Unraveling the Drivers and Barriers of Wireless Sensor Networks

(WSNs) Uptake Amongst Grape Growers in Maharashtra

Bhuvaneshwari T H

and Akshay S Deshmukh

Agricultural Development and Rural Transformation Centre (ADRTC),

Institute for Social and Economic Change (ISEC), Bengaluru, Karnataka, India

Keywords:

Wireless Sensor Networks, Adoption Factors.

Abstract: Globally, 70 per cent of water is used for irrigation, and there is a huge gap in fresh water and irrigation

requirements to feed the growing world population owing to resource constraints. In India, 80 per cent of

water is used for irrigation purposes owing to the irregulated consumption posed by drought situations. The

method of irrigation in the current scenario does not account for weather forecasts, soil moisture in the root

zone, evapotranspiration, plant growth stage, and crop coefficient. Irregulated irrigation can lead to plant

water stress, which, in turn, leads to slow growth and low productivity. Therefore, sustainable water use is

necessary in India. Precision farming is a solution to most agricultural problems faced by India, although the

adoption of this technology is nascent in India. Understanding the benefits and adoption behavior of precision

technology, such as WSNs, requires much attention for broader adoption. Most existing literature has focused

on developed nations, which may not be suitable for developing nations. This study investigates the factors

responsible for WSN uptake and the level of adoption among grape farmers in Maharashtra State, India.

Cross-sectional data were collected via a survey using a multistage sampling framework. Water saving and

crop dynamics also emerged as significant factors for the level of adoption. However, high costs, fragmented

land holdings, institutional issues, and gender inclusivity are barriers to adoption. The findings emphasize

support from the government and design technologies with reasonable cost and durability to lower service

charges. Overall cost, service charge, farm size, and institutional support are necessary for the diffusion of

technology in developing nations, providing relevant insights for policymakers, service providers, and

developers.

1 INTRODUCTION

Globally, 70 per cent of water is used for irrigation,

and there is a huge gap in fresh water and irrigation

requirements to feed the growing world population

owing to resource constraints. The agriculture sector

alone sustains the livelihood of around 55 per cent of

India’s population and contributes nearly 18.6 per

cent to the gross domestic product. India is

characteristically a country of small agricultural

farms, where approximately 80 per cent of the total

land holdings are less than 2 ha with 30 per cent

irrigated land only. India has made tremendous

progress in food production over time due to various

technological interventions and achieved a

production level of 319.57 million tonnes during

2019-20. However, Indian farmers still face

significant challenges in terms of optimizing resource

use, minimizing crop losses, and increasing overall

productivity. These challenges include limited access

to water resources, unpredictable weather patterns,

inadequate irrigation systems, and pests and diseases

that affect crop health. Furthermore, the increasing

population and changing dietary patterns in India

pose additional pressure on the agricultural sector to

produce more food.

Agriculture has seen many revolutions, including

the domestication of animals and plants a few

thousand years ago, the systematic use of crop

rotations, and other improvements in farming

practices a few hundred years ago, or the “green

revolution” with systematic breeding and the

widespread use of man-made fertilizers and

pesticides several decades ago (Walter et al., 2017;

Vashishth et al., 2021). Agriculture is currently

undergoing a fourth revolution triggered by the

exponentially increasing use of information and

communication technologies (ICT) (Vashishth et al.,

2021). Over the years, agricultural methods have not

improved much, and farmers still use conventional

196

H, B. T. and Deshmukh, A. S.

Unraveling the Drivers and Barriers of Wireless Sensor Networks (WSNs) Uptake Amongst Grape Growers in Maharashtra.

DOI: 10.5220/0012907200004519

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture (ICEISA 2024), pages 196-203

ISBN: 978-989-758-714-6

strategies based on expectations of the crop’s

nutritional needs. Delivering the same nutrient input

across the entire farm is no longer the best choice, as

it leads to heavy fertilizer and pesticide usage,

unnecessary water consumption, environmental

degradation, and high crop production costs. There is

an urgent need to adopt more farmer-friendly

location-specific production management strategies

in a concerted manner to achieve vertical growth in

production with ensured quality of produce and

judicious use of natural resources for better returns

per unit area. In this context, precision farming has

the potential to efficiently utilize resources per unit of

time and area to achieve sustainable agricultural

practices and increase productivity.

A broad definition of precision agriculture based

on that provided by the National Research Council

(NRC, 1997) is as follows “Precision agriculture is a

management strategy that uses information

technologies to provide and process data with high

spatial and temporal resolution for decision-making

concerning crop production”. In India, the definition

of precision agriculture varies due to small land

holdings, even with the large and progressive farmers.

suitable definition in India is " Precise application of

agricultural inputs based on soil, weather and crop

requirement to maximize sustainable productivity,

quality, and profitability i.e. minimum input-

maximum output approach.'' Precision agriculture in

India is gaining importance owing to various factors.

