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An IoT-based agriculture maintenance using pervasive computing with machine learning technique

International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 201-214页

作者： Swathi Kailasam sampath dakshina murthy achanta P.Rama Koteswara Rao Ramesh Vatambeti Saikumar Kayam CSE Koneru Lakshmaiah Education FoundationGunturIndia ECE Vignan’s Institute of Information TechnologyVisakhapatnamIndia ECE NRI Institute of TechnologyKrishnaIndia CHRIST(Deemed to be University) BengaluruIndia ECE Koneru Lakshmaiah Education FoundationGunturIndia

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 *** significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization *** this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and *** 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 ***/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is *** proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less *** now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced ***,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this *** implemented design is verified on Python 3.7.8 software for simulation ***-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 *** of this,a disease-finding system has been *** 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 ***/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.

关键词： Crop harvesting Detection of plant disease RFO Classification Threshold segmentation

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