{"id":6762,"date":"2023-05-17T23:45:08","date_gmt":"2023-05-17T21:45:08","guid":{"rendered":"https:\/\/geopard.tech\/?p=6762"},"modified":"2024-11-12T18:47:41","modified_gmt":"2024-11-12T17:47:41","slug":"automatizirani-model-detekcije-granica-polja-precizna-poljoprivreda","status":"publish","type":"post","link":"https:\/\/geopard.tech\/hr\/blog\/automated-field-boundaries-detection-model-precision-agriculture\/","title":{"rendered":"Model za automatsko otkrivanje granica polja za preciznu poljoprivredu tvrtke GeoPard"},"content":{"rendered":"

GeoPard je uspje\u0161no razvio model za automatsko prepoznavanje granica polja koriste\u0107i vi\u0161egodi\u0161nje satelitske snimke, precizno prepoznavanje oblaka i sjena te napredne vlasni\u010dke algoritme, uklju\u010duju\u0107i duboke neuronske mre\u017ee.<\/p>\n

GeoPard model za detekciju polja postigao je vrhunsku to\u010dnost od 0,975 na meti Intersection over Union (IoU)<\/strong>, validirana diljem raznolikih regija i vrsta usjeva na globalnoj razini.<\/p>\n

Pogledajte ove slike kako biste vidjeli rezultate u Njema\u010dkoj (prosje\u010dna veli\u010dina polja je 7 hektara):<\/p>\n

\"1<\/p>\n

1 \u2013 Sirova Sentinel-2 snimka<\/p>\n

\"3<\/p>\n

2 \u2013 Super-rezolucijska slika Sentinel-2 tvrtke GeoPard (rezolucija 1 metar)<\/p>\n

\"2<\/p>\n

3 \u2013 Segmentirane granice polja, 0.975 <\/strong>Metrika to\u010dnosti Intersekcija nad unijom (IoU), <\/strong>diljem vi\u0161e me\u0111unarodnih regija i vrsta usjeva.<\/p>\n

\n

Integracija u na\u0161 API i GeoPard aplikaciju dolazi uskoro. Ova automatizirana i isplativa metoda poma\u017ee u predvi\u0111anju prinosa, koristi vladinim organizacijama i poma\u017ee velikim zemljoposjednicima kojima je \u010desto potrebno a\u017eurirati granice parcela izme\u0111u sezona.<\/p>\n

GeoPardov pristup koristi vi\u0161egodi\u0161nji trendovi vegetacije usjeva<\/a> kori\u0161tenjem vi\u0161efaktorske analize i plodoreda.<\/p>\n

<\/p>\n

 <\/p>\n

Modelu se pristupa putem GeoPard API<\/a> na osnovi pla\u0107anja po kori\u0161tenju, nude\u0107i fleksibilnost bez potrebe za skupim pretplatama.<\/p>\n

 <\/p>\n

Definiranje granica polja<\/h2>\n

Razgrani\u010denje poljoprivrednih \u010destica odnosi se na proces identificiranja i mapiranja granica poljoprivrednih polja ili zemlji\u0161nih parcela. Uklju\u010duje kori\u0161tenje razli\u010ditih tehnika i izvora podataka za odre\u0111ivanje granica pojedina\u010dnih polja ili poljoprivrednih parcela.<\/p>\n

Tradicionalno, granice polja su ru\u010dno odre\u0111ivali poljoprivrednici ili zemljoposjednici na temelju svog znanja i zapa\u017eanja.<\/p>\n

Me\u0111utim, s napretkom u tehnologiji, posebno u daljinskom istra\u017eivanju i geografskim informacijskim sustavima (GIS), automatizirane i poluautomatizirane metode postale su sve ra\u0161irenije.<\/p>\n

Jedan uobi\u010dajeni pristup je analiza satelitskih ili zra\u010dnih snimaka. Snimke visoke rezolucije snimljene satelitima ili zrakoplovima mogu pru\u017eiti detaljne informacije o krajoliku, uklju\u010duju\u0107i granice izme\u0111u razli\u010ditih zemlji\u0161nih \u010destica.<\/p>\n

Algoritmi za obradu slika mogu se primijeniti na ove slike kako bi se otkrili razli\u010diti zna\u010dajke poput promjena u vrsti vegetacije, boji, teksturi ili uzorcima koji ukazuju na prisutnost granica polja.<\/p>\n

