] The species of pathogen mainly was Marssonina ma/i （ P. Henn）, accounting for 32.5%, Phyl-losticta Pirina Sa accounted for 29.9%, and Alternaria mali Roberts accounted for 25.7%. The field fluctuation regularity was as follows ： the disease generally be- gan in the mid-May and ended in early and middle October with only one damage peak during the whole year, the fluctuation had positive correlation with rainfall （ R2 =0.891 8 ＊ ＊ ）. Control test showed that 40% flusilazole had the best control effect （86.5%）, which was best to apply after anthesis. [ Conclusion ] The paper provided theoretical basis for the effective control against apple early defoliation disease.

Silencing of early auxin responsive genes MdGH3-2/12 reduces the resistance to Fusarium solani in apple

Apple replant disease(ARD)has led to severe yield and quality reduction in the apple industry.Fusarium solani(F.solani)has been identified as one of the main microbial pathogens responsible for ARD.Auxin(indole-3-acetic acid,IAA),an endogenous hormone in plants,is involved in almost all plant growth and development processes and plays a role in plant immunity against pathogens.Gretchen Hagen3(GH3)is one of the early/primary auxin response genes.The aim of this study was to evaluate the function of MdGH3-2 and MdGH3-12 in the defense response of F.solani by treating MdGH3-2/12 RNAi plants with F.solani.The results show that under F.solani infection,RNAi of MdGH3-2/12 inhibited plant biomass accumulation and exacerbated root damage.After inoculation with F.solani,MdGH3-2/12 RNAi inhibited the biosynthesis of acid-amido synthetase.This led to the inhibition of free IAA combining with amino acids,resulting in excessive free IAA accumulation.This excessive free IAA altered plant tissue structure,accelerated fungal hyphal invasion,reduced the activity of antioxidant enzymes(SOD,POD and CAT),increased the reactive oxygen species(ROS)level,and reduced total chlorophyll content and photosynthetic ability,while regulating the expression of PR-related genes including PR1,PR4,PR5 and PR8.It also changed the contents of plant hormones and amino acids,and ultimately reduced the resistance to F.solani.In conclusion,these results demonstrate that MdGH3-2 and MdGH3-12 play an important role in apple tolerance to F.solani and ARD.

Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

Effects of Different Times of Bagging on Fruit Quality and Disease and Pest Incidence of Fuji Apple

The effects of bagging at different times in Mengyin area on the fruit quality and disease and pest incidence of Huimin short-cut Fuji apple were investigated,with unbagged fruit as the control. The results showed that among the apples bagged on May 5,May 15,May 25,June 2 and June 12,the single fruit weight and fruit shape index of the apples bagged on June 2 were highest,308. 1 g and 0. 868,respectively; the peeled harness of the apples bagged during May 5-25 was higher than that of the control; the peeled harness of the apples bagged on June 2 and June 12 was lower than that of the control; the soluble solids content of bagged fruit was lower than that of unbagged ones,and no significant differences were found among different treatments; the coloring index of the apples bagged on June 2 was up to 100%; the smooth finish index of the apples bagged on May 5 reached 96. 67%,the highest; the red chromatic value( a*) of bagged fruit was higher than that of unbagged ones,and there were no significant differences among different bagging treatments. The earlier the bagging was,the higher the incidence of black spot and bitter pit was. The incidence of black spot and bitter pit in bagged fruit was higher than that in unbagged ones. In conclusion,in Mengyin area of Shandong Province,bagging is better to be carried out on June 2,45 d after the full bloom.

Apple as a Source of Dietary Phytonutrients: Bioavailability and Evidence of Protective Effects against Human Cardiovascular Disease

The dietary consumption of fruit and vegetable is associated with a lower incidence of degenerative diseases such as cardiovascular disease. Most recent interest has focused on the bioactive phenolic compounds in vegetable products. All varieties of apple contain several antioxidants and polyphenols that possess many biological activities, such as antioxidant and anti-inflammation properties. The review describes the nutritional properties of apples and their derivatives, with a particular attention to polyphenol compounds. Moreover, the health benefits of apples and the potential molecular mechanisms against cardiovascular disease are reviewed.

