{"id":12824,"date":"2026-02-15T21:14:26","date_gmt":"2026-02-15T20:14:26","guid":{"rendered":"https:\/\/geopard.tech\/?p=12824"},"modified":"2026-02-15T21:14:26","modified_gmt":"2026-02-15T20:14:26","slug":"izvietosanas-informetu-nasa-loma-efektiva-bezpilota-lidaparatu-uav-kulturaugu-uzraudziba","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lv\/blog\/role-of-deployment-aware-nas-for-efficient-uav-based-crop-monitoring\/","title":{"rendered":"Izvieto\u0161anas zi\u0146\u0101 apzin\u012bgu NAS loma efekt\u012bv\u0101 bezpilota lidapar\u0101tu (UAV) kult\u016braugu uzraudz\u012bb\u0101"},"content":{"rendered":"

Bezpilota lidapar\u0101ti (UAV) jeb droni p\u0101rveido m\u016bsdienu lauksaimniec\u012bbu, nodro\u0161inot \u0101trus lauku att\u0113lus no gaisa. Tos izmanto, lai sken\u0113tu kult\u016braugus, lai noteiktu to vesel\u012bbu, aug\u0161anas stadiju, kait\u0113k\u013cus, nez\u0101les un nov\u0113rt\u0113tu ra\u017eu. Piem\u0113ram, \u0136\u012bn\u0101 pa\u0161laik darbojas vair\u0101k nek\u0101 250\u00a0000 lauksaimniec\u012bbas dronu, un Taizem\u0113 l\u012bdz 2023.\u00a0gadam aptuveni 301\u00a0000\u00a0t lauksaimniec\u012bbas zemes tika p\u0101rkl\u0101ta ar dronu miglo\u0161anu vai uzraudz\u012bbu. \u0160ie UAV padara lauksaimniec\u012bbu efekt\u012bv\u0101ku, \u0101tri atkl\u0101jot probl\u0113mas (piem\u0113ram, kait\u0113k\u013cu uzliesmojumus vai \u016bdens tr\u016bkumu), kuras uz zemes var nepaman\u012bt.<\/p>\n

Tom\u0113r maziem bezpilota lidapar\u0101tiem (UAV) ir \u013coti ierobe\u017eota ieb\u016bv\u0113t\u0101 skait\u013co\u0161anas jauda un akumulatora darb\u012bbas laiks. T\u0101p\u0113c sare\u017e\u0123\u012btu m\u0101ksl\u012bg\u0101 intelekta redzes algoritmu palai\u0161ana tajos re\u0101llaik\u0101 ir izaicin\u0101jums. Tradicion\u0101lie vieglie objektu noteik\u0161anas mode\u013ci (piem\u0113ram, niec\u012bgie YOLO vai MobileNet balst\u012bti detektori) var tikai da\u013c\u0113ji apmierin\u0101t \u0161\u012bs vajadz\u012bbas: tie bie\u017ei vien upur\u0113 precizit\u0101ti vai \u0101trumu un prasa iev\u0113rojamu manu\u0101lu regul\u0113\u0161anu. \u0160\u012b plaisa motiv\u0113 izvieto\u0161anai piel\u0101gotu neironu arhitekt\u016bras mekl\u0113\u0161anu (NAS): automatiz\u0113tu projekt\u0113\u0161anas metodi, kas piel\u0101go dzi\u013c\u0101s m\u0101c\u012b\u0161an\u0101s mode\u013cus prec\u012bz\u0101m lauka apst\u0101k\u013cos izvietotu bezpilota lidapar\u0101tu pras\u012bb\u0101m.<\/p>\n

