Farmer Perceptions and Economic Significance of Kissan Drones in

Maize Production: An Empirical Field Study

Ravikumar R, Harishankar K and Kalidas K

Department of Social Sciences, Vanavarayar Institute of Agriculture, (Affiliated to Tamil Nadu Agricultural University),

Pollachi-642103, India

Keywords: Maize, Kissan Drone, Perception and Economics.

ABSTRACT: Agriculture remains the backbone of India’s economy, with maize ranking third among food crops,

contributing 9% to the national food basket and over ₹100 billion to agricultural GDP. The integration of

Agriculture 5.0 technologies artificial intelligence, drone technology, and the Internet of Things plays a

pivotal role in addressing agricultural challenges and enhancing sustainability. This study explores farmer

perceptions and the economic implications of Kissan Drone adoption in maize production, focusing on

intensive cultivation areas in Tamil Nadu. Using advanced statistical models, the research evaluates

adoption levels, willingness to pay, and economic feasibility, providing data-driven policy

recommendations. Demographic analysis reveals a generational technology gap, with adopters averaging 42

years, potential adopters 52 years, and non-adopters 64 years. Findings indicate that drone adoption for Fall

Armyworm (FAW) control reduces spraying costs by 22%. Regression analysis suggests that tailored

educational programs can enhance adoption rates, but pricing alignment and innovative dissemination

strategies remain crucial. The study concludes with policy recommendations to accelerate Kissan Drone

adoption and promote sustainable farming practice.

1 INTRODUCTION

Agriculture is the main source of income for about

two thirds of India's population, and it makes a

substantial contribution to the GDP of the country

(Ministry of Finance, 2022). In order to solve current

issues and advance sustainability, digital technology

must completely revolutionise agriculture. Emerging

technologies like artificial intelligence, drone

technology, and internet of things play pivotal roles in

enhancing agricultural production and creating more

predictable, sustainable, and seamless supply chains.

Drone technology has brought remarkable precision

and efficiency to farming practices, revolutionizing

tasks such as pesticide and liquid fertilizer

applications, water area mapping, water sampling,

pest infestation tracking, and crop management.

Recognizing these advantages and market potential,

the Indian government introduced "Kisan drones" to

address operational delays, reduce pesticide and

fertilizer consumption through automation, lower

spraying and fertilizer application costs, and minimize

human exposure to hazardous chemicals. To ensure

inclusive adoption, it is essential to formulate a

strategy that achieves economies of scale in Kisan

drones, making them accessible to all types of

farmers. This research aims to explore Kisan drone

technology adoption and its economic impact at the

farm level. Ultimately, this policy framework aims to

improve the precision and efficiency of agricultural

practices, thereby contributing to the sustainable

development of Indian agriculture.

2 REVIEW OF LITERATURE

Drones revolutionize modern agriculture by enabling

precision crop monitoring, resource optimization, and

cost reduction (Ahirwar et al., 2019); Beriya, 2022).

Yet, widespread adoption faces hurdles like regulatory

constraints and the need for extensive training.

Despite its potential, research on drone technology in

Indian agriculture is limited, with farmers' perceptions

and adoption influenced by their technological

familiarity and education levels. (Puri et al., 2017);

(Dutta and Goswami, 2020). Studies suggest that

larger farms with greater resources tend to adopt

drone technology more rapidly, benefiting from their

R, R., K, H. and K, K.

Farmer Perceptions and Economic Signiﬁcance of Kissan Drones in Maize Production: An Empirical Field Study.

DOI: 10.5220/0012884000004519

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 143-150

ISBN: 978-989-758-714-6

143

enhanced capacity to absorb initial investment costs

(Kelley et al., 2003). However, the adoption rate of

drones in agriculture remains modest, accounting for

only 3% of the farming community. This tepid

adoption is attributed to barriers such as upfront costs

and limited access to training and technical support.

