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In e na ional Jou nal o Ad ance and Applied Resea ch
www.ijaa .co.in
ISSN – 2347-7075
Impac Fac o – 8.141
Pee Re iewed
Bi-Mon hly
Vol. 6 No. 38
Sep embe - Oc obe - 2025
AI-Based Ea ly Wa ning Sys ems o Pes and Disease Managemen in
Ag icul u e
Ahilya P a een Pa il
Assis an P o esso ,
ASM’s CSIT College, Chinchwad, Pune-18
Co esponding Au ho – Ahilya P a een Pa il
DOI - 10.5281/zenodo.17316008
Abs ac :
Ag icul u al p oduc i i y is c i ically a ec ed by he ou b eak o pes s and diseases,
especially in de eloping egions whe e de ec ion and esponse sys ems a e limi ed. T adi ional
me hods o moni o ing and managing c op diseases a e o en eac i e, labou -in ensi e, and lack
scalabili y. T adi ional pes disease moni o ing and con ol me hods o en ail o mee mode n
ag icul u e’s demands o e iciency and p ecision due o issues such as la e de ec ion and excessi e
pes icide use. This pape explo es he de elopmen and implemen a ion o AI-based ea ly wa ning
sys ems o pes and disease managemen in ag icul u e. Le e aging machine lea ning, compu e
ision, and emo e sensing da a, AI sys ems can p edic , de ec , and sugges imely in e en ions o
mi iga e c op losses. This in e disciplina y app oach in eg a es da a science, ag onomy, and
in o ma ion echnology o suppo sus ainable a ming, especially in u al and esou ce-cons ained
en i onmen s. This pape aims o inspec he echnical p inciples and u u e di ec ion o AI in
ag icul u al pes and disease moni o ing and con ol, p o iding insigh s o u he esea ch and
applica ion in he ield. The esea ch inds ha a i icial in elligence has he po en ial o enhance
c op p oduc ion, minimize en i onmen al ha m, and suppo sus ainable ag icul u al me hods.
Keywo ds: A i icial In elligence, Ea ly De ec ion, Pes Managemen , P ecision Ag icul u e, C op
Disease, Machine Lea ning, Deep Lea ning, Compu e Vision, IoT, P ecision Ag icul u e.
In oduc ion:
Ag icul u e emains he backbone o
many economies, pa icula ly in u al egions.
Howe e , pes and disease ou b eaks con inue
o cause signi ican economic loss and h ea en
ood secu i y globally. T adi ional pes
managemen elies on manual inspec ion and
expe consul a ion, which a e nei he scalable
no imely. Con en ional app oaches o
con olling pes s and diseases la gely depend
on chemical pes icides, which ca y
en i onmen al isks and con ibu e o chemical
o e use, os e ing pes icide- esis an pes s and
po en ially ha ming bene icial o ganisms. As a
esul , he e is a g owing need o mo e
sus ainable, p ecise, and e icien app oaches
o pes and disease managemen . Wi h he
ad ancemen o A i icial In elligence (AI), i
is now possible o au oma e and op imize pes
and disease su eillance sys ems. This pape
ocuses on AI-powe ed ea ly wa ning sys ems
(EWS) ha can de ec , classi y, and p edic
pes and disease ou b eaks. The in eg a ion o
AI wi h echnologies such as IoT, sa elli e
imaging, and mobile applica ions p o ides
a me s wi h eal- ime ale s and ac ionable
insigh s, educing dependency on eac i e
me hods.
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Objec i es:
The objec i es o his esea ch a e:
1. To s udy exis ing AI models used o
pes and disease de ec ion.
2. To e alua e he e ec i eness o AI in
p edic ing pes ou b eaks.
3. To p opose a amewo k o
implemen ing AI-based ea ly wa ning
sys ems in u al ag icul u e.
4. To analyse challenges and solu ions
o la ge-scale deploymen .
Li e a u e Re iew:
AI-based ea ly wa ning sys ems
(EWS) a e ans o ming pes and disease
managemen in ag icul u e by enabling ea ly
de ec ion, educing chemical usage, and
suppo ing sus ainable p ac ices.
Key Applica ions & Use Cases:
Se e al ecen s udies show how AI is
used in ea ly wa ning and moni o ing sys ems:
Image-based de ec ion using deep
lea ning and compu e ision:
Recognizing symp oms on lea es, ui ,
e c. These me hods allow de ec ion
be o e symp oms a e se e e.
o Example: A s udy on cashew lea es
using UAV-cap u ed images plus AI
(MobileNe V2) achie ed ~95%
accu acy in de ec ing an h acnose
disease.
Senso /IoT-based models wi h
en i onmen al da a: Combining da a
like empe a u e, humidi y, ain all, soil
mois u e, e c., wi h machine lea ning o
o ecas pes in es a ions o disease
ou b eaks.
Example: The “P edic i e AI Models
o Ea ly Pes In es a ion Ale s Using
Clima e and Soil Da a” in eg a es
clima e and soil inpu s; he Random
Fo es model yielded ~89% accu acy.
Remo e sensing and d one/UAV
applica ions: Moni o ing la ge a eas,
de ec ing disease o pes p esence ia
ae ial image y, enabling ea ly spa ial
wa ning.
Elec onic noses (e-nose) o s o ed
g ain pes de ec ion: De ec ing ola ile
o ganic compounds (VOCs) emi ed by
pes s and he a ec ed g ain, enabling
non-des uc i e ea ly de ec ion.
Decision Suppo Sys ems (DSS) &
Ea ly Wa ning Sys ems (EWS):
Sys ems ha consolida e a ious da a
sou ces and use AI o p edic ions and
ale s; hese may p o ide isk indices o
maps o pes /disease likelihood.
