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CHAPTER-10
AI FOR ENVIRONMENTAL SUSTAINABILITY
D . P adeep Pokh iyal
HoD, Compu e Science & Technology, MIT, Dhalwala, Rishikesh
P amod Kuma
Assis an P o esso , Compu e Science & Technology, OIMT, Rishikesh
Akhilesh Bijalwan
Assis an P o esso , Compu e Science & Technology, MIT Rishikesh
Abs ac
We a e a a c i ical junc u e o he plane , con on ing se e e en i onmen al
challenges including accele a ing clima e change, biodi e si y loss, esou ce
deple ion, and pe asi e pollu ion. Add essing hese complex, in e connec ed
issues equi es inno a i e, da a-d i en app oaches ha anscend adi ional
me hods.
Sol ing hese complex, in e connec ed p oblems demands inno a i e, da a-d i en
solu ions beyond adi ional app oaches. A i icial In elligence (AI), wi h i s
ema kable capabili ies in da a p ocessing ,da a analysis, pa e n ecogni ion,
p edic ion, and op imiza ion, has eme ged as powe ul ool o complemen human
e o s in achie ing en i onmen al sus ainabili y. This in oduc o y sec ion will
se he s age by ou lining he global en i onmen al c isis and emphasizing why AI
is uniquely posi ioned o con ibu e o solu ions, mo ing beyond anecdo al
e idence o highligh he g owing academic and indus ial ocus on his
in e sec ion.
Keywo ds: A i icial In elligence, En i onmen al Sus ainabili y, Machine
Lea ning, Clima e Change, Conse a ion, Resou ce Managemen , Pollu ion
Con ol, Sus ainable De elopmen Goals.
AI o imp o emen and adjus men in Clima e
Clima e change is pe haps he mos p essing en i onmen al challenge, and
Fo una ely, AI p o ides coun less ways o bo h educe i s oo causes and help us
adap o i s ine i able changes.
Emissions Moni o ing and Reduc ion:
AI o e s powe ul ools o ackling clima e change. I enables highly accu a e
moni o ing o g eenhouse gas (GHG) emissions by analyzing sa elli e image y
and senso da a. This includes pinpoin ing me hane leaks, acking de o es a ion
a es, and p o iding de ailed insigh s in o ca bon oo p in s ac oss a ious sou ces
like indus ial acili ies, ag icul u e, and land use changes.
Away om moni o ing, AI is e olu ionizing ene gy sys ems. I can o esee
ene gy demand, lawlessly pu oge he enewable ene gy sou ces like sola and
wind in o g ids, and e icien ly manage ene gy s o age. Machine lea ning models,
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o ins ance, can o ecas wind speeds and sola i adiance, leading o mo e
e ec i e powe dis ibu ion and a educed dependence on ossil uels.
Fu he mo e, AI ex ensi ely imp o es indus ial e iciency. F om sma
manu ac u ing o op imizing complex supply chains, AI-d i en solu ions educe
ene gy u iliza ion and was e gene a ion, ul ima ely lowe ing ca bon oo p in s
c osswise a ious indus ies.
AI algo i hms can op imize a ic low, ou e planning, and public ansi
ne wo ks o educe uel consump ion and emissions. This includes de eloping
mo e e icien au onomous ehicles and sma ci y in as uc u e.
Clima e Modeling and P edic ion:
AI signi ican ly enhances ou abili y o adap o a changing clima e by imp o ing
bo h p edic ion and assessmen . Enhanced clima e models, pa icula ly hose
le e aging deep lea ning, o e g ea e accu acy and esolu ion. This leads o mo e
p ecise p edic ions o wea he pa e ns, ex eme e en s like loods, d ough s, and
wild i es, and long- e m clima e ajec o ies, ul ima ely bols e ing p epa edness
and ea ly wa ning sys ems.
Concu en ly, AI acili a es obus clima e isk assessmen . By analyzing as
his o ical and eal- ime da ase s, AI can e ec i ely e alua e clima e- ela ed isks
o communi ies, in as uc u e, and ecosys ems, p o iding c ucial insigh s o
de eloping in o med adap i e s a egies.
AI o Biodi e si y and Ecosys em Conse a ion
The apid decline in biodi e si y poses an exis en ial h ea . AI is inc easingly
deployed o enhance conse a ion e o s, o en by o e coming he limi a ions o
manual da a collec ion and analysis.