Despite these challenges, drones offer compelling

economic benefits. Research indicates that 95% of

farmers in Ghana were willing to pay for drone

technology due to its potential to enhance crop yields,

reduce input costs, and improve resource allocation

(Annor-Frempong and Akaba, 2020); Techno-

economic feasibility assessments have revealed

favorable returns on investment, with notable internal

rates of return and significant increases in farmers'

revenue and time savings per hectare (Mogili and

Deepak, 2018). Farmers' adoption decisions are

complex and influenced by personal, contextual, and

operational factors. While some farmers may remain

unaware of the benefits of drone technology, others

perceive drones as overly intricate or disruptive to

their traditional farming methodologies (Suvedi et al.,

2022). Thus, addressing these barriers and tailoring

adoption strategies to farmers' diverse needs and

contexts are crucial for accelerating the uptake of

drone technology in agriculture.

3 RESEARCH GAP

The literature on Kisan drone technology in

agriculture has predominantly focused on farmers'

perceptions, adoption rates, and economic outcomes,

particularly in maize cultivation. However, a

significant research gap exists regarding the

comprehensive exploration of perceptions and

economies of drone adoption in India. Addressing

this gap is essential as it can offer insights into

drones' actual economic benefits, affecting farmers'

income, cost reduction, and resource optimization.

Existing studies, primarily from developed nations,

lack thorough research on economic implications.

Bridging this gap could foster a more holistic

understanding of Kisan drones' role in Indian

agriculture, guiding policy decisions and promoting

sustainable practices.

4 RESEARCH QUESTIONS

The existing research concerning Kisan drone

technology has predominantly centered on

examining farmers' perceptions, adoption rates, and

the resulting economic consequences within the

context of maize cultivation. Nevertheless, there

exists a noticeable dearth of comprehensive studies

dedicated to the economic aspects of drone

technology. The following research explore the key

factors influencing farmers' perceptions regarding

the adoption and their willingness to pay for this

technology adoption.

5 OBJECTIVES OF THE STUDY

This study comprehensively explores Kisan drone

technology adoption and its agricultural impact, with

interconnected objectives. It seeks to understand

farmers' perceptions, dynamics of Kisan drone

adoption, and estimate willingness to pay using

direct inquiry method. Additionally, it evaluates the

economics of drone application, empowering

farmers to optimize resource utilization. Finally, the

study advocates for policy options encouraging

responsible and widespread drone use, aiming to

bridge gaps between perception, economics, and

policy for informed decision-making and sustainable

practices.

6 METHODOLOGY OF THE

STUDY

The research endeavours to examine

comprehensively the perceptions of farmers, the

dynamics governing awareness and adoption, their

willingness to pay, and the cost economics

associated with the utilization of Kisan drone

technology, particularly within the realm of maize

cultivation in Tamil Nadu. The selection of maize as

the focal crop stems from its heightened

susceptibility to pests and reliance on plant

protection chemicals, rendering it conducive to the

integration of drones into the production process.

The research methodology entails a cross-sectional

survey approach using validated questionnaire to

survey the respondents in way of direct interviews.

Sampling Technique

A multi-stage random sampling technique is used to

select 240 maize farmers from the Dindigul and

Tirupur districts. Farmers are categorized into three

groups: adopters (actively using Kissan Drones),

potential adopters (interested but not yet using), and

non-adopters (not interested).

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The districts serve as separate strata to ensure

balanced representation. The total sample is divided

proportionally based on the maize farming

population in each district. Within each district,

specific blocks with maize cultivation are randomly

selected.

This approach ensures a representative and diverse

sample of maize farmers.

Modelling Adoption

The FACOPA model was adopted to analyse factors

influencing adoption, considering socio-structural

factors and perceived complexities. Multinomial logit

analysis will be used to understand varying levels of

propensity to adopt. Principal Component Analysis

(PCA) will be used to reduce the dimensionality of

the dataset, particularly concerning perceived

complexities related to Kisan drone adoption. This

will help identify key factors influencing adoption

perceptions. Linear regression was adopted to explore

the relationship between socio-structural variables and

the perception of complexity, using the principal

components as dependent variables and other socio-

economic and farm-related variables as independent

variables. The estimated willingness to pay and assess

cost economics for drone technology through direct

inquiry methods with respondents, providing valuable

insights into the economic aspects of adoption.