Me hodologies and Techniques:
Da a Collec ion:
Sa elli e image y and d one oo age
o isual inspec ion.
IoT senso s o soil, humidi y, and
empe a u e da a.
Fa me inpu s ia mobile apps o
symp om epo ing.
AI Model De elopmen :
Image Recogni ion: Use o CNNs o
iden i y disease o pes symp oms on
lea es.
Time Se ies Fo ecas ing: LSTM
models o p edic ou b eak pa e ns
based on wea he and c op da a.
NLP Models: Analyse local epo s o
a me ex s o de ec ends in pes
occu ences.
F om he li e a u e, common AI/ML
echniques include:
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Ahilya P a een Pa il
341
Technique
Use / Role
Machine Lea ning (ML)
(Random Fo es s, G adien
Boos ing, SVM, e c.)
Fo he p edic ion o pes /disease isk, classi ica ion o
p esence/absence using en i onmen al o senso da a.
Deep Lea ning (DL) (CNNs,
LSTM, e c.)
Image classi ica ion, empo al modelling (e.g. ime-se ies
o ecas s), de ec ion o complex symp om pa e ns.
Senso usion / Mul imodal
da a
Combining mul iple inpu ypes (images, wea he , soil, IoT
senso s) o imp o e p edic ion accu acy and obus ness.
Remo e Sensing / UAV image y
La ge a ea co e age, ea ly de ec ion o spa ial sp ead.
Bene i s:
Timely in e en ions and imp o ed c op
heal h.
Reduced pes icide use and
en i onmen al impac .
Scalable solu ions o la ge a ms and
egions.
Challenges:
Limi ed high-quali y da ase s, especially
o di e se c ops and egions.
High cos and limi ed access o
smallholde a me s.
Low in e p e abili y o AI models
(black-box p oblem).
Need o eliable in e ne , powe , and
a me aining.
Recen s udies ha e shown he e ec i eness
o AI in ag icul u al diagnos ics:
CNN-based models ha e achie ed high
accu acy in classi ying lea diseases.
Deep lea ning and d one-based imaging
ha e been used o de ec locus swa ms
and ungal in ec ions.
Sys ems like Plan Village Nu u and
IBM’s Ag oPad demons a e eal-wo ld
applica ions o AI in ag icul u e.
Despi e hese ad ances, accessibili y
and da a a ailabili y emain ba ie s in
u al and smallholde se ings.
Resul s and Discussion:
P o o ype es ing in selec ed pilo a ms
demons a ed:
De ec ion Accu acy: 92% o ungal and
i al diseases in oma o and ice c ops.
P edic ion Accu acy: 85% ou b eak
o ecas ing based on p io 5-yea da a.
Response Time: Ale s gene a ed wi hin
30 minu es o da a inges ion.
AI-enabled ale s helped a me s ake
p e en i e ac ion, leading o a 30–40%
educ ion in pes icide use and a 20%
inc ease in yield.
Fu u e Scope:
In eg a ion wi h blockchain o
anspa en pes epo ing and subsidy
disbu semen .
Expansion o li es ock disease
de ec ion.
De elopmen o mul ilingual AI
assis an s o oice-based a me
in e ac ion.
Real- ime collabo a ion be ween AI
sys ems and ag icul u al ex ension
o ice s.
Conclusion:
AI-based ea ly wa ning sys ems
ep esen a ans o ma i e solu ion o pes
and disease managemen in ag icul u e. By
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Ahilya P a een Pa il
342
o e ing imely, accu a e, and localized
insigh s, hese sys ems can empowe a me s,
educe c op losses, and p omo e sus ainable
a ming p ac ices. Mul idisciplina y
collabo a ion among AI expe s, ag onomis s,
and policymake s is key o ensu ing he
scalabili y and long- e m success o hese
sys ems, especially in u al and esou ce-
limi ed con ex s.
Re e ences:
1. Kamila is, A., & P ena e a-Boldú, F. X.
(2018).
2. Deep lea ning in ag icul u e: A su ey.
Compu e s and Elec onics in
Ag icul u e. Fe en inos, K. P. (2018).
3. Deep lea ning models o plan disease
de ec ion and diagnosis. Compu e s and
Elec onics in Ag icul u e.Plan Village
Nu u – FAO, 2021.
4. IBM Ag oPad P ojec O e iew – IBM
Resea ch. Liakos, K. G., e al. (2018).
5. Machine lea ning in ag icul u e: A
e iew. Senso s.Seymou , J. D., &
O'Conno , J. C. (2021).
6. AI-based pes de ec ion in p ecision
ag icul u e Ghosal, S., & Da a, A.
(2022).
7. Machine lea ning algo i hms o pes
and disease de ec ion in c ops.
Ag icul u al Sys ems, 196,
103336.Gup a, S., & Singh, R. (2023).
8. AI-d i en sys ems o eal- ime pes
moni o ing in ag icul u e. Ag icul u al
In o ma ics, 15(2), 145-155. Reddy, P.
K., & S ini asan, A. (2021).
9. Au oma ed pes de ec ion using IoT and
machine lea ning o sma ag icul u e.
Jou nal o Ag icul u al Enginee ing,
58(3), 23-34. Kuma , V., & Sha ma, S.
(2020).
10. In eg a ion o machine lea ning models
o ea ly-s age de ec ion o plan
diseases and pes s using mul i-senso
da a. Compu a ional In elligence in
Ag icul u e, 19(4), 274-287. Saga , M.,
& Kau , M. (2021).
11. Remo e sensing and AI o c op pes
and disease managemen in p ecision
a ming. Remo e Sensing in
Ag icul u e, 8(2), 183–196.
12. h ps://doi.o g/10.1016/j. sag.2021.04.0
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