In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectiv...In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectively and accurately segmenting leaf lesions presents considerable challenges.Existing grape leaf lesion segmentationmodels have several limitations,such as a large number of parameters,long training durations,and limited precision in extracting small lesions and boundary details.To address these issues,we propose an enhanced DeepLabv3+model incorporating Strip Pooling,Content-Guided Fusion,and Convolutional Block Attention Module(SFC_DeepLabv3+),an enhanced lesion segmentation method based on DeepLabv3+.This approach uses the lightweight MobileNetv2 backbone to replace the original Xception,incorporates a lightweight convolutional block attention module,and introduces a content-guided feature fusion module to improve the detection accuracy of small lesions and blurred boundaries.Experimental results showthat the enhancedmodel achieves a mean Intersection overUnion(mIoU)of 90.98%,amean Pixel Accuracy(mPA)of 94.33%,and a precision of 95.84%.This represents relative gains of 2.22%,1.78%,and 0.89%respectively compared to the original model.Additionally,its complexity is significantly reduced without sacrificing performance,the parameter count is reduced to 6.27 M,a decrease of 88.5%compared to the original model,floating point of operations(GFLOPs)drops from 83.62 to 29.00 G,a reduction of 65.1%.Additionally,Frames Per Second(FPS)increases from 63.7 to 74.3 FPS,marking an improvement of 16.7%.Compared to other models,the improved architecture shows faster convergence and superior segmentation accuracy,making it highly suitable for applications in resource-constrained environments.展开更多
Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to p...Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.展开更多
Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive success...Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.展开更多
The effects of the severity and timing of leaf removal(LR)on the amino acids of Sauvignon Blanc grapes and wines were studied during the 2017 growing season.High-performance liquid chromatography(HPLC)was used to anal...The effects of the severity and timing of leaf removal(LR)on the amino acids of Sauvignon Blanc grapes and wines were studied during the 2017 growing season.High-performance liquid chromatography(HPLC)was used to analyze the amino acids profiles of grape berries and wines.The basal leaves were removed at three time points(40,56 and 72 days after flowering,named LR40,LR56 and LR72,respectively)at two severity levels(one at which the first,third,and fifth basal leaves of each shoot were removed(50%level);and another at which the first six basal leaves were removed(100%level)).The results showed that leaf removal had little impact on total soluble solids(°Brix),titratable acidity,pH or berry weight.The LR72-50%treated grapes had higher berry weight,titratable acidity and°Brix than those of the other treatments.The highest concentrations of total amino acids and of total amino acids except proline were detected in LR72-50%treated grapes(2 952.58 and 2 764.36 mg L^-1,respectively);the lowest were detected in LR72-100%treated grapes(2 172.82 and 2 038.71 mg L^-1,respectively).LR72-50%treatment significantly promoted the synthesis of aspartic acid,serine,arginine,alanine,aminobutyric acid and proline at both severity levels for grapes,the concentrations of all of these amino acids were increased relative to the control concentrations.The LR72-50%,LR40-100%and LR72-100%treated wines had higher total amino acids concentrations and higher concentrations of some individual amino acids,such as arginine,alanine and serine,than did the control wines.Of all the amino acids studied,glycine,tyrosine,cysteine,methionine and lysine were not significantly influenced by the timing or severity basal defoliation in grapes and wines.The present study reveals the effects of the timing and severity of leaf removal on the amino acids profiles of grapes and wines.展开更多
Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013...Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013 and 2014, and comprehensive analysis of the chemical and volatile composition in berries was performed. The results showed that content of reducing sugar in both varieties was not affected while total acid was generally decreased by leaf removal and cluster thinning. The pH of berry juice was correspondingly higher in most treatment groups. Meanwhile, promoting effects on accumulation of total phenols, tannin in both varieties and total anthocyanins in Cabernet Sauvignon were found. As for monomeric anthocyanins, percentage of malvidin and its derivatives was decreased by leaf removal and cluster thinning. Besides, cinnamylated anthocyanins decreased with the intensity of cluster thinning. The accumulation of non-anthocyanin phenolics was similarly affected in the two varieties. Notably, cluster thinning was more effective on enhancing the phenolics content than leaf removal. The combination of middle level of leaf removal and cluster thinning was the most favor to the accumulation of phenolic acids. Furthermore, cluster thinning could also significantly enhance the synthesis of flavanols and stilbenes. Lastly, content and variety of aroma compounds in both grape varieties were also significantly affected by the treatments. The results provided a theoretical basis for a combination of leaf removal and cluster thinning to improve quality of grapes and wines.展开更多
The demand of grape in Bangladesh is fulfilled through import from foreign countries. The fruits of local cultivars of grapes are sour and seeded. Development of seedless grape varieties having increased sweetness, hi...The demand of grape in Bangladesh is fulfilled through import from foreign countries. The fruits of local cultivars of grapes are sour and seeded. Development of seedless grape varieties having increased sweetness, higher yield with better nutritional quality is necessary to reduce the import dependency. The present research activities are the part of a grape improvement project. A pot experiment was conducted at the Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh, during June to November 2011 to determine the suitable gamma irradiation doses on growth, leaf area and biochemical characters of grape saplings. Three vegetative bud stages viz. bud initiation stage, 4-leaf stage and 8-leaf stage, and four doses of gamma irradiation viz. 0, 5, 10, and 15 Gy were used as treatments. The experiment was laid out in a Randomized Complete Block Design with four replications. Different irradiation doses and vegetative bud stages showed significant variations in respect of plant growth characters, leaf area, soluble protein and total sugar content. Interaction effects also had significant variations on most of the parameters studied. Higher doses of gamma irradiation had showed detrimental effect on grape saplings. Generally, increased in irradiation doses showed decreased and detrimental effects on most of the parameters under study. Maximum numbers and length of roots, total dry matter, leaf area and chlorophyll-a and chlorophyll-b content were found at 5 Gy irradiation dose. Total soluble protein and sugar content of leaf were found maximum at no irradiation and 15 Gy, respectively. Higher number of roots and length, total dry matter, leaf area, chlorophyll-a, and b and soluble protein content of leaf were observed at bud initiation stage while 8-leaf stage showed maximum total sugar of leaf. In the combined effect of gamma irradiation and vegetative bud stages, all parameters showed best results in 5 Gy with bud initiation stage except total sugar content of leaf.展开更多
An experiment was conducted to investigate the response of salicylic acid as a second messenger to the heat stress in grape plants. For this purpose, all leaves of grape (Vitis vinifera×V. labrussa L. cv. Jingxiu...An experiment was conducted to investigate the response of salicylic acid as a second messenger to the heat stress in grape plants. For this purpose, all leaves of grape (Vitis vinifera×V. labrussa L. cv. Jingxiu) plants were removed except the 3rd, 4th, 5th, 6th, and 7th ones. The 5th leaf was fed with C-SA, and the 4th and 6th leaves were exposed to high 14 temperature at 40±0.5°C. It was observed that more C-SA transported out from the 5th leaf and the distribution of C-SA 14 14 in each organ of plant altered in response to heat stress. The accumulation of C-SA in both the 4th and 6th leaves being 14 exposed to high temperature was at least three times higher than that in control. The distribution of C-SA in other distal 14 leaves (the 3rd and 7th leaf) decreased, but more C-SA accumulated in stems adjacent to the 4th or 6th leaf exposed to 14 high temperature. In addition, there was more C-SA being transported upwards or downwards while the 4th and 6th 14 leaves were exposed to high temperature respectively. Therefore, our results suggested that SA was closely involved in signal transduction of heat stress in grape plants. However, the ratio of C radioactivity assayed after SA being extracted 14 to that of direct assay with apparatus was more than 70%, which indicated about 30% C was lost or catabolized during 14 transportation.展开更多
基金supported by the following grants:Zhejiang A&F University Research Development Fund(Talent Initiation Project No.2021LFR048)and 2023 University-Enterprise Joint Research Program(Grant No.LHYFZ2302)from the Modern Agricultural and Forestry Artificial Intelligence Industry Academy.
