With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,s...With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.展开更多
The importance of food security,especially in combating the problem of acute hunger,has been underscored as a key component of sustainable development.Considering the major challenge of rapidly increasing demands for ...The importance of food security,especially in combating the problem of acute hunger,has been underscored as a key component of sustainable development.Considering the major challenge of rapidly increasing demands for both food security and safety,the management and control of major pests is urged to secure supplies of major agricultural products.However,owing to global climate change,biological invasion(e.g.,fall armyworm),decreasing agricultural biodiversity,and other factors,a wide range of crop pest outbreaks are becoming more frequent and serious,making China,one of the world’s largest country in terms of agricultural production,one of the primary victims of crop yield loss and the largest pesticide consumer in the world.Nevertheless,the use of science and technology in monitoring and early warning of major crop pests provides better pest management and acts as a fundamental part of an integrated plant protection strategy to achieve the goal of sustainable development of agriculture.This review summarizes the most fundamental information on pest monitoring and early warning in China by documenting the developmental history of research and application,Chinese laws and regulations related to plant protection,and the National Monitoring and Early Warning System,with the purpose of presenting the Chinese model as an example of how to promote regional management of crop pests,especially of cross border pests such as fall armyworm and locust,by international cooperation across pest-related countries.展开更多
Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribu...Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.展开更多
Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types,as well as guarantee food quality and limited use of pesticides.We aim at extending traditional monitor...Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types,as well as guarantee food quality and limited use of pesticides.We aim at extending traditional monitoring by means of traps,by involving the general public in reporting the presence of insects by using smartphones.This includes the largely unexplored problem of detecting insects in images that are taken in noncontrolled conditions.Furthermore,pest insects are,in many cases,extremely similar to other species that are harmless.Therefore,computer vision algorithms must not be fooled by these similar insects,not to raise unmotivated alarms.In this work,we study the capabilities of state-of-the-art(SoA)object detection models based on convolutional neural networks(CNN)for the task of detecting beetle-like pest insects on nonhomogeneous images taken outdoors by different sources.Moreover,we focus on disambiguating a pest insect from similar harmless species.We consider not only detection performance of different models,but also required computational resources.This study aims at providing a baseline model for this kind of tasks.Our results show the suitability of current SoA models for this application,highlighting how FasterRCNN with a MobileNetV3 backbone is a particularly good starting point for accuracy and inference execution latency.This combination provided a mean average precision score of 92.66%that can be considered qualitatively at least as good as the score obtained by other authors that adopted more specific models.展开更多
Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabili...Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm （Hyphantria cunea Drury）. We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements （Willmott＇s index of agreement 〉0.85）. Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.展开更多
Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research co...Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.展开更多
文摘With the increasing global population and mounting pressures on agricultural production,precise pest monitoring has become a critical factor in ensuring food security.Traditional monitoring methods,often inefficient,struggle to meet the demands of modern agriculture.Drone remote sensing technology,leveraging its high efficiency and flexibility,demonstrates significant potential in pest monitoring.Equipped with multispectral,hyperspectral,and thermal infrared sensors,drones can rapidly cover large agricultural fields,capturing high-resolution imagery and data to detect spectral variations in crops.This enables effective differentiation between healthy and infested plants,facilitating early pest identification and targeted control.This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases.It also discusses existing challenges,aiming to provide insights and references for future research.
基金This study was supported by the National Natural Science Foundation of China(31727901 and 31901873).
文摘The importance of food security,especially in combating the problem of acute hunger,has been underscored as a key component of sustainable development.Considering the major challenge of rapidly increasing demands for both food security and safety,the management and control of major pests is urged to secure supplies of major agricultural products.However,owing to global climate change,biological invasion(e.g.,fall armyworm),decreasing agricultural biodiversity,and other factors,a wide range of crop pest outbreaks are becoming more frequent and serious,making China,one of the world’s largest country in terms of agricultural production,one of the primary victims of crop yield loss and the largest pesticide consumer in the world.Nevertheless,the use of science and technology in monitoring and early warning of major crop pests provides better pest management and acts as a fundamental part of an integrated plant protection strategy to achieve the goal of sustainable development of agriculture.This review summarizes the most fundamental information on pest monitoring and early warning in China by documenting the developmental history of research and application,Chinese laws and regulations related to plant protection,and the National Monitoring and Early Warning System,with the purpose of presenting the Chinese model as an example of how to promote regional management of crop pests,especially of cross border pests such as fall armyworm and locust,by international cooperation across pest-related countries.
基金Under the auspices of National Natural Science Foundation of China(No.42171407,42077242)Key Program of National Natural Science Foundation of China(No.42330607)。
文摘Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.
基金partly supported and funded by the Hasler Foundation under the Project“ARPI:Automated Recognition of Pest Insect Images”(20028)。
文摘Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types,as well as guarantee food quality and limited use of pesticides.We aim at extending traditional monitoring by means of traps,by involving the general public in reporting the presence of insects by using smartphones.This includes the largely unexplored problem of detecting insects in images that are taken in noncontrolled conditions.Furthermore,pest insects are,in many cases,extremely similar to other species that are harmless.Therefore,computer vision algorithms must not be fooled by these similar insects,not to raise unmotivated alarms.In this work,we study the capabilities of state-of-the-art(SoA)object detection models based on convolutional neural networks(CNN)for the task of detecting beetle-like pest insects on nonhomogeneous images taken outdoors by different sources.Moreover,we focus on disambiguating a pest insect from similar harmless species.We consider not only detection performance of different models,but also required computational resources.This study aims at providing a baseline model for this kind of tasks.Our results show the suitability of current SoA models for this application,highlighting how FasterRCNN with a MobileNetV3 backbone is a particularly good starting point for accuracy and inference execution latency.This combination provided a mean average precision score of 92.66%that can be considered qualitatively at least as good as the score obtained by other authors that adopted more specific models.
基金supported by National Science and Technology Major Projects of China(21-Y30B05-9001-13/15)
文摘Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm （Hyphantria cunea Drury）. We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements （Willmott＇s index of agreement 〉0.85）. Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.
基金funded by the Zhejiang Agricultural Cooperative and Extensive Project of Key Technology(2020XTTGCY04-02,2020XTTGCY01-05)the Major Special Project for 2025 Scientific and Technological Innovation(Major Scientific and Technological Task Project in Ningbo City)(2021Z048).
文摘Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.