Species Moni o ing and Iden i ica ion:
• Au oma ed Wildli e T acking: AI-powe ed compu e ision and acous ic
moni o ing sys ems can iden i y and ack indi idual animals om came a
aps, audio eco dings, and d one oo age, p o iding c ucial da a on
popula ion dynamics, beha io , and mo emen pa e ns.
• En i onmen al DNA (eDNA) Analysis: Machine lea ning can analyze
eDNA da a o de ec he p esence o species in aqua ic and e es ial
en i onmen s, o e ing a non-in asi e and e icien me hod o biodi e si y
su eys.
• Th ea De ec ion: AI can iden i y poaching ac i i ies, illegal logging, and
habi a des uc ion by analyzing sa elli e image y, d one oo age, and senso
da a, enabling apid in e en ion.
Habi a Assessmen and Res o a ion:
• Remo e Sensing o Habi a Mapping: AI can p ocess sa elli e and ae ial
image y o map and moni o changes in habi a s, iden i y a eas o deg ada ion,
and assess he e ec i eness o es o a ion e o s.
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• P edic i e Modeling o Conse a ion Planning:Machine lea ning models
can p edic a eas a high isk o de o es a ion o habi a loss, allowing
conse a ionis s o p io i ize in e en ions and op imize esou ce alloca ion.
AI o Resou ce Managemen and Ci cula Economy
E icien esou ce managemen is undamen al o sus ainabili y. AI o e s
solu ions o op imizing esou ce use, minimizing was e, and os e ing ci cula
economic models.
Wa e Resou ce Managemen :
Wa e Demand P edic ion and Leak De ec ion: AI algo i hms can analyze
his o ical wa e usage pa e ns and en i onmen al da a o p edic demand,
op imize wa e dis ibu ion, and de ec leaks in wa e in as uc u e, signi ican ly
educing wa e loss.
Wa e quali y
Wa e quali y is a undamen al aspec o en i onmen al heal h and human well-
being. T adi ional me hods o moni o ing wa e quali y, which o en in ol e
manual sampling and labo a o y analysis, can be ime-consuming, labo -
in ensi e, and p o ide only snapsho s o wa e condi ions. This is whe e AI-
powe ed solu ions o e a signi ican leap o wa d.
He e's an elabo a ion on how AI enhances wa e quali y moni o ing:
• Real- ime Da a Collec ion h ough Sma Senso s: A he hea o AI-powe ed
wa e quali y moni o ing a e ad anced senso s. These senso s, o en
in eg a ed wi h he In e ne o Things (IoT), a e deployed in a ious wa e
bodies – i e s, lakes, ese oi s, ea men plan s, and e en dis ibu ion
ne wo ks. They con inuously collec da a on a wide ange o pa ame e s,
including:
• Physical pa ame e s: Tempe a u e, pH, u bidi y (cloudiness), dissol ed
oxygen le els.
• Chemical pa ame e s: P esence o speci ic pollu an s like hea y me als,
ni a es, phospha es, chlo ine, and o ganic compounds.
• Biological pa ame e s: Indica o s o ha m ul algal blooms o bac e ial
con amina ion. These senso s ansmi da a wi elessly, o en o cloud-based
pla o ms, enabling con inuous, au oma ed su eillance.
• AI o Ad anced Da a Analysis and Pa e n Recogni ion: The shee olume
o eal- ime da a gene a ed by hese senso s would be o e whelming o
human analysis. This is whe e AI, pa icula ly machine lea ning and deep
lea ning algo i hms, excels. AI algo i hms can:
• Iden i y ends and anomalies: They can quickly de ec sub le changes o
sudden spikes in wa e quali y pa ame e s ha migh indica e pollu ion e en s
o con amina ion. Fo example, a sudden d op in dissol ed oxygen could
signal an indus ial discha ge o an algal bloom.
• P edic u u e wa e quali y: By analyzing his o ical da a, wea he pa e ns,
land use, and o he en i onmen al a iables, AI models can p edic po en ial
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con amina ion e en s o changes in wa e quali y be o e hey occu . This
allows o p oac i e measu es, such as p edic ing ha m ul algal blooms weeks
in ad ance.
• Pinpoin pollu ion sou ces: By co ela ing da a om mul iple senso s ac oss a
wa e body o ne wo k, AI can help ace he o igin o pollu ion, aiding in
emedia ion e o s.
• Au oma ed classi ica ion: AI can classi y wa e quali y in o di e en
ca ego ies (e.g., "excellen ," "good," "poo ") based on p ede ined s anda ds
and he collec ed da a, simpli ying assessmen .
• Timely In e en ions and P oac i e Managemen : The eal- ime insigh s
p o ided by AI a e c i ical o enabling imely in e en ions. When a pollu an
is de ec ed o p edic ed, au ho i ies can:
• Issue immedia e ale s: In o ming he public abou unsa e swimming
condi ions o po en ial d inking wa e con amina ion.
• Mobilize esponse eams: Dispa ching pe sonnel o in es iga e he sou ce o
pollu ion and ini ia e cleanup ope a ions.
• Adjus wa e ea men p ocesses: Op imizing chemical dosages o il a ion
me hods in wa e ea men plan s o e ec i ely emo e newly de ec ed
con aminan s.
• Implemen p e en a i e measu es: Based on p edic i e analy ics, measu es
like adjus ing ag icul u al p ac ices o educe nu ien uno o managing dam
eleases o p e en looding can be aken.
• Cos -E ec i eness and Scalabili y: Compa ed o adi ional manual sampling,
AI-d i en moni o ing sys ems can signi ican ly educe cos s associa ed wi h
labo , eagen s, and labo a o y analysis. Thei au oma ed na u e allows o
con inuous moni o ing ac oss la ge a eas wi h ewe human esou ces,
making hem highly scalable o widesp ead deploymen .
In essence, AI ans o ms wa e quali y moni o ing om a eac i e, snapsho -
based app oach o a p oac i e, eal- ime, and da a-d i en sys em. This empowe s
en i onmen al manage s, public heal h o icials, and e en he gene al public wi h
he in o ma ion needed o sa egua d wa e esou ces and ensu e access o sa e and
clean wa e .
Was e Managemen and Recycling:
Sma Was e So ing" ep esen s a pi o al a ea whe e a i icial in elligence is
ans o ming was e managemen . T adi ionally, ecycling acili ies (Ma e ial
Reco e y Facili ies, o MRFs) elied hea ily on manual labo o so incoming
mixed was e. This p ocess was o en slow, ine icien , cos ly, and posed heal h
isks o wo ke s. The in oduc ion o AI, pa icula ly h ough obo ics and
compu e ision sys ems, has d ama ically imp o ed his.
He e's an elabo a ion on how sma was e so ing wo ks and i s bene i s:
• The Challenge o Mixed Was e: When you pu you ecyclables in o a single
bin (single-s eam ecycling), hey' e all mixed oge he . This mixed s eam,
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o en con aining a ious ypes o plas ics, pape s, me als, glass, and e en
con aminan s, needs o be sepa a ed in o dis inc ma e ial ca ego ies o be
p ope ly ecycled. Manual so ing is p one o e o and can' keep up wi h he
shee olume and speed o mode n was e s eams.
• AI-Powe ed Compu e Vision: The "Eyes" o he Sys em:
• High-Speed Imaging: As was e a els along con eyo bel s a high speeds,
specialized came as con inuously cap u e images and ideos o he ma e ials.
• Objec Recogni ion: Compu e ision algo i hms, ained on as da ase s o
was e ma e ials, analyze hese images in eal- ime. They can ins an ly iden i y
di e en ypes o plas ics (e.g., PET, HDPE, PVC), a ious g ades o pape
and ca dboa d, di e en me als (aluminum, s eel), and glass, e en
dis inguishing be ween di e en colo s o o ms.
• Fea u e Ex ac ion: The AI doesn' jus ecognize he objec ; i ex ac s
ea u es like shape, size, colo , ex u e, and e en anspa ency, which a e
c ucial o accu a e classi ica ion.
• Con aminan De ec ion: C ucially, compu e ision can also iden i y non-
ecyclable i ems o con aminan s (like ood was e, ex iles, o haza dous
ma e ials) ha can comp omise he quali y o ecyclable bales.
• Robo ics: The "Hands" o he Sys em:
• P ecision Picking: Once he AI's compu e ision sys em iden i ies and
classi ies a ma e ial on he con eyo bel , i sends a signal o an indus ial
obo ic a m. These obo s a e equipped wi h a ious g ippe s, suc ion cups, o
o he end-e ec o s designed o p ecisely pick up and mo e he iden i ied i em.
• High-Speed Ope a ion: Robo ic so e s can ope a e con inuously a speeds a
exceeding human capabili ies, o en pe o ming dozens o hund eds o picks
pe minu e.
• Consis ency and Endu ance: Unlike human so e s who expe ience a igue
and equi e b eaks, obo s can wo k i elessly a ound he clock wi h consis en
accu acy, signi ican ly inc easing h oughpu and e iciency.
• Haza dous Ma e ial Handling: Robo s can sa ely handle sha p objec s, b oken
glass, o po en ially haza dous ma e ials, p o ec ing human wo ke s om
inju y.
Key Bene i s o Sma Was e So ing:
• Inc eased Recycling Ra es: By accu a ely iden i ying and sepa a ing mo e
ma e ials, AI sys ems maximize he amoun o was e ha can be ecycled,
di e ing i om land ills.
• Reduced Land ill Was e: The di ec consequence o highe ecycling a es is
a signi ican educ ion in he olume o was e sen o land ills, conse ing
land ill space and mi iga ing en i onmen al impac s.
• Imp o ed Pu i y o Recycled Ma e ials: AI's p ecision in so ing leads o
cleane , less con amina ed s eams o ecyclable ma e ials. This highe pu i y

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commands be e p ices in he ma ke and makes he ecycling p ocess mo e
e icien o downs eam manu ac u e s.
• Cos Sa ings: While he ini ial in es men in AI obo ics can be subs an ial,
he long- e m cos sa ings om educed labo , inc eased e iciency, and
highe - alue ecycled ma e ials a e signi ican .
• Enhanced Wo ke Sa e y: Au oma ing he mos dange ous and epe i i e
so ing asks emo es human wo ke s om haza dous en i onmen s,
imp o ing o e all sa e y in ecycling acili ies.
• Da a-D i en Op imiza ion: AI sys ems can collec and analyze da a on was e
composi ion, so ing e iciency, and ope a ional pe o mance. This da a
p o ides aluable insigh s o MRF ope a o s o u he op imize hei
p ocesses, iden i y bo lenecks, and adap o changing was e s eams.
In essence, sma was e so ing le e ages he powe o AI o ans o m ine icien ,
labo -in ensi e ecycling in o a highly au oma ed, p ecise, and economically
iable p ocess, playing a c ucial ole in ad ancing he ci cula economy.
Was e Managemen
Was e Gene a ion Fo ecas ing" is a c ucial applica ion o AI ha empowe s
municipali ies and was e managemen companies o ope a e mo e e icien ly,
sus ainably, and cos -e ec i ely. Ins ead o elying on his o ical a e ages o
educa ed guesses, AI enables p ecise p edic ions o how much was e will be
gene a ed, whe e, and when.
He e's an elabo a ion on his concep :
• The Need o P edic ion: Was e gene a ion is a dynamic p ocess in luenced
by a mul i ude o ac o s. Wi hou accu a e o ecas s, municipali ies ace
signi ican challenges:
• Ine icien Collec ion Rou es: T ucks migh isi bins ha a e nea ly emp y,
was ing uel and labo , o miss bins ha a e o e lowing, leading o li e and
public heal h issues.
• Subop imal Resou ce Alloca ion: Di icul y in de e mining he igh numbe
o collec ion ehicles, s a , o he capaci y needed o p ocessing acili ies
(e.g., ecycling plan s, compos ing acili ies, land ills).
• Inc eased Ope a ional Cos s: Unnecessa y ips, o e ime pay o eme gency
collec ions, and po en ial ines o en i onmen al non-compliance.
• En i onmen al Impac : Highe uel consump ion om ine icien ou es
con ibu es o g eenhouse gas emissions.
• How AI P edic s Was e Gene a ion: AI, pa icula ly machine lea ning and
deep lea ning algo i hms, excels a iden i ying complex pa e ns and
ela ionships wi hin as da ase s. Fo was e gene a ion o ecas ing, AI models
a e ained on a wide a ay o his o ical and eal- ime da a, including:
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• His o ical Was e Da a: Volumes and ypes o was e collec ed o e days,
weeks, mon hs, and yea s om speci ic neighbo hoods, dis ic s, o e en
indi idual sma bins.
• Demog aphic Da a: Popula ion densi y, household size, age dis ibu ion, and
changes in popula ion.
• Socio-economic Fac o s: Income le els, consump ion pa e ns, economic
ac i i y (e.g., e ail sales da a), and ou ism.
• Seasonal and Calenda ic Fac o s: Holidays, es i als (which o en lead o
spikes in was e), aca ion pe iods, and school schedules.
• Wea he Da a: Tempe a u e, p ecipi a ion ( ain o snow can impac ou doo
ac i i ies and was e gene a ion).
• Special E en s: Conce s, spo ing e en s, public ga he ings ha gene a e
localized su ges in was e.
• Policy Changes: Implemen a ion o new ecycling p og ams, plas ic bag bans,
o pay-as-you- h ow schemes.
• Senso Da a om Sma Bins: Real- ime ill le els om bins equipped wi h
IoT senso s p o ide immedia e da a on was e accumula ion.
• AI's Analy ical Capabili ies: Pa e n Recogni ion: AI algo i hms can de ec
sub le, ecu ing pa e ns ha migh be in isible o human analysis, such as
daily luc ua ions, weekly cycles, and annual ends.
• Co ela ion Iden i ica ion: They can iden i y how di e en inpu a iables
(e.g., a public holiday coinciding wi h a ho spell) in luence was e gene a ion.
• Time-Se ies Analysis: Many AI models a e speci ically designed o analyze
ime-se ies da a, making hem highly e ec i e a p edic ing u u e alues
based on pas obse a ions.
• Anomaly De ec ion: AI can lag unusual was e gene a ion pa e ns ha de ia e
om p edic ions, po en ially indica ing an unexpec ed e en o an issue wi h
he collec ion sys em.
• Bene i s o Municipali ies and Was e Managemen S a egies:
• Op imized Collec ion Rou es: Wi h accu a e o ecas s, municipali ies can
dynamically adjus collec ion schedules and ou es. Ins ead o ixed ou es,
ucks can be dispa ched only when bins in speci ic a eas a e nea ing ull
capaci y, signi ican ly educing uel consump ion, emissions, and ope a ional
cos s. This also minimizes collec ion noise and a ic dis up ion.
• E icien Resou ce Alloca ion: Fo ecas s enable be e planning o ehicle
lee s, s a ing le els, and he capaci y o p ocessing acili ies. Fo ins ance,
knowing ha was e gene a ion will inc ease be o e a majo es i al allows o
p e-posi ioning addi ional bins and scheduling ex a collec ions.
• P oac i e In as uc u e Planning: Long- e m was e gene a ion o ecas s
in o m decisions abou in es ing in new ecycling cen e s, compos ing
acili ies, o land ill expansion, ensu ing ha in as uc u e mee s u u e
demand.
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• Imp o ed Public Se ices: Reduced o e lows mean cleane s ee s and public
spaces, enhancing u ban aes he ics and public heal h.
• Enhanced En i onmen al Pe o mance: Op imized ou es lead o lowe ca bon
oo p in s. Be e unde s anding o was e s eams suppo s mo e e ec i e
ecycling and was e di e sion p og ams.
• Da a-D i en Policy Making: Insigh s om AI o ecas ing can in o m he
de elopmen o new was e managemen policies, public awa eness
campaigns, and incen i es o was e educ ion.
• Cos Sa ings: Ul ima ely, he e iciency gained h ough op imized ope a ions
ansla es in o subs an ial inancial sa ings o municipali ies and axpaye s.
In summa y, AI-powe ed was e gene a ion o ecas ing ans o ms was e
managemen om a eac i e sys em in o a p oac i e, da a-d i en ope a ion,
leading o mo e sus ainable, economical, and en i onmen ally iendly u ban
en i onmen s.
Sus ainable Ag icul u e:
P ecision Fa ming:P ecision a ming, also known as p ecision ag icul u e,
ep esen s a pa adigm shi in how we cul i a e c ops. Mo ing away om a "one-
size- i s-all" app oach o ield managemen , i le e ages ad anced echnologies,
pa icula ly A i icial In elligence (AI), o manage a ms a a highly localized and
p ecise le el. The co e idea is o apply he igh amoun o inpu s (wa e , e ilize ,
pes icides) a he igh ime and in he igh place, based on eal- ime da a and
speci ic c op needs.
He e's an elabo a ion on how AI enables p ecision a ming:
Da a Collec ion and In eg a ion: The Founda ion o P ecision
A he hea o AI-d i en p ecision a ming is he collec ion o as and di e se
da ase s. This da a comes om nume ous sou ces:
Senso s:
• Soil Senso s: Embedded in he g ound, hese measu e mois u e le els, nu ien
con en (ni ogen, phospho us, po assium), pH, empe a u e, and o ganic
ma e con en .
• C op Senso s: Moun ed on d ones, ac o s, o sa elli es, hese use
mul ispec al o hype spec al imaging o assess c op heal h, de ec s ess
( om wa e , nu ien s, o pes s), and measu e chlo ophyll con en .
• Wea he S a ions: On- a m o egional s a ions p o ide eal- ime da a on
empe a u e, humidi y, ain all, wind speed, and sola adia ion.
• IoT De ices: Ne wo ked de ices moni o ing e e y hing om i iga ion
sys em p essu e o equipmen pe o mance.
• Sa elli e Image y and D ones: P o ide high- esolu ion ae ial iews o ields,
e ealing a ia ions in c op igo , opog aphy, and po en ial p oblem a eas
ha migh be in isible om he g ound.
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• His o ical Da a: Pas yield da a, soil analysis epo s, ain all eco ds, and
disease ou b eaks con ibu e o he AI's lea ning.
• Geospa ial Da a: GPS echnology allows o p ecise mapping o ields,
enabling a iable- a e applica ion o inpu s.
AI-D i en Analy ics: T ans o ming Da a in o Ac ionable Insigh s
This is whe e AI uly shines. Machine lea ning and deep lea ning algo i hms
p ocess he immense olume o collec ed da a o iden i y pa e ns, make
p edic ions, and gene a e ecommenda ions:
• P edic i e Modeling: AI models can p edic :
• Op imal Plan ing Times: Based on soil empe a u e, mois u e, and long- e m
wea he o ecas s.
• Disease and Pes Ou b eaks: By analyzing clima e da a, his o ical ou b eaks,
and ea ly signs o plan s ess, AI can ale a me s o po en ial in es a ions,
allowing o p oac i e, a ge ed in e en ion.
• Yield Fo ecas s: Mo e accu a ely p edic ha es yields based on cu en
g owing condi ions and his o ical pe o mance.
• Pa e n Recogni ion: AI can de ec sub le a ia ions in soil composi ion o
c op heal h ac oss a ield ha human obse a ion migh miss, leading o mo e
a ge ed in e en ions.
• Decision Suppo Sys ems: AI in e aces p o ide a me s wi h clea ,
ac ionable ecommenda ions. Fo example, a dashboa d migh show a map o
he ield highligh ing a eas needing mo e wa e , speci ic nu ien de iciencies,
o zones wi h po en ial pes ac i i y.
Op imiza ion o Key Ag icul u al P ac ices:
AI's insigh s di ec ly ansla e in o op imized esou ce managemen :
Op imizing I iga ion:
• P oblem: O e -i iga ion was es wa e and can leach nu ien s; unde -
i iga ion s esses c ops and educes yields.
• AI Solu ion: By combining eal- ime soil mois u e da a wi h wea he o ecas s
and c op wa e demand models, AI can c ea e a iable- a e i iga ion maps.
This means only speci ic zones o a ield ecei e wa e , and only he p ecise
amoun needed, deli e ed h ough sma i iga ion sys ems (e.g., d ip
i iga ion o p ecision sp inkle s). This d as ically educes wa e consump ion.
Op imizing Fe iliza ion:
• P oblem: Blanke applica ion o e ilize s leads o nu ien uno , pollu ing
wa e ways, and was ed esou ces.
• AI Solu ion: Soil senso da a, coupled wi h c op nu ien up ake models and
yield goals, allows AI o gene a e a iable- a e e iliza ion p esc ip ions.
Sp eade s equipped wi h GPS and AI-con olled sys ems can hen apply
di e en amoun s o e ilize o di e en pa s o he ield, ensu ing c ops ge
exac ly wha hey need, minimizing excess and en i onmen al impac .