Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based ...Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.展开更多
Today,leaf trait estimation remains a labor-intensive process.The effort to obtain ground truth measurements limits how accurately this task can be performed automatically.Traditionally,plant scientists manually measu...Today,leaf trait estimation remains a labor-intensive process.The effort to obtain ground truth measurements limits how accurately this task can be performed automatically.Traditionally,plant scientists manually measure the traits of harvested leaves and associate them with sensor data,which is key for training machine learning approaches and to automate the processes.In this paper,we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton,which is automatically extracted.We use the generated leaves to fine-tune different leaf trait estimation methods.We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software.Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves.Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves' traits,making our data crucial for developing and testing data-driven trait estimation methods.Accurate trait estimation is key to understanding crop growth,productivity,and pest resistance,as leaf size directly influences photosynthesis,yield potential,and vulnerability to insects and fungal growth.展开更多
基金funded by Fundamental Research Funds of CAF(CAFYBB2023PA003)Science and Technology Innovation 2030-Major Projects(2023ZD0406103)National Natural Science Foundation of China(32271877).
文摘Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions.However,due to the occlusion between tree crowns,current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands,making parameter extraction challenging such as maximum crown width height(HMCW).This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations.By coupling spatial competition with phenotype parameters,the study identifies the optimal fitting model for HMCW of Chinese fir.The results demonstrate that the constructed spatial structure units align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees.Among the 10,191 trained HMCW models,the Bagging model integrating XGBoost,Random Forest(RF),Support Vector Regression(SVR),Gradient Boosting(GB),and Ridge exhibited the best performance.Compared to the best single model(RF),the Bagging model achieved improved accuracy(R^(2)=0.8346,representing a 1.6%improvement;RMSE=1.4042,reduced by 6.66%;EVS=0.8389;MAE=0.9129;MAPE=0.0508;and MedAE=0.5076,with corresponding improvements of 1.63%,1.49%,0.1%,and 7.06%,respectively).This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters.The refined spatial structure units better link 3D structural phenotypes with environmental factors.This approach aids in canopy morphology simulation and forest management research.
基金This work has partially been funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany's Excellence Strategy,EXC-2070–390732324–PhenoRob.
文摘Today,leaf trait estimation remains a labor-intensive process.The effort to obtain ground truth measurements limits how accurately this task can be performed automatically.Traditionally,plant scientists manually measure the traits of harvested leaves and associate them with sensor data,which is key for training machine learning approaches and to automate the processes.In this paper,we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton,which is automatically extracted.We use the generated leaves to fine-tune different leaf trait estimation methods.We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software.Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves.Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves' traits,making our data crucial for developing and testing data-driven trait estimation methods.Accurate trait estimation is key to understanding crop growth,productivity,and pest resistance,as leaf size directly influences photosynthesis,yield potential,and vulnerability to insects and fungal growth.