One of the main factors is the increasing population

and changing dietary patterns in India, which are

putting additional pressure on the agricultural sector

to produce more food. Another factor is the need for

sustainable and environment-friendly agricultural

practices. In recent years, research has been

conducted to evaluate the effectiveness of Wireless

Sensor Networks (WSNs) in agricultural research

plots. Several agricultural start-ups have entered the

Indian market to introduce WSNs to farming fields.

The Indian government also plans to provide farmers

with sensors, due to the advantages of this

technology. WSNs are wireless networks that are

composed of base stations and numerous nodes

(wireless sensors). These networks are utilized for

monitoring environmental or physical conditions,

including temperature, pressure, and sound, and for

transmitting data collaboratively through the network

to a central location. A WSN is comprised of a series

of small, low-cost, low-energy, and easily deployable

sensors (Pazand & Datta, 2008). These sensors are

utilized in agriculture (Casto et al., 2021). Wireless

sensor networks are required for precision agriculture

for several reasons. Wireless sensor networks monitor

various parameters such as temperature, humidity,

soil moisture levels, and crop growth, firstly, provide

timely and informed decisions regarding irrigation,

fertilization, and pest control, leading to more

efficient use of resources and improved crop yields

(Musa et al., 2022; Naresh et al., 2020; Liu, 2022).

These data allow farmers to implement targeted

irrigation practices, minimize water waste, and ensure

optimal water use. Second, wireless sensor networks

eliminate the need for physical wiring and manual

data collection, reduce labor-intensive tasks, and

allow farmers to focus on other aspects of farming. In

addition, wireless sensor networks enable data

transmission over long distances, making it easier for

farmers to monitor and manage large agricultural

areas. Third, wireless sensor networks can provide

early warning systems for weather conditions, crop

diseases, and pest infestations (Thakur et al., 2019; Li

et al., 2020). This helps farmers take preventive

measures and minimize potential losses (Thakur et

al., 2019; Wang et al., 2006; Li et al., 2020; Kumar &

Paramasivam, 2017). Furthermore, sensor networks

are cost-effective and scalable, making them suitable

for implementation in diverse agricultural landscapes

in India. These advantages make wireless sensor

networks an essential tool for precision agriculture in

India, helping farmers optimize their farming

practices, reduce costs, and increase productivity.

Sensor networks allow them to make data-driven

decisions and apply resources, such as water,

fertilizers, and pesticides, more efficiently (Thakur et

al., 2019). Precision agriculture using wireless sensor

networks is gaining traction in India (Shah et al.,

2009). Farmers increasingly realize the benefits of

adopting precision agriculture techniques supported

by wireless sensor networks. These techniques not

only improve overall crop yields but also reduce

production costs and minimize environmental

impacts.

The adoption of precision farming technology has

been studied extensively, with research focusing on

factors such as farm size, education levels, and

perceptions of net benefits. Attitudes and perceptions

of farmers towards precision farming are crucial in

determining their willingness to adopt. Cost,

complexity, and reliability concerns can hinder

adoption, while access to information and training

programs, demonstration plots, and extension services

can play a vital role. The opinions and experiences of

peers, family members, and community members may

also influence adoption. Government policies, support

measures, and incentives can promote adoption. While

previous studies have evaluated socio-economic

factors affecting precision agriculture adoption using

Unraveling the Drivers and Barriers of Wireless Sensor Networks (WSNs) Uptake Amongst Grape Growers in Maharashtra

197

a single precision technology mostly from developed

country contexts, valuable references for policy-

making have been provided by previous studies, but

further research is necessary to fully understand the

factors affecting precision farming technology

adoption in developing countries. The adoption of

WSNs in developing countries, particularly India, has

been understudied in the literature. This paper

evaluates the perceptions of WSN technology,

attitudes toward adoption, barriers to uptake, and final

adoption decisions. In this study, the study advances

the literature related to adoption by focusing on

characteristics corresponding with the number of

WSNs adopted in India. The paper aims to fill these

gaps by examining various factors, including

socioeconomic, agro-economic, financial, and

institutional characteristics, that may influence the

intention to adopt and barriers in WSNs widespread

uptake.