Druga tehnika uklju\u010duje kori\u0161tenje LiDAR (Light Detection and Ranging) podataka, koji pomo\u0107u laserskih zraka mjeri udaljenost izme\u0111u senzora i Zemljine povr\u0161ine.<\/p>\n

LiDAR podaci mogu pru\u017eiti detaljne podatke o nadmorskoj visini i topografiji, omogu\u0107uju\u0107i prepoznavanje suptilnih varijacija u terenu koje mogu odgovarati granicama polja.<\/p>\n

Dodatno, geografski informacijski sustavi (GIS) igraju klju\u010dnu ulogu u odre\u0111ivanju granica polja.<\/p>\n

GIS softver omogu\u0107uje integraciju i analizu razli\u010ditih slojeva podataka, uklju\u010duju\u0107i satelitske snimke, topografske karte, evidenciju vlasni\u0161tva zemlji\u0161ta i druge relevantne informacije. Kombiniranjem ovih izvora podataka, GIS mo\u017ee pomo\u0107i u interpretaciji i identifikaciji granica polja.<\/p>\n

To\u010dno razgrani\u010denje polja klju\u010dno je iz nekoliko razloga. Omogu\u0107uje bolje upravljanje poljoprivrednim resursima, omogu\u0107uje tehnike precizne poljoprivrede te podupire planiranje i provedbu poljoprivrednih praksi poput navodnjavanja, gnojidbe i suzbijanja \u0161tetnika.<\/p>\n

To\u010dni podaci o granicama parcela tako\u0111er poma\u017eu u upravljanju zemlji\u0161tem, planiranju kori\u0161tenja zemlji\u0161ta i uskla\u0111enosti s poljoprivrednim propisima.<\/p>\n

Kako je to korisno?<\/h2>\n

Igra klju\u010dnu ulogu u poljoprivredi i upravljanju zemlji\u0161tem, pru\u017eaju\u0107i nekoliko prednosti i va\u017enosti potkrijepljenih dokazima i globalnim podacima. Evo nekih klju\u010dnih to\u010daka:<\/p>\n

1. Precizna poljoprivreda:<\/strong> Precizne granice polja poma\u017eu u implementaciji tehnika precizne poljoprivrede, gdje se resursi poput vode, gnojiva i pesticida precizno usmjeravaju na odre\u0111ena podru\u010dja unutar polja.<\/p>\n

Prema izvje\u0161\u0107u Svjetske banke, tehnologije precizne poljoprivrede imaju potencijal pove\u0107ati prinose usjeva za 20%i smanjiti tro\u0161kove ulaznih sirovina za 10-20% .<\/p>\n

2. U\u010dinkovito upravljanje resursima:<\/strong> Omogu\u0107uje poljoprivrednicima bolje upravljanje resursima optimizacijom sustava navodnjavanja, prilagodbom praksi gnojenja i pra\u0107enjem zdravlja usjeva. Ova preciznost smanjuje rasipanje resursa i utjecaj na okoli\u0161.<\/p>\n

Organizacija za hranu i poljoprivredu (FAO) procjenjuje da prakse precizne poljoprivrede mogu smanjiti potro\u0161nju vode za 20-50%, smanjiti potro\u0161nju gnojiva za 10-20%i smanjiti kori\u0161tenje pesticida za 20-30%.<\/p>\n

3. Prostorno planiranje:<\/strong> To\u010dni podaci o granicama poljoprivrednih parcela klju\u010dni su za planiranje kori\u0161tenja zemlji\u0161ta, osiguravaju\u0107i u\u010dinkovito kori\u0161tenje dostupnog poljoprivrednog zemlji\u0161ta. Omogu\u0107uje kreatorima politika i upraviteljima zemlji\u0161ta dono\u0161enje informiranih odluka u vezi s raspodjelom zemlji\u0161ta, plodoredom i zoniranjem.<\/p>\n

Ovo mo\u017ee dovesti do pove\u0107ane poljoprivredne produktivnosti i pobolj\u0161ane sigurnosti hrane. Studija objavljena u Journal of Soil and Water Conservation otkrila je da bi u\u010dinkovito planiranje kori\u0161tenja zemlji\u0161ta moglo pove\u0107ati globalnu proizvodnju hrane za 20-67%.<\/p>\n

