Factors Influencing the Adoption of IoT Based Micro Level

Farm Intelligence Systems by Dryland Farmers in the

State of Andhra Pradesh

T. Yamini

, Y. Prabhavathi

1,*

and Vani Srilatha

Institute of Agribusiness Management, S V Agril College,

Acharya N G Ranga Agricultural University, Tirupati, India

Agricultural Extension, DAATTC, Acharya N G Ranga Agricultural University, Lam, Guntur, India

Keywords: Smart Agriculture, IoT, Micro Level Crop Intelligence Systems, Binary Logit.

Abstract: Small holders in India are faced with multiple challenges, with limited access to essential information being

a prominent hurdle, hindering their ability in making informed decisions throughout the crop cycle. This

leaves them to various risks, particularly weather and pest attacks. Application of smart agricultural

technological innovations such as AI, IoT, big data, robots, and drones enhances decision support systems,

farm efficiency with promising economic and social benefits for smallholders, yet adoption remains a

significant challenge in Indian agricultural landscape. Hence, the study majorly emphasizes on identifying the

determinants of adoption of IoT (Internet of Things) based micro level crop intelligence systems in Anantapur

district of Andhra Pradesh state, a region highly susceptible to climate changes. Primary data from a sample

of 100 and employing binary logistic regression revealed that factors namely perceived usefulness, perceived

ease of use, farmer innovativeness, facilitating factors and influential factors significantly increased the

likelihood of adoption of IoT technologies. Conversely, perceived cost and complexity of decision making

for farm operations decreased the likelihood of adoption. Thus the study advocates boosting adoption factors

and streamlining processes to integrate IoT in smallholder farming, enhancing resilience and farm efficiency

amidst challenges.

1 INTRODUCTION

Indian farmers, predominantly small holders,

grappled with challenges encompassing land

fragmentation, resource constraints and market

volatility. Access to right information throughout

crop cycle remains a significant challenge. Despite

relying on their own knowledge, advices from fellow

farmers, input dealers and institutional sources for

farm decisions, farmers still confront with risks of

weather and pest attacks (Rehman, et al. 2013; Kapur

and Kumar, 2015). Technological innovations in

agriculture are identified as potential solutions for

challenges in Indian agriculture. Smart agriculture

integrates IoT, drones, big data and AI into precision

farming, enabling real-time data on soil moisture,

weather and crop water needs. This optimizes

fertilization, pest control and irrigation leading

Corresponding Author

increased productivity. This aligns with Sustainable

Development Goals, offering substantial economic,

social, and environmental benefits (FAO, 2019). IoT

optimizes dryland farming with weather tracking,

crop monitoring and smart irrigation, cutting yield

losses and financial risk.

While IoT offers benefits to smallholder farmers,

its widespread adoption across India remains a

significant challenge, with slow adoption rates by

farmers globally (Walter et al. 2017). Slow adoption

rates persist due to lack of technical proficiency and

socio-demographic and other factors among farmers.

Reliable internet connectivity is essential access to

access real-time information, but costly. Further, time

gap between the technology and its adoption at farmer

level is driven by these drivers and hence farmers

showing unwillingness to shift from conventional

practices (Naik et al. 2022). understanding these

determinants is crucial for promoting adoption of

Yamini, T., Prabhavathi, Y. and Srilatha, V.

Factors Inﬂuencing the Adoption of IoT Based Micro Level Farm Intelligence Systems by Dryland Farmers in the State of Andhra Pradesh.

DOI: 10.5220/0012882000004519

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 69-77

ISBN: 978-989-758-714-6

these technologies as they holds immense potential

for revolutionizing the agricultural through efficient

decision support systems. Hence, the study aims to

identify factors influencing farmers adoption of

various farm level crop intelligence systems in

Anantapur District of Andhra Pradesh.

2 LITERATURE REVIEW

The present study adds to the growing body of

literature by identifying factors influencing the

adoption of IoT based farm intelligence systems.

