Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a...Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.展开更多
Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has a...Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has attracted considerable attention from manufacturers and researchers.The objective of this research was to design a UAAS equippingwith a data acquisition system,to characterize its chemical application performance based on droplet deposition data and optimize the operating parameters.Each test was repeated three times to assess the reliability of the spraying system.Various flight parameters were also evaluated.The optimal spray pressure for the XR8001 and XR8002(TeeJet,Wheaton,IL,USA)nozzles was found to be 300 kPa,and the latter nozzle had a higher droplet deposition rate and spray volume.Spray volume was not significantly affected by the flight speed or droplet density and was negatively correlated with the nozzle pressure.The results of this study provide a basis for improving the efficiency of UAAS chemicalapplication systems in terms of large-scale application.展开更多
Tomato disease control is an urgent requirement in the field of intellectual agriculture,and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases.Some diseased areas on...Tomato disease control is an urgent requirement in the field of intellectual agriculture,and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases.Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation.Blurred edge also makes the segmentation accuracy poor.Based on UNet,we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module(MC-UNet).First,a Multi-scale Convolution Module is proposed.This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes,and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module.Second,a Cross-layer Attention Fusion Mechanism is proposed.This mechanism highlights tomato leaf disease locations via gating structure and fusion operation.Then,we employ SoftPool rather than MaxPool to retain valid information on tomato leaves.Finally,we use the SeLU function appropriately to avoid network neuron dropout.We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32%accuracy and 6.67M parameters.Our method achieves good results for tomato leaf disease segmentation,which demonstrates the effectiveness of the proposed methods.展开更多
Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease...Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .展开更多
基金supported by the Changsha Municipal Natural Science Foundation,China(kq2014160)in part by the Key Projects of Department of Education of Hunan Province,China(21A0179)+1 种基金the Hunan Key Laboratory of Intelligent Logistics Technology,China(2019TP1015)the National Natural Science Foundation of China(61902436)。
文摘Banana is a significant crop,and three banana leaf diseases,including Sigatoka,Cordana and Pestalotiopsis,have the potential to have a serious impact on banana production.Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases.Therefore,this paper proposes a novel method to identify banana leaf diseases.First,a new algorithm called K-scale VisuShrink algorithm(KVA)is proposed to denoise banana leaf images.The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds,the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image.Then,this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net(GR-ARNet)based on Resnet50.In this,the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed;the ResNeSt Module adjusts the weight of each channel,increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification;the model's computational speed is increased using the hybrid activation function of RReLU and Swish.Our model achieves an average accuracy of 96.98%and a precision of 89.31%applied to 13,021 images,demonstrating that the proposed method can effectively identify banana leaf diseases.
基金This work was partially financially supported by the National Key Research and Development Program of China(Grant No.2016YFD0200701).
文摘Unmanned agricultural aircraft system(UAAS)hasbeen widely employed as a low-cost and reliable method to apply agrochemicals to small agricultural fields in China.The performance of battery-poweredmultirotor UAAS has attracted considerable attention from manufacturers and researchers.The objective of this research was to design a UAAS equippingwith a data acquisition system,to characterize its chemical application performance based on droplet deposition data and optimize the operating parameters.Each test was repeated three times to assess the reliability of the spraying system.Various flight parameters were also evaluated.The optimal spray pressure for the XR8001 and XR8002(TeeJet,Wheaton,IL,USA)nozzles was found to be 300 kPa,and the latter nozzle had a higher droplet deposition rate and spray volume.Spray volume was not significantly affected by the flight speed or droplet density and was negatively correlated with the nozzle pressure.The results of this study provide a basis for improving the efficiency of UAAS chemicalapplication systems in terms of large-scale application.
基金supported by the Scientific Research Project of Education Department of Hunan Province(Grant No.21A0179)in part by the Changsha Municipal Natural Science Foundation(Grant No.kq2014160)+2 种基金in part by the National Natural Science Fund project(Grant No.62276276)in part by the Natural Science Foundation of China(Grant No.61902436)in part by Hunan Key Laboratory of Intelligent Logistics Technology(2019TP1015).
文摘Tomato disease control is an urgent requirement in the field of intellectual agriculture,and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases.Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation.Blurred edge also makes the segmentation accuracy poor.Based on UNet,we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module(MC-UNet).First,a Multi-scale Convolution Module is proposed.This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes,and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module.Second,a Cross-layer Attention Fusion Mechanism is proposed.This mechanism highlights tomato leaf disease locations via gating structure and fusion operation.Then,we employ SoftPool rather than MaxPool to retain valid information on tomato leaves.Finally,we use the SeLU function appropriately to avoid network neuron dropout.We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32%accuracy and 6.67M parameters.Our method achieves good results for tomato leaf disease segmentation,which demonstrates the effectiveness of the proposed methods.
基金supported by the Changsha Municipal Natural Science Foundation(grant no.kq2014160)in part by the National Natural Science Foundation in China(grant no.61703441)+2 种基金in part by the Key Projects of the Department of Education,Hunan Province(grant no.19A511)in part by the Hunan Key Laboratory of Intelligent Logistics Technology(grant no.2019TP1015)in part by the National Natural Science Foundation of China(grant no.61902436).
文摘Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .
