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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
A i icial In elligence in Pes icide Analysis: Ad ances, Challenges, and Fu u e
Di ec ions
Dipali P asad Shinde
Assis an P o esso
D .D.Y.Pa il A s, Comme ce & Science Junio College, Aku di, Pune
Co esponding Au ho – Dipali P asad Shinde
DOI - 10.5281/zenodo.17313107
Abs ac :
Pes icides a e essen ial in mode n ag icul u e bu pose isks o human heal h and he
en i onmen . T adi ional pes icide analysis echniques—such as ch oma og aphy, spec ome y, and
immunoassays—a e accu a e bu ime-consuming and cos ly. A i icial in elligence (AI), pa icula ly
machine lea ning (ML) and deep lea ning (DL), is eme ging as a ans o ma i e app oach o pes icide
de ec ion, quan i ica ion, esidue p edic ion, and isk assessmen . This pape e iews he in eg a ion o
AI wi h analy ical chemis y o pes icide moni o ing, highligh s cu en achie emen s in spec oscopy
and senso -based de ec ion, e alua es p edic i e modeling o pes icide beha io , and p oposes a
amewo k o AI-d i en isk assessmen sys ems. We also discuss limi a ions such as da a sca ci y,
in e p e abili y, and s anda diza ion issues, while ou lining u u e oppo uni ies o sus ainable, p ecise,
and eal- ime pes icide analysis.
Keywo ds: A i icial In elligence, Pes icide Analysis, Machine Lea ning, Deep Lea ning, Food
Sa e y, En i onmen al Moni o ing
In oduc ion:
Pes icides imp o e c op yield bu
lea e esidues in ood, wa e , and soil.
Con en ional me hods such as GC–MS and
LC–MS a e accu a e bu equi e ad anced
labs. AI can educe analysis ime, au oma e
da a in e p e a ion, p edic pes icide
deg ada ion, and suppo decision-making.
Analy ical Chemis y o Pes icides:
Ch oma og aphic me hods (GC–MS,
LC–MS/MS), spec oscopic me hods (NMR,
FTIR, UV-Vis, Raman), and biosenso s a e
s anda d. Howe e , hey a e cos ly and no
scalable o eal- ime applica ions.
Role o A i icial In elligence in Pes icide
Analysis:
AI enables au oma ed in e p e a ion o
ch oma og aphic and spec al da a. Machine
lea ning models like SVM and Random Fo es
a e used o classi ica ion, while CNNs
analyze GC/LC-MS peaks. AI-powe ed IoT
senso s allow eal- ime moni o ing. P edic i e
models es ima e pes icide a e and oxici y.
Case S udies :
AI combined wi h Raman
spec oscopy de ec s o ganophospha es in
ui s. CNNs imp o e GC–MS classi ica ion
accu acy. IoT + AI enables eal- ime wa e
moni o ing.
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Dipali P asad Shinde
180
Me hodology:
S ep 1: Da a collec ion → S ep 2:
P ep ocessing → S ep 3: AI Model → S ep 4:
Risk Assessmen → S ep 5: Decision Suppo .
This s udy uses a mixed expe imen al–
compu a ional app oach. Pes icide s anda ds
and ield samples (p oduce, wa e , soil) we e
analyzed by GC–MS/LC–MS o p o ide
g ound- u h concen a ions and by
Raman/FTIR and po able senso s o p oduce
spec al/ ime-se ies da ase s. Da a
p ep ocessing included baseline co ec ion,
no maliza ion, and PCA-based denoising. We
ained and compa ed classical ML models
(SVM, Random Fo es ) and deep lea ning
models (1D/2D CNNs, LSTM) using a
70/15/15 ain/ alida ion/ es spli wi h 5- old
c oss- alida ion. Model pe o mance was
e alua ed by RMSE, MAE, R² o eg ession
and by p ecision/ ecall/F1 and ROC–AUC o
classi ica ion. Explainabili y me hods (SHAP,
LIME) we e used o in e p e model
p edic ions; ield alida ion was pe o med by
compa ing on-si e p edic ions o labo a o y
GC–MS esul s.
Challenges & Limi a ions:
Challenges include da a sca ci y,
black-box in e p e abili y, lack o global
s anda ds, and ha dwa e scalabili y issues.
Fu u e Di ec ions:
Fu u e esea ch should ocus on
explainable AI, global open pes icide spec al
da abases, low-cos po able es ing de ices,
and blockchain in eg a ion o ood sa e y
aceabili y.
Conclusion:
AI can e olu ionize pes icide analysis
by enhancing speed, accu acy, and scalabili y
beyond adi ional me hods. Collabo a ion
be ween chemis s, AI expe s, and egula o s
is c ucial o build eliable and s anda dized
sys ems.
Re e ences:
1. Liu, Y., Zhang, H., & Chen, W. (2023).
AI-assis ed Raman spec oscopy o
pes icide de ec ion. Analy ica Chimica
Ac a.
2. Wang, J., Li, P., & Zhao, X. (2022).
Deep lea ning o GC-MS-based
pes icide esidue analysis. Jou nal o
Ag icul u al and Food Chemis y.
3. Singh, R., Kuma , V., & Meh a, S.
(2024). Machine lea ning in pes icide
esidue moni o ing: A e iew. Food
Chemis y.
4. Sha ma, P., Gup a, A., & Rao, D.
(2023). IoT and AI in pes icide
de ec ion o sma ag icul u e. Senso s.
5. Zhang, L., Wei, Y., & Sun, M. (2025).
AI o p edic ing pes icide a e and
en i onmen al isk. Chemosphe e.