7 RESULTS AND DISCUSSION

7.1 Distribution of Respondents’

Socioeconomic Characteristics

Socioeconomic characteristics encompass a range of

attributes and factors that delineate the economic

and social standing of individuals, households,

communities, or broader populations. These

characteristics offer valuable insights into the

distribution of assets, opportunities, and access to

services within a society. The respondents were

categorized based on their perceptions of drone use

in crop production. The classification includes

adopters, who actively employ or express positive

interest in drone technology (N=94); individuals

with an idea for adoption, indicating interest but no

active commitment (N=61); and those not willing to

adopt, who show reluctance or disinterest in drone

use (N=85). This classification facilitates a

significant understanding of attitudes toward drones

in agriculture, capturing varying levels of acceptance

and apprehension within the surveyed population.

The examination of the table reveals distinct

variations in the average age of respondents across

the three identified categories. Notably, the average

age within the adopter’s category is relatively

younger, standing at 42 years. In contrast,

individuals indicating an idea for adoption exhibit an

average age of 52 years, while those expressing a

lack of willingness to adopt Kissan drones have an

average age of 64 years. This disparity underscores a

clear pattern wherein younger farmers show a

greater propensity to embrace Kissan drones. It is

evident that respondents in the younger age

demographic exhibit a more favourable perception

towards Kissan drone adoption compared to their

older counterparts, suggesting a generational divide

in the acceptance of this technology within the

agricultural community.

Table 1: Socio Economic characteristics of surveyed

respondents.

Variables

Adopters

(N=92)

Willing to

Adopt

(N=61)

Not Willing

to Adopt

(N=85)

Total

(N=240)

Age in

Years

(Mean)

47 52 64 54

Education

Collegiate

level

57 8 10 75

Higher

Secondar

34 31 16 81

Secondary

level

3 18 34 55

Primary

level

0 4 17 21

o formal

Education

0 0 8 8

Intensity in farm operations

Less than 50

days pe

year

12 14 17 43

Between 50

to 100 days

er yea

18 41 38 97

Between

101 to 150

days pe

yea

29 5 24 58

More tha

50 da

35 1 6 42

Information Pursuing

Less than 5

hours pe

month

15 20 31 66

Between 5

to 10 hours

er month

12 23 17 52

Farmer Perceptions and Economic Signiﬁcance of Kissan Drones in Maize Production: An Empirical Field Study

145

Between 10

to 15 hours

er month

38 12 18 68

More tha

15 hours pe

month

29 6 19 54

Size o

Farm in ha

(Mean)

3.68 3.15 3.32 3.38

7.2 Frequency Adoption of Kissan

Drone

The control of Maize Fall Armyworm using Kissan

drones represents a groundbreaking and innovative

approach that holds significant promise for farmers.

The study contributes fresh perspectives by shedding

light on the frequency interval of Kissan drone usage

in Maize Fall Armyworm control. Analyzing the

frequency of Kissan drone adoption offers valuable

insights into the integration of this technology into

agricultural practices. The inform strategies aimed at

promoting wider acceptance and utilization of drone

technology among farmers, ultimately fostering

more sustainable and productive farming practices.

The data clearly indicates that adopters primarily

utilize drones for spraying insecticide against the

Fall Armyworm (FAW). Among adopters,

approximately 58.5 percent employ Kissan drones

for this purpose twice during the maize production

cycle, followed by 27.7 percent who use them only

once. A smaller percentage, approximately 10.6

percent of the surveyed respondents, utilizes drones

thrice during the maize production cycle. These

findings underscore the prevalence of drone usage

among adopters and highlight the frequency with

which they integrate this technology into their maize

production practices, particularly for combating

FAW infestations.