文摘In recent years,fungal diseases affecting grape crops have attracted significant attention.Currently,the assessment of black rot severitymainly depends on the ratio of lesion area to leaf surface area.However,effectively and accurately segmenting leaf lesions presents considerable challenges.Existing grape leaf lesion segmentationmodels have several limitations,such as a large number of parameters,long training durations,and limited precision in extracting small lesions and boundary details.To address these issues,we propose an enhanced DeepLabv3+model incorporating Strip Pooling,Content-Guided Fusion,and Convolutional Block Attention Module(SFC_DeepLabv3+),an enhanced lesion segmentation method based on DeepLabv3+.This approach uses the lightweight MobileNetv2 backbone to replace the original Xception,incorporates a lightweight convolutional block attention module,and introduces a content-guided feature fusion module to improve the detection accuracy of small lesions and blurred boundaries.Experimental results showthat the enhancedmodel achieves a mean Intersection overUnion(mIoU)of 90.98%,amean Pixel Accuracy(mPA)of 94.33%,and a precision of 95.84%.This represents relative gains of 2.22%,1.78%,and 0.89%respectively compared to the original model.Additionally,its complexity is significantly reduced without sacrificing performance,the parameter count is reduced to 6.27 M,a decrease of 88.5%compared to the original model,floating point of operations(GFLOPs)drops from 83.62 to 29.00 G,a reduction of 65.1%.Additionally,Frames Per Second(FPS)increases from 63.7 to 74.3 FPS,marking an improvement of 16.7%.Compared to other models,the improved architecture shows faster convergence and superior segmentation accuracy,making it highly suitable for applications in resource-constrained environments.
基金The authors acknowledge that this work was financially supported by the National Natural Science Foundation of China(Grant No.32171910)the Natural Science Foundation of Shandong Province(Grant No.ZR2020MC085)+1 种基金the Key R&D Project of Shandong Province(Grant No.2019JZZY010734)the Key R&D Project of Zibo City,Shandong Province,China(Grant No.2019ZBXC143).
文摘Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.
基金This work was supported by the PublicWelfare Industry(Agriculture)Research Projects Level-2 under Grant 201503116-04-06Postdoctoral Foundation of Heilongjiang Province under Grant LBHZ15020Harbin Applied Technology Research and Development Program under Grant 2017RAQXJ096 and National Key Application Research and Development Program in China under Grant 2018YFD0300105-2.
文摘Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.
基金supported by the Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, Chinathe earmarked fund for China Agriculture Research System for Grape Industry (CARS-29-zp-6)
文摘The effects of the severity and timing of leaf removal(LR)on the amino acids of Sauvignon Blanc grapes and wines were studied during the 2017 growing season.High-performance liquid chromatography(HPLC)was used to analyze the amino acids profiles of grape berries and wines.The basal leaves were removed at three time points(40,56 and 72 days after flowering,named LR40,LR56 and LR72,respectively)at two severity levels(one at which the first,third,and fifth basal leaves of each shoot were removed(50%level);and another at which the first six basal leaves were removed(100%level)).The results showed that leaf removal had little impact on total soluble solids(°Brix),titratable acidity,pH or berry weight.The LR72-50%treated grapes had higher berry weight,titratable acidity and°Brix than those of the other treatments.The highest concentrations of total amino acids and of total amino acids except proline were detected in LR72-50%treated grapes(2 952.58 and 2 764.36 mg L^-1,respectively);the lowest were detected in LR72-100%treated grapes(2 172.82 and 2 038.71 mg L^-1,respectively).LR72-50%treatment significantly promoted the synthesis of aspartic acid,serine,arginine,alanine,aminobutyric acid and proline at both severity levels for grapes,the concentrations of all of these amino acids were increased relative to the control concentrations.The LR72-50%,LR40-100%and LR72-100%treated wines had higher total amino acids concentrations and higher concentrations of some individual amino acids,such as arginine,alanine and serine,than did the control wines.Of all the amino acids studied,glycine,tyrosine,cysteine,methionine and lysine were not significantly influenced by the timing or severity basal defoliation in grapes and wines.The present study reveals the effects of the timing and severity of leaf removal on the amino acids profiles of grapes and wines.