Isolation of phloridzin-degrading,IAA-producing bacterium Ochrobactrum haematophilum and its effects on the apple replant soil environment

We isolated and identified a bacterium that could produce IAA and degrade phloridzin in the rhizosphere soil of healthy replanted apple(the rootstock is M9T337 and the scion is Yanfu 3),providing a theoretical basis for reducing the obstacles associated with apple replant disease(ARD).Isolates were screened using Salkowski colorimetry and screening medium for phloridzin.The isolate of interest(W6)was identified as Ochrobactrum haematophilum based on morphological analysis,physiological and biochemical tests,and 16S rDNA sequencing.In a laboratory experiment,W6 produced auxin and promoted the growth of Arabidopsis thaliana roots,and its degradation rate of 100 mg.L^(-1 )phloridzin was 62.0%.In a pot experiment,W6 significantly reduced the phenolic acid contents of replanted soil,lowered the abundance of the harmful fungus Fusarium solani,and increased soil enzyme activities,thereby improving the micro-ecological environment of replant soil.W6 increased the root antioxidant enzyme activity and leaf photosynthetic pigment content of replanted Malus hupehensis Rehd.seedlings,effectively alleviating the decrease in net photosynthetic rate,transpiration rate and stomatal conductance caused by ARD.In a field experiment,W6 also promoted the growth of replanted apple(the rootstock is M9T337 and the scion is Yanfu 3)saplings.Therefore,W6 can promote apple growth and degrade phenolic acids,and it can be used as an effective treatment for the reduction of ARD.

Distribution of Calcium in Bagged Apple Fruit and Relationship between Antioxidant Enzyme Activity and Bitter Pit

[Objective]The aim was to study the distribution of Ca in bagged apple fruit and the relationship between antioxidant enzyme activity and bitter pit disease,which will provide a basis for resolving Ca metabolism disorder in apple cultivation. [Method]With Fuji Apple as the tested material,the changes of Ca2＋ content and antioxidant enzyme activity in different parts of apple fruit after picking bags and storage period were determined. [Result]The results showed that Ca contents in the light surface of fruits were higher than that in the backlight surface. The Ca contents of stalk cavity were higher than that of calyx-end. The activities of SOD,POD,CAT and APX in the light surface of fruits were higher than that in the backlight surface. The activities of SOD,POD,CAT and APX of stalk cavity were higher than that of calyx-end. The contents of MDA in the light surface of fruits were lower than that in the backlight surface. The contents of MDA of stalk cavity were lower than that of calyx-end. [Conclusion]The incidence rate of bitter pit in the light surface of fruits were lower than that in the backlight surface,and the incidence rate of bitter pit of stalk cavity were lower than that of calyx-end.

Effects of Soil Texture on the Growth of Young Apple Trees and Soil Microbial Community Structure Under Replanted Conditions

A two-year field experiment was carried out in order to study the occurrence degree and mechanism of apple replant disease(ARD)in the apple orchards with different soil textures.So we can adopt appropriate controlmeasures according to the severity of ARD.Healthy two-year-old seedlings with consistent growth were selected,of which the root stock was T337 and the scion was Yanfu 3.There were significant differences in biomass between methyl bromide fumigation and replanted treatments,and the difference was the largest in clay loam,followed by sandy loam,and loam,which verified ARD in clay loam was most serious,followed by sandy loam and loam.Based on high-throughput sequencing of fungi in soil samples,fungal richness and diversity were the highest in clay loam,followed by sandy loam,and loam.The relative abundance of Fusarium in SX,SL,FX,FL,WX and WL was 7.33%,19.32%,2.70%,4.24%,10.71%and 23.87%,respectively.Based on Real-time quantitative analysis,there were significant differences in the number of Fusarium oxysporum and Fusarium solani between methyl bromide fumigation and replanted treatments,i.e.,clay loam>sandy loam>loam.Fusarium was the main pathogen causing ARD.This shows that ARD is the most serious under replanted clay loam condition.High-throughput sequencing technology was used to prove the difference in Fusarium was one of the important reasons for ARD under different soil textures.This technology provides a new idea for the prevention and control of ARD.

The effects of crop rotation combinations on the soil quality of old apple orchard

This study investigated the effects of six crop rotation combinations on the soil quality of old apple orchard and seedling growth of Malus hupehensis Rehd.(apple rootstock) under pot conditions. The inhibitory effects of crops such as Allium fistulosum, Brassica juncea, and Triticum aestivum on four species of Fusarium were observed and compared in six treatments. These were continuous cropping(CK), fumigation with the methyl bromide(FM), rotating A. fistulosum only(R1), rotating A. fistulosum and T. aestivum(R2), rotating A. fistulosum, B. juncea, and T. aestivum(R3), and fallow(FC) in a year. The results showed that the biomass of Malus hupehensis Rehd. seedlings increased significantly. The root length increased and the root architecture was optimized. The respiration rate of the root system was increased by about 1 time after rotation. The treatments of R1, R2, R3, and FC increased bacterial count by 232.17%, 96.04%, 316.21%, and 60.02%, respectively. However, the fungi were reduced in varying degrees and bacteria/fungi ratio was increased by 5–10 times. The enzyme activities, p H, and organic matter were increased, but soil bulk density was decreased. Phenolic acids such as phloridzin was decreased significantly. The copy number of four Fusarium species declined by 85.59%, 74.94%, 69.68%, and 54.41% after rotating three different crops(R3 treatment). The root volatiles of three plants inhibited mycelial growth and spore germination of four Fusarium species.