Table 2: Frequency and purpose of adoption.

Frequency

interval

Purpose of Adoption (N=94)

Only once

Spraying insecticide against

FAW

26 (27.7)

Two times

Spraying insecticide against

FAW

55 (58.5)

Three times

Spraying insecticide against

FAW

10 (10.6)

Three an

above

Spraying insecticide against

FAW

3(3.2)

7.3 Factors–Complexity Perception

Adoption of Kissan Drones in

Agriculture

To explore the influence of perceived complexity on

drone adoption, a survey was carried out among

maize cultivating farmers. The surveys were

conducted in a face-to-face manner, and the

respondents self-completed a paper questionnaire

with the support of a researcher. The aim of the

analysis is to measure the probability of PA adoption

as dependent on perceived complexity, which is

established as a composite variable. Therefore, a

purposive sample technique was used. A

questionnaire was submitted to a sample of 300

farmers. This sampling technique aims to

subjectively select interviewees with the purpose of

gathering detailed information on the object of study

(Kelley et al. 2003). To obtain a purposive sample,

an initial question was asked: "Have you ever heard

of precision agriculture?" If the answer was

negative, the respondents were excluded from the

survey. This choice was due to the desire to have a

sample that was at least "aware" of the subject of the

survey. To exploratory work, purposive sampling is

commonly used to collect empirical data (Etikan et

al. 2016). The complexity perceived from adopting

drone technologies is gauged using six distinct

factors, each assessed on a Likert scale from 1

(strongly disagree) to 5 (strongly agree), to

demonstrate their potential impact on adoption.

These factors include:

Cost Impact (co): The deployment of Agriculture 5.0

technologies contributes to farm efficiency by

lowering operational costs.

Management Complexity (mo): The implementation

of these technologies introduces complexities in

farm management, necessitating enhanced

managerial competencies.

Organizational Adjustment (oo): Adopting these

technologies may pose challenges in organizational

and structural adaptation.

Agricultural Practice Modification (po): Drone

technologies necessitate alterations to traditional

farming practices.

Financial Commitment (fo): The investment in these

technologies involves substantial financial outlays

that may be difficult to recoup.

Adoption Rarity (io): The prevalence of Agriculture

5.0 technologies is limited in the regions where

farmers are operating.

To examine the relationship among variables within

the same conceptual framework, an initial

correlation analysis was employed. This analysis

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

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revealed significant relationships among the six

factors, as evidenced by noteworthy Pearson

correlation coefficients (p < 0.01) detailed in Table 3

Following the identification of these significant

correlations, we assessed the internal consistency of

the dataset using Cronbach’s alpha, a metric that

evaluates the reliability of a scale by determining the

cohesion among a set of items. While a high

Cronbach’s alpha value doesn’t confirm the one-

dimensionality of a scale, it prompted us to further

investigate this aspect through exploratory factor

analysis. Consequently, these variables were chosen

for Principal Component Analysis (PCA), which

identified a single component accounting for 68% of

the total variance. This component's extraction

validates the interconnectedness among various

dimensions and enables us to refine our

understanding of complexity through a unified

indicator derived from the PCA coefficients. With

this index established, the analysis can proceed in

two stages.

A linear regression model is employed to examine

the impact of socio-structural factors on the

complexity perception. By applying this analytical

approach, we can pinpoint which factors play a

crucial role in shaping an individual's perception

throughout the adoption phase. Prior to conducting

the linear regression, a correlation analysis was

carried out to ensure that there was no correlation

among the socio-structural variables. The analysis

reveals that the socio-structural variables exhibit

weak correlations with each other, all values falling

below 0.6. This suggests a limited association

between the variables, indicating that changes in one

variable are not strongly linked to changes in

another.

Table 3: Correlation analysis for the perceived complexity.