Adrian et al. (2005) identified that perception of

usefulness, perception of ease of use, attitude of

confidence, perception of net benefit, farm size and

farmer educational levels positively influenced the

farmers intention in adoption of precision agriculture

technologies (PAT). Souza Filho et al. (2011)

emphasized socio-economic, crop, land ownership,

technology and systemic factors along with

neighboring farmers, institutions, and social norms

influenced PAT adoption. According to Tey and

Brindal (2012), factors influencing PAT adoption

include socio economic, institutional, behavioural,

agroecological, information sources, farmers

perception, technology and farmers behavior. Aubert

et al. (2012) emphasized that Perceived ease of use,

usefulness, resource availability, trialability, and

farmer characteristics impact PAT adoption, while

farm size does not.

Antolini et al. (2015) highlighted that socio-

economic, agro-ecological, institutional,

technological and behavioural factors, information

sources and farmers perception were key adoption

drivers of PAT. Tubtiang and Pipatpanuvittaya

(2015) revealed guava farmers' adoption of smart

farm technologies is influenced not only by perceived

usefulness and ease of use but also by external factors

like financing and land structure. Torrez et al. (2016)

identified farm size, operator size, cropping

efficiency, risk aversion, and time are key factors

influencing PAT adoption among Kansas farmers,

with large farms and operator age showed linear and

inverse relationships. As per Paustian and Theuvsen,

(2017), among various socio demographic factors,

networking events significantly influenced Denmark

and German farmers’ adoption of PAT.

Chuang et al. (2020) found that organizational

support, income, trust, perceived usefulness and ease

of use positively drives young farmers' intention to

adopt IoT technologies, while factors like land

ownership and willingness-to-pay had affected these

decisions. While insufficient information,

knowledge, awareness and perceived practical value

hinders adoption. Vecchio et al. (2020) examined that

higher rates of adoption of PAT were among younger,

highly educated farmers with access to intensive

information and large farm sizes holders. Yatribi

(2020) emphasized that perceived utility remains the

most identified determinant while farmers gender and

experience were not always determinants for

adoption. According to Mohr and Rainer Kuhl

(2021), perceived behavioural control had the greatest

influence followed by farmers personal attitude in

acceptance of Artificial intelligence systems in

agriculture. Rosario et al. (2022) employing structural

equation model revealed that socio-psychological

determinants play a key role in understanding the

decision making process in the context of adoption of

sustainable agriculture innovations.

3 MATERIALS AND METHODS

3.1 Selection of Study Area and Sample

Respondents

Anantapur district of Andhra Pradesh, the second

driest district in India, was chosen for its vulnerability

to climate change and with more than 70 % of farmers

depending on agricultural agriculture (MANAGE,

2019). Recent trends showed that dryland farmers of

the district are shifting from annual to perennial crops

to mitigate climate risks. NGOs, agri-tech startups

like FASAL, FYLLO and government institutions are

promoting farming services centered around IoT-

based farm-level crop intelligence systems in the

study area. Adoption of these technologies in these

climate susceptible areas has wider scope of

impacting the agriculture towards attaining

sustainability through facilitating farmers to take

informed decisions at every stage of crop cycle. The

study obtained a list of farmers adopting IoT-based

crop intelligence technologies from agri-tech startups

and randomly selected 50 farmers. Additionally, 50

neighboring farmers with similar irrigation, cropping,

and market conditions were identified, making the

sample size to 100 farmers.

3.2 Description of Interview Schedule

The interview schedule for primary data collection

comprised two main components. The first

addressed

socio demographic and other information particulars

to identify the determinants. The second component

included 33 statements rated on a five-point Likert

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

scale and these statements covered perceived

usefulness, ease of use, decision-making complexity,

predictive abilities, resource scarcity, produce

quality, farmer innovativeness, influential and

facilitating factors, and perceived cost components.

