Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason i...Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.展开更多
Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Dop...Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.展开更多
文摘Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.
基金supported in part by the National Natural Foundation of China(No.62027801).
文摘Recently,researchers have proposed an emitter localization method based on passive synthetic aperture.However,the unknown residual frequency offset(RFO)between the transmit-ter and the receiver causes the received Doppler signal to shift,which affects the localization accu-racy.To solve this issue,this paper proposes a RFO estimation method based on range migration fitting.Due to the high frequency modulation slope of the linear frequency modulation(LFM)-mod-ulation radar signal,it is not affected by RFO in range compression.Therefore,the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then,the matched filters are designed under different RFOs.When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time,the given RFO is the real RFO between the transceivers.The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s,the pulse repe-tition frequency(PRF)of the transmitter radar signal is not less than 1 kHz,the range detection is not larger than 1000 km,and the signal noise ratio(SNR)is not less than-5 dB.