4. Poljoprivredne subvencije i osiguranje:<\/strong> Mnoge zemlje pru\u017eaju poljoprivredne subvencije i programe osiguranja temeljene na granicama parcela. Precizno razgrani\u010denje poma\u017ee u utvr\u0111ivanju podobnih povr\u0161ina, osiguravanju pravedne raspodjele subvencija i to\u010dnom izra\u010dunu premija osiguranja.<\/p>\n

Na primjer, Zajedni\u010dka poljoprivredna politika (ZPP) Europske unije oslanja se na to\u010dne granice parcela za izra\u010dun subvencija i pra\u0107enje uskla\u0111enosti.<\/p>\n

5. Upravljanje zemlji\u0161tem i pravne granice:<\/strong> Odre\u0111ivanje granica poljoprivrednih parcela klju\u010dno je za upravljanje zemlji\u0161tem, imovinska prava i rje\u0161avanje zemlji\u0161nih sporova. To\u010dne karte granica parcela poma\u017eu u utvr\u0111ivanju pravnog vlasni\u0161tva, podupiru sustave zemlji\u0161nih knjiga i olak\u0161avaju transparentne zemlji\u0161ne transakcije.<\/p>\n

Svjetska banka procjenjuje da samo 30% svjetske populacije ima zakonski dokumentirana prava na svoju zemlju, \u0161to nagla\u0161ava va\u017enost pouzdanih podataka o granicama zemlji\u0161ta za sigurno vlasni\u0161tvo nad zemljom.<\/p>\n

6. Uskla\u0111enost i ekolo\u0161ka odr\u017eivost:<\/strong> Precizne granice polja poma\u017eu u pra\u0107enju uskla\u0111enosti, osiguravaju\u0107i pridr\u017eavanje ekolo\u0161kih propisa i odr\u017eivih poljoprivrednih praksi.<\/p>\n

Poma\u017ee u identificiranju tampon zona, za\u0161ti\u0107enih podru\u010dja te podru\u010dja sklonih eroziji ili zaga\u0111enju vode, omogu\u0107uju\u0107i poljoprivrednicima da poduzmu odgovaraju\u0107e mjere. Uskla\u0111enost s ekolo\u0161kim standardima pospje\u0161uje odr\u017eivost i smanjuje negativne u\u010dinke na ekosustave.<\/p>\n

Prema FAO-u, odr\u017eive poljoprivredne prakse mogu smanjiti emisije stakleni\u010dkih plinova do 6 milijardi tona godi\u0161nje.<\/p>\n

Ove to\u010dke ilustriraju njegovu korisnost i va\u017enost u poljoprivredi i upravljanju zemlji\u0161tem. Predo\u010deni dokazi i globalni podaci podupiru pozitivne u\u010dinke koje mo\u017ee imati na u\u010dinkovitost resursa, planiranje kori\u0161tenja zemlji\u0161ta, pravne okvire, odr\u017eivost okoli\u0161a i ukupnu poljoprivrednu produktivnost.<\/p>\n

Ukratko, odre\u0111ivanje granica poljoprivrednih povr\u0161ina je proces identifikacije i mapiranja granica poljoprivrednih polja ili parcela. Oslanja se na razne tehnike poput analize satelitskih snimaka, LiDAR podataka i GIS-a za to\u010dno definiranje i razgrani\u010denje tih granica, omogu\u0107avaju\u0107i u\u010dinkovito upravljanje zemlji\u0161tem i poljoprivredne prakse.<\/p>","protected":false},"excerpt":{"rendered":"

GeoPard je uspje\u0161no zavr\u0161io razvoj automatiziranog modela detekcije granica polja koriste\u0107i vi\u0161egodi\u0161nje satelitske snimke, precizno otkrivanje oblaka i sjena te naprednu vlasni\u010dku tehnologiju\u2026<\/p>","protected":false},"author":210157960,"featured_media":6764,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1586,1661,1372,1377,1378,1368],"tags":[1618,1668],"class_list":["post-6762","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product-features","category-satellite-imagery","category-blog","category-crop-monitoring","category-remote-sensing","category-yield","tag-crop-monitoring","tag-field-boundaries"],"acf":[],"yoast_head":"\nAutomated Field Boundaries Detection Model for Precision Agriculture by GeoPard - 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