首页|Study Findings on Robotics Are Outlined in Reports from China Agricultural University (Leaf-density Estimation for Fruit-tree Canopy Based On Wind-excited Audio)
Study Findings on Robotics Are Outlined in Reports from China Agricultural University (Leaf-density Estimation for Fruit-tree Canopy Based On Wind-excited Audio)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - A new study on Robotics is now availab le. According to news reporting out of Beijing,People’s Republic of China, by N ewsRx editors, research stated, “It is important to obtain real-timeleaf densit y of fruit-tree canopies for the precision spray control of plant-protection rob ots. However,conventional detection techniques for the characteristics of fruit -tree canopies cannot acquire the canopyinternal information, which may provide an unsatisfactory accuracy of detection of leaf densities.”Funders for this research include National Natural Science Foundation of China ( NSFC), Yantai Localityand University Cooperation Development Project.Our news journalists obtained a quote from the research from China Agricultural University, “Thispaper proposes a method for estimating canopy leaf density of fruit trees based on wind-excited audio. Awind-exciting implement was used to f orce fruit-tree canopy leaves vibrating to produce audio. Then, somecorrelation analysis methods were used to extract key characteristic parameters of wind-exc ited audio thatwere significantly correlated with leaf density. Finally, based on the data set of wind-excited audio, a fewmachine-learning methods were used to develop leaf-density estimation models. Test results showed that:(1) there w ere five key feature parameters of wind-excited audio that were significantly co rrelated withleaf density: the short-time energy, spectral centroid, the freque ncy average energy, the peak frequency,and the standard deviation of frequency. (2) the estimation model of leaf density developed based onbackpropagation neu ral network for fruit-tree canopy showed the optimal estimation results, which c anachieve the estimation of leaf density of fruit-tree canopies accurately. The overall correlation coefficient® of the estimation model was more than 0.84, t he root-mean-square error was less than 0.73 m2 m-3,and the mean absolute error was less than 0.53 m2 m-3.”
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRoboticsChina Agricultural University
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