Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield...Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.展开更多
A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure ...A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.展开更多
随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。...随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。设计一套完整的光伏电站智能监控系统,通过对光伏发电区、变配电设备区、电站运维区等关键场景实施布控,利用边缘智能硬件装置采集光伏组件、逆变器、并网柜、储能柜及变配电柜等关键设备及各气象元素数据,通过光伏电站智能监控软件平台数据分析、实时告警、智能诊断、远程巡检等功能实时感知光伏电站发电营收及设备运行情况,有效提升光伏电站发电效率,减少电站本地运维人员成本开销。展开更多
文摘Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.
基金This work was financially supported by MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2022-RS-2022-00156354)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program(Project No.P0016038).
文摘A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.
文摘随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。设计一套完整的光伏电站智能监控系统,通过对光伏发电区、变配电设备区、电站运维区等关键场景实施布控,利用边缘智能硬件装置采集光伏组件、逆变器、并网柜、储能柜及变配电柜等关键设备及各气象元素数据,通过光伏电站智能监控软件平台数据分析、实时告警、智能诊断、远程巡检等功能实时感知光伏电站发电营收及设备运行情况,有效提升光伏电站发电效率,减少电站本地运维人员成本开销。