Perceived usefulness was measured by facilitating

timely decisions, resource optimization, yield

increase, operation monitoring, and risk mitigation.

Perceived ease of use assessed simplicity in

acquisition, operation, and maintenance. Decision-

making complexity considered technology

suitability, climate vulnerability, and operational

compatibility. Predictive decision-making assessed

technology for predictive farm operations and climate

risk mitigation. Resource scarcity evaluated

technology for areas with Farmer innovativeness was

gauged by proactive technology search, interest in

operations, willingness to experiment, and risk

acceptance. Influential factors included

recommendations from peers, departments, media,

and social platforms. Perceived cost assessed initial

and recurring expenses versus benefits.

3.3 Statistical Techniques Employed

for the Study

3.3.1 Binary Logistic Regression

The functional form of binary regression (logistic)

model is briefly described as follows:

Ln [Pi1-Pi] = β0 + β1X1+ β2X2+β3X3+ β4X4+

β5X5+ β6X6+β7X7 + β8X8 + β9X9

+β10X10+β11X11+β12X12+β13X13+β14X14+β15

X15+β16X16+ β17X17+ β18X18+ β19X19+

β20X20

Where, Pi is the probability that the farmer adopted

farm level crop intelligence systems, that takes value

of 1, if adopted and 0 otherwise

𝑋𝑖 is a vector of the independent variables

hypothesized to influence the adoption decision and

these variables are Table 1 revealed that majority of

the sample farmers were in the age group of 30-45

years (47 %) had education level of degree and above

(55 %), had family size between four to six (68 %),

had more than 15 years of farming experience (46 &)

and were large farmers (53 %) with land holdings of

more than 10 acres.

X1 – Age (Categorical, with less than 30 years as reference over others

X2 – Education (Continuous variable)

X3 – Farming Experience (Continuous variable)

X4 – Farm size (Continuous variable)

X5 – Membership in farmer collectives (1 for Yes, 0, otherwise)

X6 – Leadership role (1 for Yes, 0, otherwise)

X7 – Attending farm related events (Not attending as reference over Others)

X8 – Usage of agricultural technological apps (1 for Yes, 0, otherwise

X9 – Mass media for agri information. (Newspaper as reference over radio

& television)

X10 – Social media for agri information (You Tube as reference over

WhatsApp, Facebook)

X11 – Perceived Usefulness

X12 – Perceived Ease of Use

X13 – Complexity of decision

making

X14 – Predictive Decision

making

X15 – Resource Scarcity

X16 – Farm Produce Quality

X17 – Farmer Innovativeness

X18 – Influential Factors

X19 – Facilitating Factors

X20

–

Perceived Cost

4 RESULTS AND DISCUSSION

4.1 Descriptive Statistics

Table 1: Socio-demographic characteristics of sample farmers.

S. No Age Frequency Percentage (%)

Age

Less than 30 years

30 - 45

ears 47 47

45 - 60

ears 36 36

More than 60

ears 2 2

Educational Level

Primar

Education 7 7

Factors Inﬂuencing the Adoption of IoT Based Micro Level Farm Intelligence Systems by Dryland Farmers in the State of Andhra Pradesh

Secondar

Education 30 30

Intermediate 8 8

ree and above 55 55

Family Size

1 to 3 24 24

4 to 6 68 68

7 to 9 0 0

10 & above 8 8

Farming Experience

1 to 5

ears 24 24

6 to 10

ears 27 27

11 to 15

ears 3 3

>15

ears 46 46

Farm Size

< 2.5 Acres (Mar

inal farmer) 0 0

2.5 to 5 Acres (Small farmers) 18 18

5 to 10 Acres (Medium farmers) 29 29

>10 Acres (Lar

e farmers) 53 53

4.2 Other Profile Characteristics of

Sample Respondents

Table 2 results indicated that 56% of surveyed

farmers seldom participated in agricultural events of

state departments, NGOs or financial institutions.

