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Book part
Publication date: 30 September 2020

Rashbir Singh, Prateek Singh and Latika Kharb

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything…

Abstract

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Content available
Book part
Publication date: 30 September 2020

Abstract

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Article
Publication date: 19 November 2021

Swathi Kailasam, Sampath Dakshina Murthy Achanta, P. Rama Koteswara Rao, Ramesh Vatambeti and Saikumar Kayam

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…

Abstract

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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