M\u016bsdienu prec\u012bz\u0101 lauksaimniec\u012bba izmanto bezpilota lidapar\u0101tus (dronus), lai apsekotu laukus un uzraudz\u012btu kult\u016braugu st\u0101vokli. Lidojot virs liel\u0101m plat\u012bb\u0101m, droni var uz\u0146emt augstas iz\u0161\u0137irtsp\u0113jas augu, augsnes un lauka mode\u013cu att\u0113lus. \u0160ie att\u0113li tiek padoti datorredzes algoritmiem, kas atkl\u0101j nez\u0101les starp kult\u016braugiem, nov\u0113rt\u0113 ra\u017eu (piem\u0113ram, skaitot aug\u013cus vai galvi\u0146as) vai pamana agr\u012bnas slim\u012bbas vai bar\u012bbas vielu tr\u016bkuma paz\u012bmes. Piem\u0113ram, droni \u013cauj m\u0113r\u0137tiec\u012bgi izsmidzin\u0101t herbic\u012bdus uz nez\u0101\u013cu plankumiem, samazinot \u0137\u012bmisko vielu lieto\u0161anu un izmaksas.<\/p>\n

Tom\u0113r dronu mazajiem borta datoriem (kuru jauda bie\u017ei vien ir ierobe\u017eota l\u012bdz da\u017eiem vatiem) ir gr\u016bt\u012bbas darbin\u0101t lielus neironu t\u012bklus lidojuma \u0101trum\u0101. Tas apgr\u016btina re\u0101llaika anal\u012bzes veik\u0161anu: ja drons pamana probl\u0113mu, tam ir \u0101tri j\u0101rea\u0123\u0113 vai j\u0101re\u0123istr\u0113 dati, pirms izl\u0101d\u0113jas akumulators. Pa\u0161reiz\u0113jie vieglie detektori (piem\u0113ram, YOLOv8 nano, YOLO-tiny, MobileNets) tiek izstr\u0101d\u0101ti ar roku, un bie\u017ei vien ir saist\u012bti ar kompromisiem: maz\u0101ka mode\u013ca izgatavo\u0161ana to pa\u0101trina, bet var kait\u0113t precizit\u0101tei.<\/p>\n

T\u0101 rezult\u0101t\u0101 ir liela vajadz\u012bba p\u0113c metod\u0113m, kas autom\u0101tiski atrod vislab\u0101ko iesp\u0113jamo modeli, \u0146emot v\u0113r\u0101 bezpilota lidapar\u0101ta (UAV) ierobe\u017eojumus. Izvieto\u0161anas zi\u0146\u0101 apzin\u012bgs NAS atbilst \u0161ai vajadz\u012bbai, mekl\u0113jot neironu t\u012bkla arhitekt\u016bras, kas kop\u012bgi optimiz\u0113 noteik\u0161anas precizit\u0101ti un resursu izmanto\u0161anu (latentumu, jaudu, atmi\u0146u) re\u0101los bezpilota lidapar\u0101tu apst\u0101k\u013cos. \u0160\u012b pieeja var nodro\u0161in\u0101t specializ\u0113tus mode\u013cus, kas efekt\u012bvi darbojas dronu aparat\u016br\u0101, vienlaikus saglab\u0101jot augstu precizit\u0101ti kult\u016braugu uzraudz\u012bbas uzdevumiem.<\/p>\n

Bezpilota lidapar\u0101tu objektu noteik\u0161anas pras\u012bbas kult\u016braugu uzraudz\u012bb\u0101<\/h2>\n

Lauksaimniec\u012bbas bezpilota lidapar\u0101ti veic virkni vizu\u0101l\u0101s noteik\u0161anas uzdevumu, katram no tiem ir savas pras\u012bbas:<\/p>\n

1. Kult\u016braugu vesel\u012bbas un stresa noteik\u0161ana:<\/strong> Droni izmanto RGB, termisk\u0101s vai multispektr\u0101l\u0101s kameras, lai identific\u0113tu stresa skartus augus, bar\u012bbas vielu tr\u016bkumu vai slim\u012bbu simptomus. Re\u0101llaika algoritmi var kart\u0113t lauka main\u012bgumu, vadot ap\u016bde\u0146o\u0161anu vai m\u0113slo\u0161anu. Prec\u012bza augu stresa paz\u012bmju noteik\u0161ana \u013cauj savlaic\u012bgi iejaukties, lai saglab\u0101tu ra\u017eu.<\/p>\n

