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 ima...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.展开更多
林木腐烂病是苹果树、梨树和杨树等林木枝干的重要真菌性病害。为了筛选出对苹果树腐烂病菌Valsa mali var.mali、梨树腐烂病菌V.mali var.pyri和杨树腐烂病菌V.sordida等3种不同寄主腐烂病菌都能有效防控的杀菌剂,本研究开展室内毒力...林木腐烂病是苹果树、梨树和杨树等林木枝干的重要真菌性病害。为了筛选出对苹果树腐烂病菌Valsa mali var.mali、梨树腐烂病菌V.mali var.pyri和杨树腐烂病菌V.sordida等3种不同寄主腐烂病菌都能有效防控的杀菌剂,本研究开展室内毒力试验比较了7种杀菌剂对3种腐烂病病原菌菌丝生长和分生孢子萌发的抑制效果,并进一步通过田间活性测定试验比较7种杀菌剂对梨树腐烂病病斑扩展和分生孢子发生的防治效果,同时测定了增效剂8.6%聚乙二醇(PEG)对7种杀菌剂的增效作用。毒力测定结果表明,苯醚甲环唑、戊唑醇、吡唑醚菌酯和丙唑·多菌灵对3种腐烂病病原菌菌丝生长和分生孢子萌发的抑制作用较强,其中EC_(50)平均值最低的是苯醚甲环唑,而戊唑醇的MIC平均值最低,在0.33 mg/L浓度下对3种腐烂病病原菌的菌丝生长和分生孢子萌发抑制率均达到100%。田间试验结果表明,45%苯醚甲环唑SC、43%戊唑醇SC和35%丙唑·多菌灵SE对梨树腐烂病病斑扩展和分生孢子萌发的防治效果突出,其中45%苯醚甲环唑SC 30.00 mg/L对病斑扩展防治效果达到82.23%,孢子萌发抑制效果达到85.96%,田间防治效果最好。10%丙硫唑SC+8.6%PEG处理组对病斑扩展防治效果提高了15.39百分点,达到73.46%,分生孢子萌发抑制率提高了23.75百分点,达到83.06%,增效作用显著。本研究为苹果树、梨树和杨树等3种寄主腐烂病的化学防控提供了科学依据。展开更多
Auxin is throughout the entire life process of plants and is involved in the crosstalk with other hormones,yet its role in apple disease resistance remains unclear.In this study,we investigated the function of auxin/i...Auxin is throughout the entire life process of plants and is involved in the crosstalk with other hormones,yet its role in apple disease resistance remains unclear.In this study,we investigated the function of auxin/indole-3-acetic acid(IAA)gene Md IAA24 overexpression in enhancing apple resistance to Glomerella leaf spot(GLS)caused by Colletotrichum fructicola(Cf).Analysis revealed that,upon Cf infection,35S::Md IAA24 plants exhibited enhanced superoxide dismutase(SOD)and peroxidase(POD)activity,as well as a greater amount of glutathione(reduced form)and ascorbic acid accumulation,resulting in less H_(2)O_(2)and superoxide anion(O_(2)^(-))in apple leaves.Furthermore,35S::Md IAA24 plants produced more protocatechuic acid,proanthocyanidins B1,proanthocyanidins B2 and chlorogenic acid when infected with Cf.Following Cf infection,35S::Md IAA24 plants presented lower levels of IAA and jasmonic acid(JA),but higher levels of salicylic acid(SA),along with the expression of related genes.The overexpression of Md IAA24 was observed to enhance the activity of chitinase andβ-1,3-glucanase in Cfinfected leaves.The results indicated the ability of Md IAA24 to regulate the crosstalk between IAA,JA and SA,and to improve reactive oxygen species(ROS)scavenging and defense-related enzymes activity.This jointly contributed to GLS resistance in apple.展开更多
基金supported in part by the General Program Hunan Provincial Natural Science Foundation of 2022,China(2022JJ31022)the Undergraduate Education Reform Project of Hunan Province,China(HNJG-20210532)the National Natural Science Foundation of China(62276276)。
文摘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.
基金supported by the National Key Research and Development Program of China(Grant No.2018YFD1000307)the National Natural Science Foundation of China(Grant No.32172529)+2 种基金the Special Funds for Major Scientific and Technological Innovation from Shaanxi Province(Grant No.2020zdzx03-0101)the Earmarked Fund for China Agriculture Research System(Grant No.CARS-27)China Postdoctoral Science Foundation(Grant Nos.2017M610657,2018T111108)。
文摘Auxin is throughout the entire life process of plants and is involved in the crosstalk with other hormones,yet its role in apple disease resistance remains unclear.In this study,we investigated the function of auxin/indole-3-acetic acid(IAA)gene Md IAA24 overexpression in enhancing apple resistance to Glomerella leaf spot(GLS)caused by Colletotrichum fructicola(Cf).Analysis revealed that,upon Cf infection,35S::Md IAA24 plants exhibited enhanced superoxide dismutase(SOD)and peroxidase(POD)activity,as well as a greater amount of glutathione(reduced form)and ascorbic acid accumulation,resulting in less H_(2)O_(2)and superoxide anion(O_(2)^(-))in apple leaves.Furthermore,35S::Md IAA24 plants produced more protocatechuic acid,proanthocyanidins B1,proanthocyanidins B2 and chlorogenic acid when infected with Cf.Following Cf infection,35S::Md IAA24 plants presented lower levels of IAA and jasmonic acid(JA),but higher levels of salicylic acid(SA),along with the expression of related genes.The overexpression of Md IAA24 was observed to enhance the activity of chitinase andβ-1,3-glucanase in Cfinfected leaves.The results indicated the ability of Md IAA24 to regulate the crosstalk between IAA,JA and SA,and to improve reactive oxygen species(ROS)scavenging and defense-related enzymes activity.This jointly contributed to GLS resistance in apple.
