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Chirag Sohaliya

Bio: Chirag Sohaliya is an academic researcher. The author has contributed to research in topics: Irrigation management & Wireless sensor network. The author has an hindex of 1, co-authored 1 publications receiving 12 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2011
TL;DR: WSN based expert system is proposed to provide optimized water supply for various crops in the field using on field soil moisture and temperature data to understand the actual need of water.
Abstract: Increased demand of farm product due to population growth and limited resources of irrigation water has made the field irrigation management system as an important element of agricultural activity. Wireless Sensor Network (WSN) is the most preferred platform due to its low cost, small size, low power consumption, reduced maintenance, great flexibility, portability and scalability features. WSN based expert system is proposed to provide optimized water supply for various crops in the field. It uses on field soil moisture and temperature data to understand the actual need of water.

12 citations


Cited by
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Proceedings ArticleDOI
20 May 2016
TL;DR: The paper provides efficient automated farm monitoring and irrigation techniques which incorporate wide range of sensors to remotely sense and monitor various parameters of the soil like temperature, moisture, fertility and regulate the supply of water and fertilizer to the land based on the requirement.
Abstract: Agriculture requires the dedication of many natural resources, including land, water, and energy. The quality and quantity of these natural resources has degraded over the years due to various economic problems associated with increased cost of inputs, decreased farm incomes, ever declining land, labour, energy resources and also ecological problems such as soil, water pollution and soil erosion, putting the viability of future agricultural operations at risk. The remedy to this is to adopt sustainable agriculture which supports careful management and cultivation of crops involving less use of fertilizer, pesticides, calculated use of precious natural resources like energy, water through controlled irrigation and fertigation practices with the help of green sensor technology and electronic control systems. The paper provides efficient automated farm monitoring and irrigation techniques which incorporate wide range of sensors to remotely sense and monitor various parameters of the soil like temperature, moisture, fertility and regulate the supply of water and fertilizer to the land based on the requirement. An algorithm formulated with the threshold values of sensor outputs is used to code the microcontroller which performs the required actions by employing relays until the strayed-out parameter has been brought back to its optimum level. The cloud based user friendly interface facilitates real-time data logging of environmental parameters while also supporting analysis of past statistics for future growth by means of a web-based customizable application. Furthermore, the project aims to optimize the use of land and labour, conserve water, increase crop yield, avoid wastage of energy and provide maximum automation and benefit the society by adopting smart environment friendly technology to implement newer and sustainable ways of agriculture.

32 citations

Proceedings ArticleDOI
01 Oct 2017
TL;DR: The experimental results show that by using the SIDS system, the amount of irrigation time is precisely calculated based on the measured agricultural parameters and the water irrigation utilization is improved.
Abstract: Smart Irrigation Decision Support (SIDS) system based on fuzzy logic using Wireless Sensor Network (WSN) is considered for precision water irrigation. WSN consists of limited-energy sensor nodes which are equipped with sensing, wireless communication and processing capabilities. SIDS aims to measure the agricultural parameters including the soil temperature and soil moisture using the sensor nodes. The rate of soil moisture reduction is calculated from the current soil moisture reading and the previous one. These soil temperate and the rate of soil moisture reduction are employed as input parameters for fuzzy logic controller to produce the amount of irrigation time as output parameter. The fuzzy logic rules and linguistic values for the input and output parameters of fuzzy logic are carefully selected with the guide of agricultural experts including the farmers. The experimental results show that by using the SIDS system, the amount of irrigation time is precisely calculated based on the measured agricultural parameters. In addition, the water irrigation utilization is improved.

24 citations

Journal ArticleDOI
TL;DR: Simulation results show that the proposed SHPA scheme eliminates the noise associated with the measurements, improves the network lifetime and sensing accuracy, and enhances the crop yields.
Abstract: Smart heterogeneous precision agriculture (SHPA) using a wireless sensor network is introduced for limited energy sensor nodes, deployed in an agricultural farm which is divided into a number of heterogeneous agricultural areas. At each time step, the extended Kalman filter is adopted to measure and to predict the agricultural parameters including the soil moisture and temperature so that the noise associated with noisy measurements is filtered. After that, sensor node selection algorithm proactively selects the sensor nodes for each individual area to sense the agricultural parameters. Thus, the network lifetime and sensing accuracy are improved. The sampling interval for each crop is predefined based on the crop types and agricultural requirements so that the crop yields are improved. Also, the design, framework, algorithms, and architecture of SHPA are considered and proposed. Compared with other schemes, simulation results show that the proposed SHPA scheme eliminates the noise associated with the measurements, improves the network lifetime and sensing accuracy, and enhances the crop yields.

23 citations

Journal ArticleDOI
TL;DR: In this paper, an optimally heterogeneous irrigation (OHI) system using a wireless sensor network is developed for agricultural water irrigation, whereby a region such as a field, is divided into a number of heterogeneous agricultural areas.
Abstract: An optimally heterogeneous irrigation (OHI) system using a wireless sensor network is developed for agricultural water irrigation, whereby a region, such as a field, is divided into a number of heterogeneous agricultural areas. An extended Kalman filter (EKF) is adopted to filter the system state including the sensed soil moisture and temperature from the associated noisy measurements. EKF is also used to predict the next system states. After that, the suboptimal irrigation water amount for each crop is proactively computed according to the crop requirements, the predicted system state and the soil conditions so that water irrigation utilization and crop yields are improved subject to the overall residual water available below a predefined level. A random bit climbing optimization method is employed to determine the suboptimal water amount for each area so that the proposed objectives are satisfied. Simulation results are compared with other traditional irrigation schemes and show that the proposed OHI approach improves water utilization, meets the heterogeneous crop requirements, copes with different soil types, prioritises crop classes and in turn enhances crop yields.

11 citations

Proceedings ArticleDOI
01 Nov 2013
TL;DR: This work considers tracking of multiple objects using a wireless sensor network where distributed nodes transmit to a fusion center using random access, using a gradient algorithm to solve the underlying non-linear optimization problem.
Abstract: We consider tracking of multiple objects using a wireless sensor network where distributed nodes transmit to a fusion center using random access. During an initialization phase, targets are identified on a discrete set of locations using a sparse identification method. Tracking then proceeds to update the target locations and amplitudes explicitly, using a gradient algorithm to solve the underlying non-linear optimization problem. Updating continues at the pace dictated by the average sensing/transmission rate, which can be adjusted to suit an expected target velocity. By focusing explicitly on the target locations, as opposed to continuing with sparse identification over a quantized space whose size may be much greater than the number of targets, the goal is to reduce the computational complexity, improve the performance, and eliminate the spatial quantization effects.

5 citations