2 METHODOLOGY

2.1 Field Survey

Data for this study were obtained from a survey of

grape producers from Nashik District (Dindori,

Niphad, Sinar, and Chandwada blocks) of

Maharashtra. The primary data were collected

through a questionnaire survey to obtain information

about producer drivers and barriers to WSN. A multi-

stage random sampling method was used to select

households. District, Taluks, villages, and WSN-

adopted householders were similarly selected in 12

villages with the help of agriculture start-ups (Fyllo,

Fasal, Sensartics pvt ltd, Jio Agri, Yuktix,) working

in this area. 50 (n=50) farmers were selected based on

the current adoption of WSNs and those who have at

least used technology for a minimum of 3 years. This

study purposely concentrates on Nashik. Firstly,

Nashik's geographical location and climate are

conducive to grape cultivation. Additionally, Nashik

has a long history and tradition of grape cultivation,

with established infrastructure and expertise in grape

farming practices due to the highest area under this

crop farmers tends to use new technology to be

efficient in the production. Grape crop selection is

mainly due to more number of adopters available

compared to other crops. WSNs uptake in India

mainly for commercial crops and horticulture crops

due to their high profits such as sugarcane,

pomegranate, chili, banana, apple, guards and orange

(Fig.1). Grape soil-water status constitutes one of the

main driving factors that affect plant vegetative

growth, yield, and wine quality. using a wireless

sensor network in grape cultivation can provide

continuous measurement of soil and crop parameters

to characterize the variability of soil water status,

which can help grape growers maximize crop yield

and minimize susceptibility to various pests and

diseases. Additionally, the technology can facilitate

the creation of a real-time networked database that

can be used to design the planting layout, irrigation,

and fertilization system layout. Sula Vineyards in

Nashik, India provided field support during the

deployment of the wireless sensor network at their

farms (Shah et al., 2009). In the Nashik Grape

industry, many Agri start-ups introduced wireless

sensor network technology in the fields of farmers for

irrigation and microclimate monitoring purposes.

2.2 Empirical Approach

Perceptions and barriers were collected from the

adopted farmers using a survey method. The obtained

results were analyzed using descriptive statistics.

Follow-up open questions were asked to respondents

who could volunteer further reasons for their

responses around intended adoption and barriers

while hindering the uptake of WSN (Barnes et al.,

2019; Maheswari et al., 2008).

3 FINDINGS

3.1 Socio‑Economic Characteristics

Table 1 provides descriptive statistics summarizing

the distribution of key continuous variables in the

survey data for the 50 farmers. Regarding precision

agriculture technology adoption, respondents use

2.78 technologies on average, though utilization

ranges from 1 to 10 technologies demonstrating

substantial variation. The mean education level is

approximately senior secondary at 13.22 years, with

a fair degree of dispersion between 10 and 17 years.

Meanwhile, the average farm size consists of 14.59

acres but the spread spans very small 5 acres to quite

large 53 acre holdings. Looking at wireless sensor

network specifics, the mean coverage area is 7.41

acres, though deployment reaches up to 30 acres in

maximum cases. Correspondingly, the average

annual cost of WSN amounts to a substantial Rs.

107,000, but with extreme variation from Rs. 20,000

to nearly Rs. 500,000 for sophisticated systems -

Showing major differences in technology

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

198

sophistication. The associated annual average service

charge lies around Rs. 7,500 with similar variability.

For farmers themselves, mean year of experience

stands at 29 years highlighting highly seasoned

cultivators. Farming households typically engage 2

workers, largely family members, but labour

provision extends up to 8 members revealing wide-

ranging labour resourcing. Reasonable mean

distances to agriculture extension agents suggest

moderate geographical access at 5.91 km. Lastly,

annual revenue from grape cultivation averages Rs.

434,000 across sampled farmers, capturing largely

commercially-focused profitable growers, but ranges

from Rs. 200,000 to Rs. 650,000 in exceptional cases.

Thus, while averages indicate overall representative

central tendencies, substantial deviations highlight

the diversity across grape farms in the study region.

Table 1. Descriptive statistics of continuous variable

Factors Mean Min Max SD

Num_of_

technolog

2.78 1 10 2.45

Education 13.22 10 17 2.18

Farm_ Size 14.59 5 53 10.16

Total_Area_

WSN

7.41 2 30 5.83

Farming

experience

(

Years

)

29 5 60 13.65

No_person_

farmin

2.25 1 8 1.22

Num_YR_

Insta

3.66 2 5 0.83

Distance_

agent

5.91 0 15 3.19

Cost_ WSN 10700

20000 49000

107265.2

Service_

charge/ Y

7523.4

2000 15000 4467.79

Farm_inco

me (000)

43400

20000

65200

100439.7

Table 2 provides a descriptive summary of the key

socioeconomic characteristics of the 50 farmers that

have adopted wireless sensor network (WSN)

technologies. The sample has relatively high levels of

education, with most possessing 10-15 years of

schooling and only 15.6 per cent having very

advanced qualifications. In terms of farm size,

distribution is balanced between medium (4-10 acre)

and large-scale (>10 acre) holdings, allowing

reasonable comparison. Regarding age, most fall into

the active 31-50 years (68.8%), followed by the 51-

60 years’ group. Only 6.2 per cent represent younger

generation farmers under 30 years old.

Furthermore, the most common level of farming

experience is 11-30 years (43.8%), pointing to the

prevalence of highly seasoned agriculturalists,

potentially more amenable towards technology

integration. Social category membership leans more

towards the general category (62.5%) rather than

other backward classes. In addition, while sole

proprietor-cultivators constitute 12.5 per cent, the

majority rely on family-based collective farming

groups of 2 members (68.8%) revealing moderate

household sizes on grape farms. Income levels show

polarization towards mid-tier groups making Rs.

310,000–500,000 annually (71.9%), still representing

largely profitable commercial activities supporting

technology purchases.

Moreover, strong social capital exists through

widespread agricultural organizational membership

(81.2%) that can enable WSN technology diffusion

through peer networking. The branch of farmers

without access to credit is also limited at 21.9 per

cent, though it would be critical to evaluate if this

prohibited adoption in other cases. Peer influence in

adoption decisions is also prevalent at 71.9 per cent,

highlighting the need for visible pilot trials and

testimonials to motivate adoption. In summary, the

profile of these WSN adopters consists largely of

commercially focused, small-scale grape producers

with extensive cultivation expertise balanced with a

mid-career orientation amenable towards innovation.

Table 2. Socio-economic characteristics distribution of

WSNs adopted farmers

Parameter Adopted farmers (%)

Num_of_technology

Low intensity (1-3) 37.50

Medium intensity (4-8) 53.13

High intensity (9-11) 9.38

Farm size

(

Acre

)

Medium farmers

(

4-10

)

53.1

Lar

e farmers

(

>10

)

46.9

Age (Yrs)

Less than 30 6.2

31-50 68.8

51-60 12.5

More than 61 12.5

Education (Yrs)

10 to 15 84.4

15 to 17 15.6

Farming experience (Years)

Less than 10 12.5

11 to 30 43.8

31 to 50 37.5

Unraveling the Drivers and Barriers of Wireless Sensor Networks (WSNs) Uptake Amongst Grape Growers in Maharashtra

199

More than 51 6.2

Social categor

GM 62.5

OBC 37.5

No of persons engaged in

farming

1 membe

12.5

2 members 68.8

3 members 12.5

4 members 3.1

8 members 3.1

Farm income (Rs) from

<300000 3.1

310000-400000 37.5

410000-500000 34.5

>510000 25

anization membershi

Yes 81.2

No 18.8

Access to financial support

Yes 78.1

No 21.9

Social influence in ado

tion

Yes 71.9

No 28.1

Table 3 presents detailed statistics on the specific

WSN technologies implemented by the surveyed

farmers. It covers 5 categories including moisture

sensors, scalar sensors, tracer units, master nodes, and

full weather stations. For each variant, it provides a

breakdown of the average area under coverage (4.6 to

9.5 acres), the average number of nodes adopted (1.6

to 2.08), the mean cost per installation (INR 27,800

to 95,428), and the mean annual service charges paid

(INR 2,700 to 6,428). The table also shows the

substantial deviations around these technology

investment levels with maximum costs ranging from

INR 120,000 per scalar unit to INR 364,000 for a

weather station. Overall, farmers tend to install

sensors over reasonably small land sizes of 2 to 5

acres, with 1-2 sensor nodes on average linked to a

base station. Moisture sensors and scalar units

constitute the cheapest options with weather stations

and master nodes being far more capital and service

intensive. The data highlights how farmers tend to

implement fairly basic sensor network configurations

for critical applications like soil moisture monitoring,

rather than high cost and complex deployments. It

provides insights into current investment ranges

across distinct types of WSN technologies farmers are

adopting, also revealing significant price variability

across units and operators even for the same

underlying technology and acreage coverage. The

large deviations point to the lack of standards and

need for regulations around WSN charges and

specifications. The insights on variants can help

prioritize current recommendations and policies for

supporting WSN-based precision irrigation among

smallholders.

