The purpose of this thesis is to use drones and machine learning algorithms for automating crack detection in tunnel systems.With the high resolution RGB cameras and LiDAR sensor in drones,you get the imagery and stru...The purpose of this thesis is to use drones and machine learning algorithms for automating crack detection in tunnel systems.With the high resolution RGB cameras and LiDAR sensor in drones,you get the imagery and structural data required to inspect tunnels.The images are then fed through CNNs together with SVMs for detecting and classification cracks in concrete and other surfaces.With this automated mechanism,the process will no longer need manual effort,and the inspection will be more precise and safer.The study shows the efficiency of this hybrid approach,which has 92%detection rate,much better than traditional inspection.And it is also very good at reducing false positives,and produces more trustworthy results.Crack severity is sorted into hairline,medium and deep cracks to make the process of maintenance and repairs easier.According to the results,paired with drones and machine learning,tunnel inspections become more effective,and data collection and analysis greatly enhanced.This method has potential use cases in infrastructure monitoring and could possibly be used for other structural damage detection tasks in high-dimensional domains.展开更多
Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology v...Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops.ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index,the rice blast index(RBI).This RBI was compared with 30 established vegetation indexes,using correlation analysis and visual comparison to further shortlist six superior indexes,including RBI.These were evaluated using the K-nearest neighbor(KNN)and random forests(RF)classification models.RBI demonstrated superior detection accuracy for leaf blast in both the KNN model(95.0% overall accuracy and 93.8% kappa coefficient)and the RF model(95.1%overall accuracy and 92.5% kappa coefficient).This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection,offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.展开更多
With the rapid advancement of modern agriculture,mechanized and intelligent pollination has emerged as a crucial focus for enhancing agricultural efficiency and minimizing labor expenses.Traditional pollination method...With the rapid advancement of modern agriculture,mechanized and intelligent pollination has emerged as a crucial focus for enhancing agricultural efficiency and minimizing labor expenses.Traditional pollination methods,limited by environmental factors and high labor costs,fail to adequately address the production demands of large-scale orchards and vegetable gardens.Consequently,researchers have integrated mechanized equipment,drone technology,robotics,and deep learning algorithms to achieve accurate identification and precise pollination on inflorescences.The research on mechanized and intelligent pollination has not only injected new momentum into the field of fruit and vegetable pollination but also provided key technological support for addressing global agricultural labor shortages and increasing crop yields.This review summarizes recent advances in mechanized and intelligent pollination,focusing on deep learning’s role in object recognition,improvements in pollination equipment,and the effectiveness of intelligent pollination across various fruits or vegetables.Studies indicate that mechanized and intelligent pollination significantly enhances working efficiency and fruit yields,though it continues to face challenges such as technical complexity and high implementation costs.Looking ahead,as robotics and artificial intelligence algorithms continue to advance,mechanized and intelligent pollination is poised for broader adoption in agricultural management practices.This review systematically summarizes the research progress in mechanized and intelligent pollination technologies for fruit and vegetable crops,providing significant theoretical support and reference value for future studies in crop pollination techniques.展开更多
文摘The purpose of this thesis is to use drones and machine learning algorithms for automating crack detection in tunnel systems.With the high resolution RGB cameras and LiDAR sensor in drones,you get the imagery and structural data required to inspect tunnels.The images are then fed through CNNs together with SVMs for detecting and classification cracks in concrete and other surfaces.With this automated mechanism,the process will no longer need manual effort,and the inspection will be more precise and safer.The study shows the efficiency of this hybrid approach,which has 92%detection rate,much better than traditional inspection.And it is also very good at reducing false positives,and produces more trustworthy results.Crack severity is sorted into hairline,medium and deep cracks to make the process of maintenance and repairs easier.According to the results,paired with drones and machine learning,tunnel inspections become more effective,and data collection and analysis greatly enhanced.This method has potential use cases in infrastructure monitoring and could possibly be used for other structural damage detection tasks in high-dimensional domains.
文摘Leaf blast is a significant global problem,severely affecting rice quality and yield,making swift,non-invasive detection crucial for effective field management.This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops.ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index,the rice blast index(RBI).This RBI was compared with 30 established vegetation indexes,using correlation analysis and visual comparison to further shortlist six superior indexes,including RBI.These were evaluated using the K-nearest neighbor(KNN)and random forests(RF)classification models.RBI demonstrated superior detection accuracy for leaf blast in both the KNN model(95.0% overall accuracy and 93.8% kappa coefficient)and the RF model(95.1%overall accuracy and 92.5% kappa coefficient).This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection,offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.
基金supported by the National Natural Science Foundation of China(Grant No.32201680)the China Agriculture Research System of MOF and MARA(Grant No.CARS-28-21)+4 种基金the National Science and Technology Development Program of China(Grant No.NK2022160104)Jiangsu Agricultural Machinery Integrated Program(Grant No.JSYTH01)Nanjing Modern Agricultural Machinery Equipment and Demonstration Technology Innovation Project(Grant No.NJ[2024]01)Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration Extension Fund(Grant No.NJ2024-24)Wuxi Science Technology Development Fund(Grant No.N20221003).
文摘With the rapid advancement of modern agriculture,mechanized and intelligent pollination has emerged as a crucial focus for enhancing agricultural efficiency and minimizing labor expenses.Traditional pollination methods,limited by environmental factors and high labor costs,fail to adequately address the production demands of large-scale orchards and vegetable gardens.Consequently,researchers have integrated mechanized equipment,drone technology,robotics,and deep learning algorithms to achieve accurate identification and precise pollination on inflorescences.The research on mechanized and intelligent pollination has not only injected new momentum into the field of fruit and vegetable pollination but also provided key technological support for addressing global agricultural labor shortages and increasing crop yields.This review summarizes recent advances in mechanized and intelligent pollination,focusing on deep learning’s role in object recognition,improvements in pollination equipment,and the effectiveness of intelligent pollination across various fruits or vegetables.Studies indicate that mechanized and intelligent pollination significantly enhances working efficiency and fruit yields,though it continues to face challenges such as technical complexity and high implementation costs.Looking ahead,as robotics and artificial intelligence algorithms continue to advance,mechanized and intelligent pollination is poised for broader adoption in agricultural management practices.This review systematically summarizes the research progress in mechanized and intelligent pollination technologies for fruit and vegetable crops,providing significant theoretical support and reference value for future studies in crop pollination techniques.