Only 10% were members of farmer collectives and

8% held leadership roles. About 34% had prior

experience with agricultural technology. Television

(82%) was the primary mass media source, followed

by newspapers (18%). Facebook (66%) and YouTube

(34%) were the main social media platforms for

agricultural information.

4.3 Determinants of Adoption of Farm

Level Crop Intelligence Systems by

Farmers (First Set of Factors)

Binary logistic regression employed to identify first

set of factors (Tables 4.1 & 4.2) influencing farmers

adoption of farm level crop intelligence systems. The

dependent variable is categorical and dichotomous

i.e., it takes the value of 1 for sample farmers

Table 2: Other profile characteristics of Sample Respondents.

S. No Age Frequency Percentage (%)

Farmers Participation in Farm Related

Events

Nil 23 23

Rarel

56 56

ularl

21 21

Membership in farmer collectives

(FPOs)

Yes 10 10

o9090

Leadership role played in community

Yes 8 8

o9292

Agriculture Related Technological

application usage

Yes 34 34

No 66 66

Mass media platforms as source of

agricultural information

Newspaper 18 18

Radio 0 0

TV 82 82

Social media platforms as source of

agricultural information

YouTube 34 34

WhatsApp 0 0

Faceboo

66 66

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

Table 3 : Results of Binary Logistic Regression (First Set of factors).

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 68.730 14 .000

Bloc

68.730 14 .000

Model 68.730 14 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 69.900

.497 .663

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 52.003 8 .000

Classification Table

Observed

Predicted

FLCIS ADOP Percentage Correct

O YES

Step 1

FLCIS ADOP

NO 46 4 92.0

YES 5 45 90.0

Overall Percenta

e 91.0

a. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B) 95%C.I.for EXP(B)

Lowe

Uppe

AGEGRP

12.097 3 .007***

AGEGRP (1) -28.669 28385.35 .000 1 .999 .000 .000 .

AGEGRP (2) -23.670 28385.35 .000 1 .999 .000 .000 .

AGEGRP (3) -21.722 28385.35 .000 1 .999 .000 .000 .

EDU .486 .185 6.878 1 .009* 1.625 1.131 2.336

FARMEXP -.144 .052 7.566 1 .006*** .866 .781 .959

FARMSIZE .242 .057 17.846 1 .000*** 1.274 1.139 1.425

MEMSHIP (1) 3.534 1.863 3.600 1 .050** 34.262 .890

1318.80

LEADSHIP (1) 1.251 2.295 .297 1 .586 3.494 .039 314.110

AFRE 5.451 2 .066*

AFRE (1) -3.099 1.344 5.319 1 .021** .045 .003 .628

AFRE (2) -1.921 1.153 2.774 1 .096* .146 .015 1.404

ARTP (1) 2.278 .964 5.584 1 .018** 9.761 1.475 64.593

MMP (1) 1.592 1.332 1.429 1 .232 4.913 .361 66.818

SMPF (1) .397 1.048 .143 1 .705 1.487 .191 11.597

Constant 11.216 28385.355 .000 1 1.000

74310.1

a. Variable(s) entered on step 1: AGEGRP, EDU, FARMEXP, FARMSIZE, MEMSHIP, LEADSHIP, AFRE,

ARTP, MMP, SMPF.

*** indicates 1% ; ** indicates 5 %; * indicates 10 % Significance level

Factors Inﬂuencing the Adoption of IoT Based Micro Level Farm Intelligence Systems by Dryland Farmers in the State of Andhra Pradesh

