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Khan Afsar

Bio: Khan Afsar is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Mobile robot & Population. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Journal ArticleDOI
01 Jun 2021
TL;DR: An automated irrigation system has been developed which irrigates the field in acres and a solar-powered robot is attached with various sensors and with a highresolution camera that tests crop conditions and senses the soil state.
Abstract: Water plays a significant role among other existing natural resources. The daily demand for water supplies is increasingly on the rise as the population grows. To minimize the consumption of water in irrigation, several proposals were suggested. The currently existing system known as the automated irrigation system for effective water resource use with the prediction of the weather (AISWP) functions with a single farm that lacks the reliability in the precision of weather forecasting. So, a robot-based irrigation system has been proposed to improve the performance of the system. To minimize the water usage for crops, an automated irrigation system has been developed which irrigates the field in acres. An additional characteristic of the system has also been given for the soil pH measurement to allow the use of fertilizers accordingly. The solar-powered robot is managed wirelessly by a designated application. The robot is attached with various sensors and with a high-resolution camera that tests crop conditions and senses the soil state. The application has been created to provide information about the soil’s condition such as temperature level, humidity level, water level, and level of nutrients to the PC/Laptop with the real-time values via the GSM module.

7 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , the authors examined the factors influencing the willingness of Bangladeshi farmers to adopt and pay for the Internet of Things in the agricultural sector by applying the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2).
Abstract: This paper aims to examine the factors influencing the willingness of Bangladeshi farmers to adopt and pay for the Internet of Things (IoT) in the agricultural sector by applying the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). To this end, the study employed a quantitative research methodology and obtained data from 345 farmers from the northern districts of Bangladesh. Using a cross-sectional survey design and convenience sampling method, a study of premium fruit growers was undertaken to assess IoT use in agriculture, and the primary survey data were analyzed using the Structural Equation Modeling (SEM) approach via AMOS 26. The study confirmed that effort expectancy, performance expectancy, facilitating condition, hedonic motivation, government support, price value, personal innovativeness, and trust influence the willingness of Bangladeshi farmers to adopt the IoT. Additionally, predictors such as trust and willingness to adopt were observed to influence the willingness to pay for the IoT, while the construct ‘performance expectancy’ produced no effect. The study also revealed that the willingness to adopt moderates the association between performance expectancy, price value, and willingness to pay for the IoT. This research has novel implications because it investigates the behavior of rural customers with respect to innovation adoption, which in this case is the IoT in agriculture. It outlines precise reasons for the willing adoption of the IoT in agriculture, which will, in turn, assist marketers of IoT technology in the design of appropriate marketing strategies to increase acceptance in rural areas. Using the proposed model that incorporates farmers’ willingness to pay, this empirical study takes the first step in examining whether farmers in a developing economy such as Bangladesh will adopt and pay for the IoT.

15 citations

Journal ArticleDOI
TL;DR: In this paper , the authors present an overview of current design trends in construction, current development technology for controlling and monitoring greenhouse microclimates, and the various systems available for managing greenhouse environments.

10 citations

Journal ArticleDOI
01 Jan 2022
TL;DR: Machine learning models are developed to obtain improved accuracy, namely Back Propagation Neural Network (BPNN), Support Vector Machine, and General Regression Neural Networks with the given data set, which show that GRNN has greater accuracy than Multi Linear Regression with a normalized mean square error of 0.03.
Abstract: : The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields of 28 districts in wide spectrum of Tamilnadu over a period of 18 years. The Statistical model Multi Linear Regression was used as a benchmark for crop yield prediction, which yielded an accuracy of 82% owing to its wide ranging input data. Therefore, machine learning models are developed to obtain improved accuracy, namely Back Propagation Neural Network (BPNN), Support Vector Machine, and General Regression Neural Networks with the given data set. Results show that GRNN has greater accuracy of 97% (R 2 = 0.97) with a normalized mean square error (NMSE) of 0.03. Hence GRNN can be used for crop yield prediction in diversified geographical fields.

8 citations

Proceedings ArticleDOI
06 Oct 2022
TL;DR: A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included in this article , where data from modules is stored in a separate database using an innovative IoT-based front end.
Abstract: Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology.