Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is availabl...Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.展开更多
This study explores the novel application of Triumfetta pentandra(TP,“Nkui”)fibers,a tropical plant that is abundant yet underutilized in civil engineering,to enhance the performance of compressed earth bricks(CEBs)...This study explores the novel application of Triumfetta pentandra(TP,“Nkui”)fibers,a tropical plant that is abundant yet underutilized in civil engineering,to enhance the performance of compressed earth bricks(CEBs).The main objective is to assess how incorporating these vegetal fibers can improve the mechanical properties of CEBs while maintaining durability.TP fibers were extracted,characterized,and integrated into the soil used for brick specimens.A rigorous experimental protocol was implemented,featuring a unique fiber pre-treatment,the use of a single,homogeneous clayey soil type,and controlled 28-day curing under standard humidity and temperature,which distinguishes this study from previous works.Physical measurements(moisture content,bulk density,water absorption)and mechanical tests(fiber tensile strength,compressive and flexural strength of CEBs)were conducted following French standards.The results indicate that 4%TP fiber content yields optimal mechanical performance,with compressive strength reaching 6.61 MPa and flexural strength 1.49 MPa at 28 days,compared to 5.16 MPa and 0.51 MPa for unreinforced samples.This demonstrates the potential of TP fibers to reinforce earth-based materials,providing a sustainable,locally sourced,and cost-effective construction solution.However,higher fiber content increases porosity and capillary water absorption(up to 16.75 g at 6%fibers),highlighting the importance of optimized fiber dosing and potential complementary treatments for long-term durability.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.
文摘This study explores the novel application of Triumfetta pentandra(TP,“Nkui”)fibers,a tropical plant that is abundant yet underutilized in civil engineering,to enhance the performance of compressed earth bricks(CEBs).The main objective is to assess how incorporating these vegetal fibers can improve the mechanical properties of CEBs while maintaining durability.TP fibers were extracted,characterized,and integrated into the soil used for brick specimens.A rigorous experimental protocol was implemented,featuring a unique fiber pre-treatment,the use of a single,homogeneous clayey soil type,and controlled 28-day curing under standard humidity and temperature,which distinguishes this study from previous works.Physical measurements(moisture content,bulk density,water absorption)and mechanical tests(fiber tensile strength,compressive and flexural strength of CEBs)were conducted following French standards.The results indicate that 4%TP fiber content yields optimal mechanical performance,with compressive strength reaching 6.61 MPa and flexural strength 1.49 MPa at 28 days,compared to 5.16 MPa and 0.51 MPa for unreinforced samples.This demonstrates the potential of TP fibers to reinforce earth-based materials,providing a sustainable,locally sourced,and cost-effective construction solution.However,higher fiber content increases porosity and capillary water absorption(up to 16.75 g at 6%fibers),highlighting the importance of optimized fiber dosing and potential complementary treatments for long-term durability.
