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

Abstract

PurposeIn 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/approachIn 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.FindingsIn 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/valueThe implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

“…Secondly, a stacked combination for multiple disease classification (SEMFD-Net) was proposed, which is an ensemble model by stacking baseline models and using feedforward neural networks as meta-learner, with significantly optimized performance, better than the original level [ 38 ]. Detection of plant diseases with the help of threshold segmentation and random forest classification in the investigation by Kailasam, Swathi et al This work developed a different approach for early stage crops and implemented a new disease finding system with 97.8% recognition accuracy and 99.3% true optimism and achieved a peak signal to noise ratio (PSNR) of 59.823, a structural similarity index measure (SSIM) of 0.99894, a machine squared error (MSE) value of 0.00812, with very good results and achieved some degree of innovation and improvement [ 39 ]. An automated system for plant disease detection using machine learning methods has been proposed by previous authors.…”

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

“…Another was that bodily motions were more readily concealed than facial expressions when people were in the midst of an emotional state. Other studies on deceit, which Darwin did not anticipate, is also included in the study [29]. micro facial expressions in picture sequences have made use of a broad range of face models [30].…”

Effect of chaos factor in radiation pattern in planner antenna arrays with chaos adaptive invasive weed optimization

“…The technology was able to cut the time it took for technicians to call and report on maintenance work by half [24]. Online and real-time maintenance history data will aid in the development of a new maintenance plan [25]. Saikumar et al [26] large concentric circular antenna arrays (CCAA) with high sidelobe levels may be reduced using a new hybrid CNN technique.…”