Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agri...Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.展开更多
The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was de...The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.展开更多
Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform tradit...Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.展开更多
With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning ...With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.展开更多
随着“双碳”目标的提出,可再生能源的发展和利用进入了高质量跃升发展的新阶段。风能作为一种清洁且丰富的能源形式,具有分布广泛、无污染、可再生等优势,逐渐受到广泛关注。然而随着信息化与工业化深度融合,风电场SCADA(Supervisory C...随着“双碳”目标的提出,可再生能源的发展和利用进入了高质量跃升发展的新阶段。风能作为一种清洁且丰富的能源形式,具有分布广泛、无污染、可再生等优势,逐渐受到广泛关注。然而随着信息化与工业化深度融合,风电场SCADA(Supervisory Control And Data Acquisition,数据采集与监视控制系统)系统的信息化程度不断提高,网络安全问题日益突出。为了应对这些问题,机器学习以其强大的数据处理和模式识别能力,成为一种有效的入侵检测方法,可有效检测异常行为,预防潜在网络安全威胁。其中支持向量机(SVM)、卷积神经网络(CNN)、循环神经网络(RNN)以及单类支持向量机(OCSVM)等模型可应用于风电场SCADA系统入侵检测,提高风电场SCADA系统的网络安全性,保障系统稳定运行。本文首先概述了机器学习算法,随后探讨了风电场SCADA系统面临的网络安全风险,并介绍了机器学习在入侵检测方面的应用价值及适用场景,以期为机器学习在风电场SCADA系统入侵检测的应用研究方向提供参考借鉴。展开更多
One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machin...One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machine learning,which present difficulties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another significant dif-ficulty is assessing germination quality.Additionally,the majority of these contri-butions make use of benchmark classification methods that are either inept or too complex to train with the supplied features.This manuscript addressed these issues by introducing a novel ensemble classification strategy dubbed“Assessing Germination Quality of Seed Samples(AGQSS)by Adaptive Boosting Ensemble Classification”that learns from quantitative phase features as well as universal features in greyscale spectroscopic images.The experimental inquiry illustrates the significance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.展开更多
基金funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.
文摘The aim of this article is to assist farmers in making better crop selection decisions based on soil fertility and weather forecast through the use of IoT and AI (smart farming). To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast. Random Forest machine learning algorithm was used and trained with Jupyter in the Anaconda framework to achieve an accuracy of about 99%. Based on this process, IoT with the Message Queuing Telemetry Transport (MQTT) protocol, a machine learning algorithm, based on Random Forest, and weather forecast API for crop prediction and recommendations were used. The prototype accepts nitrogen, phosphorus, potassium, humidity, temperature and pH as input parameters from the IoT sensors, as well as the weather API for data forecasting. The approach was tested in a suburban area of Yaounde (Cameroon). Taking into account future meteorological parameters (rainfall, wind and temperature) in this project produced better recommendations and therefore better crop selection. All necessary results can be accessed from anywhere and at any time using the IoT system via a web browser.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/209/42).
文摘Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.
基金supported by the National Science Foundation(NSF)CBET,Fluid Dynamics CAREER program(Grant No.2046160),program manager Ron Joslin.
文摘With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.
文摘随着“双碳”目标的提出,可再生能源的发展和利用进入了高质量跃升发展的新阶段。风能作为一种清洁且丰富的能源形式,具有分布广泛、无污染、可再生等优势,逐渐受到广泛关注。然而随着信息化与工业化深度融合,风电场SCADA(Supervisory Control And Data Acquisition,数据采集与监视控制系统)系统的信息化程度不断提高,网络安全问题日益突出。为了应对这些问题,机器学习以其强大的数据处理和模式识别能力,成为一种有效的入侵检测方法,可有效检测异常行为,预防潜在网络安全威胁。其中支持向量机(SVM)、卷积神经网络(CNN)、循环神经网络(RNN)以及单类支持向量机(OCSVM)等模型可应用于风电场SCADA系统入侵检测,提高风电场SCADA系统的网络安全性,保障系统稳定运行。本文首先概述了机器学习算法,随后探讨了风电场SCADA系统面临的网络安全风险,并介绍了机器学习在入侵检测方面的应用价值及适用场景,以期为机器学习在风电场SCADA系统入侵检测的应用研究方向提供参考借鉴。
文摘One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machine learning,which present difficulties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another significant dif-ficulty is assessing germination quality.Additionally,the majority of these contri-butions make use of benchmark classification methods that are either inept or too complex to train with the supplied features.This manuscript addressed these issues by introducing a novel ensemble classification strategy dubbed“Assessing Germination Quality of Seed Samples(AGQSS)by Adaptive Boosting Ensemble Classification”that learns from quantitative phase features as well as universal features in greyscale spectroscopic images.The experimental inquiry illustrates the significance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.