基于迁移学习的苹果落叶病识别与应用

为解决现有卷积神经网络苹果叶片病害识别模型泛化能力弱,模型体积较大等问题,该研究提出一种基于改进MobileNetV3苹果落叶病识别模型。以健康叶片和常见苹果落叶病为研究对象,包括斑点落叶病、灰斑病、褐斑病、锈病4种,每种病害2级,共9类特征,通过改进网络的注意力模块、全连接层及算子,结合迁移学习的训练方式,构建苹果落叶病识别模型。在扩充前后的数据集上对比不同的学习方式、学习率和注意力模块等对模型的影响,验证模型的识别性能。试验结果表明:采用迁移学习的方式,在训练50轮达曲线收敛,比全新学习的准确率增加6.74~10.79个百分点;使用引入的ET(efficient channel attention-tanh)注意力模块,网络损失曲线更加平滑,模型的参数量更少,模型体积减小了48%,提高了模型的泛化能力;在扩充数据集上,学习率为0.000 1时,结合迁移学习的训练方式,改进MobileNetV3(ET3-MobileNetV3)苹果落叶病识别模型,平均准确率能达到95.62%,模型体积6.29 MB。将模型部署到喷药设备上,可实现基于苹果叶片病害识别的变量喷施,该研究可为苹果叶片病害的检测与果园的现代化管理提供参考。

基于CycleGAN-IA方法和M-ConvNext网络的苹果叶片病害图像识别

针对苹果叶片病害图像识别存在数据集获取困难、样本不足、识别准确率低等问题,提出基于多尺度特征提取的病害识别网络(Multi-scale feature extraction ConvNext,M-ConvNext)模型。采用一种结合改进的循环一致性生成对抗网络与仿射变换的数据增强方法(Improved CycleGAN and affine transformation,CycleGAN-IA),首先,使用较小感受野的卷积核和残差注意力模块优化CycleGAN网络结构,使用二值交叉熵损失函数代替CycleGAN网络的均方差损失函数,以此生成高质量样本图像,提高样本特征复杂度;然后,对生成图像进行仿射变换,提高数据样本的空间复杂度,该方法解决了数据样本不足的问题,用于辅助后续的病害识别模型。其次,构建M-ConvNext网络,该网络设计G-RFB模块获取并融合各个尺度的特征信息,GELU激活函数增强网络的特征表达能力,提高苹果叶片病害图像识别准确率。最后,实验结果表明,CycleGAN-IA数据增强方法可以对数据集起到良好的扩充作用,在常用网络上验证,增强后的数据集可以有效提高苹果叶片病害图像识别准确率;通过消融实验可得,M-ConvNex识别准确率可达99.18%,较原ConvNext网络准确率提高0.41个百分点,较ResNet50、MobileNetV3和EfficientNetV2网络分别提高3.78、7.35、4.07个百分点,为后续农作物病害识别提供了新思路。

甘肃天水苹果黑星病年消长动态调查及防治药剂筛选

为明确苹果黑星病在甘肃天水的年消长动态、发病规律及筛选高效防治药剂,2023年在苹果生长季持续对苹果黑星病的病叶率和病情指数进行了调查,同时选用10种农药制剂进行田间药剂筛选试验。结果表明,天水地区苹果黑星病在5月中旬开始侵染,7月底达到第一次发病高峰,之后进入病情发展缓慢期,8月下旬至9月下旬,病菌又一次侵染秋梢,达到第二次发病高峰,之后进入病菌越冬预备期。田间防治试验表明,25%苯甲·吡唑酯SC 1000倍液、25%戊唑·异菌脲SC 2500倍液、40%氟硅唑EC 1500倍液和20%烯肟·戊唑醇SC 1000倍液的防效达83.56%~93.12%,可作为首选药剂;12.5%烯唑醇WP 1500倍液、25%吡唑醚菌酯SC 800倍液、12.5%腈菌唑EC 1000倍液和40%咪鲜胺EW 800倍液,防效次之,可作为辅助药剂,与首选药剂交替使用。综上所述,本试验明确了苹果黑星病的年消长动态、发病规律,筛选出了高效防治药剂,为苹果黑星病的有效防控提供了理论依据和技术支撑。

苹果缺钙生理性病害的主要表现及其防治措施

在生产中,苹果缺钙往往会引起生理性病害,从而使苹果的叶片和果实出现相应病害症状。近年来,由缺钙引发的生理性病害逐年加重,苹果黑点病以及果面鸡爪裂纹、裂果等病害时有发生,影响果实的口感、外观等,造成果品商品率下降。探明苹果果实缺钙的原因,利用生产管理措施来改善和缓解其引发的生理性病变,对实现苹果优质生产、以果增收等目标极为重要。介绍了苹果生产中常见的由缺钙引起的病害与病症,并根据走访实践提出防治措施,以期为西北地区的苹果果树栽培提供理论依据。