who adopted farm level crop intelligence systems and

0 otherwise. Independent variables include age,

education, farming experience, farm size,

membership, leadership, participation in farm events,

usage of agricultural apps, mass media and social

media for agricultural information. represented with

codes AGEGRP, EDU, FARMEXP, FARMSIZE,

MEMSHIP, LEADSHIP, ARTP, MMP and SMPF

respectively

The results of logit model (Table 3) showed the

model is good fit and statistically significant, as the

probability is less than 0.05 with chi square (𝜒



) value

of 52.003. The Nagelkerke R Square value explains

66.30 % of variance while classification table

indicated that the model correctly classified 91 % of

cases. The variables namely age, education, farming

experience, farm size, membership in farmer

collectives, participation in farm events/meeting and

usage of agricultural apps are key determinants

influencing farmers adoption of farm level crop

intelligence systems. Of these determinants,

education farm size, membership, and previous

experience with agricultural technological apps

increases the likelihood of adoption.The findings are

consistent with the results of Diaz et al., (2021) and

Hoang (2020) also established the positive

relationship for education and farm size with

technology adoption by farmers. Further participation

in farmer collective organizations facilitates the

exchange of information regarding the benefits of

technology adoption, which increases the probability

of adoption.

While the variables namely age, farming

experience and participation of farmers in farm events

decreases the likelihood of adoption. Farmers in the

age group of 18 to 30 years, showed increased

likelihood of adoption over other categories i.ewith

increase in age and farming experience, the farmers

will be less technology savvy. Further there might be

negative feedbacks and criticism of technology

during farmers participation in farm related

meetings/events, which might be the factor for

decreased adoption. The results are consistent with

findings of Daberkow and McBride (2003); Adrian et

al. (2005); Torrez et al. (2016) and Vecchio et al.

(2020).

4.4 Reliability Results of Data Set

The statements identified for assessment of adoption

of farm level crop level intelligence systems showed

internal consistency and reliability with Cronbach’s

alpha value above 0.70 (Cronbach and Shavelson,

2004; Table 4).

4.5 Determinants of Adoption of Farm

Level Crop Intelligence Systems

(Second Set of Factors)

Binary logistic regression was further employed to

identify second set of factors influencing the farmers

adoption of farm level crop intelligence systems.

Scores for each statement were determined based on

scale agreements, then summed to calculate total

scores for each factor. The total scores of independent

variables for the components perceived usefulness,

perceived ease of use, complex decision making,

predictive decision-making, resource scarcity,

produce quality, farmer innovativeness, influential

factor, facilitating factor and perceived cost are

represented with PUTS, PEOUTS, CDMTS,

PDMTS, RSTS, PQTS, FARMINVTTS,

INFFACTTS, FACTS, PCTS respectively.

Table 4. Results of reliability of data set.

Factors No. of statements Cronbach’s Alpha Value

Perceived usefulness 5 0.953

Perceived ease of use 4 0.919

Complex decision makin

3 0.727

Predictive decision makin

2 0.839

Resource scarcit

3 0.827

Farm produce qualit

2 0.728

Farmer innovativeness 4 0.817

Influential factors 4 0.898

Facilitatin

factors 3 0.811

Perceived cos

3 0.718

Total 33

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

Table 5. Results of Binary Logistic Regression (Second Set of factors).

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 61.831 10 .000

Bloc

61.831 10 .000

Model 61.831 10 .000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 76.798

.461 .615

Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 13.023 8 .111

Classification Table

Observed

Predicte

FLCIS ADOP Percentage Correct

O YES

Step 1

FLCIS ADOP

NO 40 10 80.0

YES 7 43 86.0

Overall Percenta

e 83.0

a. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

95% C.I.for EXP(B)

Lowe

Uppe

Step

PUTS .839 .246 11.618 1 .001*** 2.313 1.428 3.747

PEOUTS .617 .227 7.406 1 .007*** 1.854 1.189 2.893

CDMTS -.668 .374 3.201 1 .074* .513 .246 1.066

PDMTS -.039 .276 .020 1 .889 .962 .560 1.654

RSTS -.089 .279 .101 1 .751 .915 .529 1.582

PQTS -.006 .364 .000 1 .986 .994 .487 2.029

FARMINVTTS .917 .396 5.368 1 .021** 2.501 1.152 5.431

INFFACTTS .518 .244 4.525 1 .033** 1.679 1.042 2.708

FACTS .906 .354 6.566 1 .010** 2.474 1.237 4.948

PCTS -.726 .295 6.078 1 .014** .484 .272 .862

Constan

-42.618 13.106 10.574 1 .001 .000

a. Variable(s) entered on step 1: PUTS, PEOUTS, CDMTS, PDMTS, RSTS, PQTS, FARMINVTTS,

INFFACTTS, FACTS, PCTS.