Factors CI MC OA APM FC AR

CI 1

MC 0.471** 1

OA 0.279* 0.571* 1

APM 0.376** 0.608** 0.509** 1

FC 0.573* 0.189* 0.411** 0.397** 1

AR 0.543* 0.411** 0.365* 0.456** 0.272** 1

Note: ** indicates 1% level of significance, *

indicates 5% level of significance.

Table 4: Matrix components of principal component

analysis.

Matrix components

Average factor

loadin

Cost Impact 0.712

Management Complexity 0.591

Organizational Adjustment 0.671

Agricultural Practice

Modification

0.817

Financial Commitment 0.756

Adoption Rarity 0.627

The multiple linear regression analysis elucidates

how various socio-structural factors influence

farmers' perceptions of complexity regarding the

perception of new agricultural technology i.e.,

drone. The analysis delineates a nuanced landscape

where each factor—age, education, landholding size,

intensity of work, and information intensity—plays a

distinct role in shaping these perceptions. At the

outset, the analysis reveals an inverse relationship

between age and perceived complexity. Specifically,

older farmers tend to view the adoption of new

technologies as less complex, suggesting that

experience or familiarity with farming practices may

mitigate concerns over integrating new technologies

(Pannell et al., 2006). This finding highlights a

potential generational divide in the adoption process,

where younger farmers might perceive greater

barriers to technology integration, possibly due to

less experience or different attitudes towards

innovation (Ntshangase et al., 2018). Education

emerges as a factor that positively related with

perceived complexity. This suggests that more

educated farmers, who are presumably more aware

of the potential challenges and benefits of new

technologies, perceive greater complexity in

adopting these innovations. This counterintuitive

finding might reflect a more critical evaluation of

new technologies by educated farmers, underscoring

the need for educational programs to address not

only the benefits but also the practical challenges of

technology adoption (Kumar et. al., 2020). The size

of landholding also positively influences perceived

complexity, indicating that farmers with larger

operations perceive greater challenges in adopting

new technologies. This could be attributed to the

logistical and managerial complexities associated

with implementing new technologies on a larger

scale. Thus, support mechanisms for technology

adoption may need to be scalable and adaptable to

the size of the farming operation (Rejeb et al., 2022).

Farmer Perceptions and Economic Signiﬁcance of Kissan Drones in Maize Production: An Empirical Field Study

147

Conversely, the intensity of work and information

intensity exhibit negative correlations with perceived

complexity. Farmers who are more engaged in their

work and those who dedicate more time to acquiring

information through various channels tend to

perceive lower complexity in adopting new

technologies. These findings highlight the

importance of active engagement and information

access in facilitating technology adoption.

Specifically, they suggest that initiatives aimed at

increasing farmers' exposure to information and

knowledge about new technologies could play a

critical role in reducing perceived barriers and

complexities (Babu et al., 2012)/

Table 5: Correlates of socio-structural factors.

Factors Age

Educatio

Landholding

Intensity

of wor

Intensity of

information

Age 1

Education

0.271

Landholdin

g (ha)

0.479 0.409 1

Intensity of

wor

0.287

0.189 -0.573 1

Intensity of

information

0.499 0.618 0.476 0.258 1

In sum, the regression analysis provides critical

insights for policymakers and practitioners aiming to

foster agricultural technology adoption. Tailoring

support and educational programs to address the

specific needs and perceptions of different farmer

groups—considering factors such as age, education

level, landholding size, and information-seeking

behavior could enhance the adoption rates of new

agricultural technologies. These insights underscore

the necessity of a nuanced approach to technology

dissemination and adoption support, one that

acknowledges the diverse landscape of the

agricultural sector and the varied perceptions of

farmers towards new technology.

7.4 Actual Pay and Willingness to Pay

for Kissan Drone

The adopters were specifically approached to

provide valuable insights into two pivotal aspects

above the actual payments made by farmers for the

utilization of the Kissan drone and their willingness

to pay for its usage. The detailed insights serve to

inform analysis and strategic decision-making in

agricultural technology adoption initiatives,

contributing to more informed and effective

practices in the field.