基金supported by the China Agriculture Research System for Grape Industry(CARS-29-zp-06)
文摘Leaf removal and cluster thinning were carried out prior to veraison to evaluate the effects on berry quality of two Vitis vinifera cultivars（Cabernet Sauvignon and Ugni Blanc） in the Weibei Dryland of China in 2013 and 2014, and comprehensive analysis of the chemical and volatile composition in berries was performed. The results showed that content of reducing sugar in both varieties was not affected while total acid was generally decreased by leaf removal and cluster thinning. The pH of berry juice was correspondingly higher in most treatment groups. Meanwhile, promoting effects on accumulation of total phenols, tannin in both varieties and total anthocyanins in Cabernet Sauvignon were found. As for monomeric anthocyanins, percentage of malvidin and its derivatives was decreased by leaf removal and cluster thinning. Besides, cinnamylated anthocyanins decreased with the intensity of cluster thinning. The accumulation of non-anthocyanin phenolics was similarly affected in the two varieties. Notably, cluster thinning was more effective on enhancing the phenolics content than leaf removal. The combination of middle level of leaf removal and cluster thinning was the most favor to the accumulation of phenolic acids. Furthermore, cluster thinning could also significantly enhance the synthesis of flavanols and stilbenes. Lastly, content and variety of aroma compounds in both grape varieties were also significantly affected by the treatments. The results provided a theoretical basis for a combination of leaf removal and cluster thinning to improve quality of grapes and wines.
文摘The demand of grape in Bangladesh is fulfilled through import from foreign countries. The fruits of local cultivars of grapes are sour and seeded. Development of seedless grape varieties having increased sweetness, higher yield with better nutritional quality is necessary to reduce the import dependency. The present research activities are the part of a grape improvement project. A pot experiment was conducted at the Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh, during June to November 2011 to determine the suitable gamma irradiation doses on growth, leaf area and biochemical characters of grape saplings. Three vegetative bud stages viz. bud initiation stage, 4-leaf stage and 8-leaf stage, and four doses of gamma irradiation viz. 0, 5, 10, and 15 Gy were used as treatments. The experiment was laid out in a Randomized Complete Block Design with four replications. Different irradiation doses and vegetative bud stages showed significant variations in respect of plant growth characters, leaf area, soluble protein and total sugar content. Interaction effects also had significant variations on most of the parameters studied. Higher doses of gamma irradiation had showed detrimental effect on grape saplings. Generally, increased in irradiation doses showed decreased and detrimental effects on most of the parameters under study. Maximum numbers and length of roots, total dry matter, leaf area and chlorophyll-a and chlorophyll-b content were found at 5 Gy irradiation dose. Total soluble protein and sugar content of leaf were found maximum at no irradiation and 15 Gy, respectively. Higher number of roots and length, total dry matter, leaf area, chlorophyll-a, and b and soluble protein content of leaf were observed at bud initiation stage while 8-leaf stage showed maximum total sugar of leaf. In the combined effect of gamma irradiation and vegetative bud stages, all parameters showed best results in 5 Gy with bud initiation stage except total sugar content of leaf.
基金the National Natural Science Foundation of China(30070531,30270918).
文摘An experiment was conducted to investigate the response of salicylic acid as a second messenger to the heat stress in grape plants. For this purpose, all leaves of grape (Vitis vinifera×V. labrussa L. cv. Jingxiu) plants were removed except the 3rd, 4th, 5th, 6th, and 7th ones. The 5th leaf was fed with C-SA, and the 4th and 6th leaves were exposed to high 14 temperature at 40±0.5°C. It was observed that more C-SA transported out from the 5th leaf and the distribution of C-SA 14 14 in each organ of plant altered in response to heat stress. The accumulation of C-SA in both the 4th and 6th leaves being 14 exposed to high temperature was at least three times higher than that in control. The distribution of C-SA in other distal 14 leaves (the 3rd and 7th leaf) decreased, but more C-SA accumulated in stems adjacent to the 4th or 6th leaf exposed to 14 high temperature. In addition, there was more C-SA being transported upwards or downwards while the 4th and 6th 14 leaves were exposed to high temperature respectively. Therefore, our results suggested that SA was closely involved in signal transduction of heat stress in grape plants. However, the ratio of C radioactivity assayed after SA being extracted 14 to that of direct assay with apparatus was more than 70%, which indicated about 30% C was lost or catabolized during 14 transportation.