Table 3. Technology characteristics of adopted farmers

WSN

technology

variant

Mean Min Max SD

Only Moisture

Area 4.6 2 7 1.82

Number 1.6 1 2 0.55

Cost 27800 12000 42000 13535.14

Service

Char

(

)

2700 2000 4000 836.66

Scalar

Area 5 2 15 3.74

Number 2.08 1 8 2.02

Cost 40692.31 20000 160000 40468.89

Service

Char

(

)

2096.15 750 3000 554.7

Tracer

Area 5 2 10 3.16

Number 1.6 1 3 0.89

Cost 47000 20000 90000 28195.74

Service

Charge (Yr)

4200 2000 7000 2167.95

Master

Area 3.92 2 9 2.65

Number 1.67 1 5 1.63

Cost 84500 47000 250000 81138.77

Service

Charge (Yr)

7333.33 5000 9000 1505.55

Weather Station

Area 4.79 2 17.5 3.6

Number 1.71 1 7 1.45

Cost 95428.57 50000 364000 78678.82

Service

Char

(

)

6428.57 3000 9000 1247.86

3.2 Perception of Adoption

The top reasons for adopting WSNs were: crop

dynamics (11%), good quality produce (14%),

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

200

technology being scale neutral (14%), and reduction

in water use (13%). Increased yields (10-20%) and

real-time pest/disease detection (10%) were other

notable reasons (Table 4).

Table 4. Perception for the adoption of WSNs

Reasons Response

(%)

Rank

Good quality of produce &

increase in shelf life of

erries

14 I

Technology Scale neutral 14 I

Optimize water (30- 40 %)

com

ared to dri

irri

ation

13 II

Reduction in overall cost 12 III

Crop dynamics 11 IV

Real-time detection of pest

and insect

10 V

Crop-specific nutrient

recommendation

9 VI

Increase in 20 % yield 8 VII

Increase in 10% yield 5 VIII

Low investment 4 IX

3.2 Barriers for adoption

Results highlighted that the top barriers were high

service charges (11%), lack of government support

(10%), accreditation issues (10%), and problems with

produce marketing (11%). Other barriers like land

fragmentation, lack of information, network issues,

and financing constraints were also reported by fewer

respondents (Table 5). Reasons like water savings,

crop dynamics, produce quality and scale neutrality

encouraged adoption, while service costs and

institutional issues posed barriers.

Table 5. Barriers for adoptions of WSNs

Barriers Response (%) Rank

Need to depend on petiole analysis of the soil 11 I

High service charges 11 I

Accreditation problem of technology by the Government 10 II

Farmland is too scattere

9 III

No re

ularization of cost and service char

e 9 III

No Government su

ort 7 IV

Lack of finance and credit facilit

5 V

Use

-friendly app and voice alerts 5 V

Electric power supply issue 5 V

The automation unit needs to be

urchased se

aratel

Trust issue on accurac

4 VI

Lack of information 3 VII

Replacement of technology needs additional charges 3 VII

Network incompatibilit

2 VIII

4 CONCLUSIONS

This study analyzed factors influencing wireless

sensor network (WSN) technology adoption among

50 grape farmers in Maharashtra. Annual service

charges and high technology costs deter the intensity

of WSN adoption, highlighting affordability barriers.

Offering financing support and initial discounted

charges could enhance adoption. Farm income

exhibits the most substantial positive influence,

indicating that revenue-based ability to absorb costs

is critical. Policies to bolster farmer incomes would

enable WSN investments. As technology installation

time increases, usage intensity declines potentially

due to shifting needs. This suggests innovations and

upgrades are required over time. Farm size shows a

small positive effect on adopted intensity. Scale-

appropriate policies should promote WSNs among

both small and large holdings.

5 POLICY SUGGESTIONS

Provide subsidies and financing support to lower

service charges and upfront costs, enhancing WSN

affordability. Develop cost-sharing or lease-based

models. Implement minimum price standards, crop

insurance, and income stabilization programs to raise

Unraveling the Drivers and Barriers of Wireless Sensor Networks (WSNs) Uptake Amongst Grape Growers in Maharashtra

201

and stabilize grape farmer earnings. This would

facilitate WSN investments. Fund R&D initiatives to

continuously upgrade WSN solutions to align with

evolving farmer requirements over time after initial

adoption. Undertake comparative trials showcasing

WSN effectiveness across varied farm sizes. Enable

scale-neutral policies for promotion based on

potential water and cost savings. The findings

highlight that strengthening farmer economics and

purchasing power is vital for precision solutions like

wireless sensor networks to transform smallholding

agriculture alongside technology advancement

effectively. Hybrid policy approaches are required

spanning technology, institutions, and farm

economics.

ACKNOWLEDGMENTS

We gratefully thank the ISEC for providing

infrastructural support to conduct research.

Bhuvaneshwari T H, is a recipient of the Indian

Council of Social Research Doctoral Fellowship.

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