The figure reveals that adopters perceive their

current payment for drone per spray to be higher

than their willingness to pay. According to consumer

surplus theory, the actual payment should ideally be

lower than the willingness to pay to ensure societal

welfare. However, these findings raise concerns that

the cost of drone spray might need to be reduced to

encourage continuous adoption. This discrepancy

suggests a potential barrier to sustained adoption of

drone spray technology and underscores the

importance of aligning pricing strategies with

adopters' expectations and economic realities.

Adjusting the cost structure could help enhance the

affordability and accessibility of drone spray

services, thereby fostering broader adoption and

maximizing societal benefits in agricultural

practices.

Table 7: Cost Comparison of Kissan drone spray and other

spray methods.

Particulars Kissan drone

Ado

ters

(

)

Non adopters

(

61+85=146

)

Average

Spraying cost

er ha

1776 2355

Purchase cost of

chemicals

985 1205

Total cost of

crop protection

2761 3560

Per cent of cost

reduction 22

ercent

Figure 1: Actual pay Vs willingness to pay in adoption

(N=94).

Based on the responses gathered from the

respondents, it was observed that there was a

tangible 22 percent reduction in the cost of spraying,

translating to an absolute difference of 799 units.

However, this reduction was deemed inadequate as

an optimal cost-saving measure. To effectively

2000

Maximum Minimum Average

1050

950

975

800

600

650

Actual vs Willingness to pay per

ha/spray by adopters

Actual Payment per ha/spray

Williness to Pay per ha /spray

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leverage technology intervention and substantially

bolster farm profits, a cost reduction exceeding 50

percent is imperative.

8 CONCLUSION

The research concerning Kisan drone technology

that distinct variations in the average age of

respondents across the three categories. Adopters

have an average age of 42 years, while individuals

considering adoption average 52 years, and those

unwilling to adopt have an average age of 64 years.

This age disparity indicates that younger farmers are

more inclined to embrace Kissan drones,

highlighting a generational gap in technology

acceptance within the agricultural community. The

study explore that adopters primarily use drones for

spraying insecticide against Fall Armyworm (FAW).

The findings derived from the regression analysis

indicate that mediatisation and educational programs

to supply to the unique needs and perceptions of

diverse farmer groups while bearing in mind factors

such as age, education level, landholding size, and

information-seeking behavior has the potential to

significantly enhance adoption rates. The emphasis

lies in adopting a innovative approach to technology

dissemination and adoption support that recognizes

the complex landscape of the agricultural sector and

acknowledges the diverse perspectives held by

farmers regarding new technology. The study also

insight that adopters perceive current drone spray

costs to exceed their willingness to pay, contrary to

consumer surplus theory. Positioning pricing

strategies with adopters' expectations and economic

realities is crucial to foster broader adoption and

maximize societal benefits in agriculture.

Respondents noted a 22% reduction in spraying

costs, equating to 799 units, yet deemed inadequate

for optimal savings. Further cost reductions are

needed to leverage technology for substantial farm

profit enhancement. Based on the findings outlined

in the research, several policy suggestions can be

proposed to address the challenges and opportunities

identified in Kisan drone adoption and its impact on

agriculture

1) Develop tailored support programs aimed at

different farmer groups based on their adoption

behaviour (adopters, potential adopters, and non-

adopters). These programs should address specific

needs and perceptions identified in the study,

considering factors such as age, education level, and

farm size.

2) Enhance financial assistance schemes for drone

purchases and field demonstrations, particularly

targeting lower-income farmer groups. This could

include subsidies, grants, or low-interest loans to

make drone technology more accessible.

3) Implement comprehensive education and training

programs to increase awareness and improve

understanding of drone technology and its benefits

among farmers. These programs should focus on

practical aspects of drone operation, maintenance,

and integration into existing farming practices

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