. *** indicates 1% ; ** indicates 5 %; * indicates 10 % Si

nificance level

The results of logit model (Table 4) showed model is

good fit and statistically significant, as the probability

is less than 0.05 with chi square (χ^2) value of 13.023.

The Nagelkerke R Square value indicated that model

explained 61.50 % of variance and correctly

classified 83 % of the cases. The key determinants

include perceived usefulness, perceived ease of use,

farmer innovativeness, facilitating factors, influential

factors, increases the likelihood of adoption of these

systems, while perceived cost and complexity of

decision making decreases the likelihood of adoption.

Sample farmers who perceive crop intelligence

systems as facilitating timely decision-making,

resource utilization, yield enhancement, and risk

mitigation are more inclined to adopt them. Likewise,

those who find these systems easy to acquire, operate,

understand, and maintain are also likely to adopt.

Farmers who actively seek technological information,

experiment with new technologies, and accept

associated risks are more inclined towards adoption.

Moreover, those who trust recommendations from

fellow farmers, agricultural departments, media

Factors Inﬂuencing the Adoption of IoT Based Micro Level Farm Intelligence Systems by Dryland Farmers in the State of Andhra Pradesh

sources, and social media are more likely to adopt.

Perceived support from service providers,

government subsidies, and financial aid, as well as

bank linkages, also increase adoption likelihood.

Conversely, farmers who find initial costs and

ongoing expenses unjustifiable, or perceive systems

as suitable only for specific crops and climates, are

less likely to adopt. The findings align with results of

Antolini et al. (2015); Chuang et al. (2020) and Diaz

et al.(2021) supporting similar results.

5 CONCLUSION

The study aims to identify determinants of farm-level

crop intelligence system adoption among 100 dryland

farmers in climate-vulnerable Anantapur District,

Andhra Pradesh, where making the adoption of these

technologies is crucial for enabling informed

decision-making across the crop cycle. Binary

logistic regression revealed age, education, farming

experience, farm size, collective membership, farmer

participation in farm events, and app usage as crucial

determinants while age, experience, and participation

in farm events decreases the adoption likelihood.

Additionally, perceived usefulness, ease of use,

farmer innovativeness, decision complexity,

facilitating factors, influential recommendations, and

perceived costs significantly influenced the adoption.

Understanding these determinants is essential for

fostering the adoption of these systems. Tailored

strategies addressing adoption drivers, showcasing

benefits, user-friendliness and cost-effectiveness,

enabling support structures while addressing

connectivity and financial constraints are crucial.

Collaborative efforts among stakeholders, including

NGOs, agricultural departments and agri-tech

startups, are vital for promoting technology adoption

and sustainable agricultural practices in climate-

vulnerable regions like Anantapur.

AUTHOR CONTRIBUTIONS

Author

Position

Name of

the

Author

Authors

Contribution

First Author T Yamini Data Collection,

Data Validation,

Data Analysis,

Original Draft

Preparation,

Corresponding

Author

Prabhavathi

Conceptualization,

Study Design, Data

Analysis, Original

Draft Preparation,

Draft Correction

Co-Author Ch Srilatha

Vani

Draft correction

Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict

of interest

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Factors Inﬂuencing the Adoption of IoT Based Micro Level Farm Intelligence Systems by Dryland Farmers in the State of Andhra Pradesh