2. Nez\u0101\u013cu identific\u0113\u0161ana:<\/strong> Nez\u0101\u013cu noteik\u0161ana starp kult\u016braugiem \u013cauj lauksaimniekiem apsmidzin\u0101t tikai nev\u0113lamus augus, t\u0101d\u0113j\u0101di ietaupot herbic\u012bdus. Piem\u0113ram, p\u0113t\u012bjum\u0101 par kokvilnas laukiem tika izmantoti bezpilota lidapar\u0101tu att\u0113li ar YOLOv7 b\u0101zes detektoru, un tika sasniegta aptuveni 83% precizit\u0101te nez\u0101\u013cu atdal\u012b\u0161an\u0101 no kokvilnas. Tom\u0113r vizu\u0101li l\u012bdz\u012bgu nez\u0101\u013cu un kult\u016braugu at\u0161\u0137ir\u0161ana p\u0101rbl\u012bv\u0113tos lauka att\u0113los joproj\u0101m ir sare\u017e\u0123\u012bta.<\/p>\n

\"Bezpilota<\/p>\n

3. Kait\u0113k\u013cu un slim\u012bbu atkl\u0101\u0161ana:<\/strong> Bezpilota lidapar\u0101ti (UAV) var paman\u012bt uzliesmojumus (piem\u0113ram, sise\u0146us, kukai\u0146us vai s\u0113n\u012b\u0161u iedegu) agr\u0101k nek\u0101 cilv\u0113ki, kas p\u0101rvietojas k\u0101j\u0101m. Droni atbalsta ar\u012b kait\u0113k\u013cu invad\u0113to zonu kart\u0113\u0161anu, izmantojot multispektr\u0101lo att\u0113lveido\u0161anu, kas uzlabo tikai RGB. \u0100tra un prec\u012bza kait\u0113k\u013cu noteik\u0161ana ir \u013coti svar\u012bga, lai nov\u0113rstu izplat\u012b\u0161anos.<\/p>\n

4. Ienes\u012bguma nov\u0113rt\u0113jums:<\/strong> Aug\u013cu, graudu galvi\u0146u vai augu skait\u012b\u0161ana no gaisa pal\u012bdz prognoz\u0113t ra\u017eas apjomus. Mode\u013ci, kas apm\u0101c\u012bti noteikt \u0101bolus, melones vai kvie\u0161u galvi\u0146as bezpilota lidapar\u0101tu att\u0113los, var pa\u0101trin\u0101t ra\u017eas nov\u0113rt\u0113\u0161anu. Piem\u0113ram, neironu t\u012bkli dronu att\u0113los ir izmantoti, lai laukos saskait\u012btu arb\u016bzu un melo\u0146u ra\u017eu.<\/p>\n

5. M\u0113rniec\u012bba un kart\u0113\u0161ana:<\/strong> Droni ar\u012b veido lauka kartes (topogr\u0101fiju, augsnes at\u0161\u0137ir\u012bbas), kas pal\u012bdz pl\u0101not kultiv\u0113\u0161anu. Lai gan t\u0101 nav tikai objektu noteik\u0161ana, t\u0101 ir da\u013ca no bezpilota lidapar\u0101tu (UAV) uzraudz\u012bbas.<\/p>\n

\u0160ie uzdevumi bie\u017ei vien prasa gandr\u012bz re\u0101llaika secin\u0101jumus: dronam, kas lido virs laukiem, var b\u016bt nepiecie\u0161ams apstr\u0101d\u0101t video kadrus re\u0101llaik\u0101 (vair\u0101kus kadrus sekund\u0113), lai vad\u012bbas l\u0113mumus (piem\u0113ram, augstuma piel\u0101go\u0161anu vai smidzin\u0101t\u0101ja aktiviz\u0113\u0161anu) var\u0113tu pie\u0146emt nekav\u0113joties. Citos gad\u012bjumos nelielas kav\u0113\u0161an\u0101s (sekundes) var\u0113tu b\u016bt pie\u0146emamas, ja dati tiek re\u0123istr\u0113ti un analiz\u0113ti p\u0113c nos\u0113\u0161an\u0101s.<\/p>\n

Svar\u012bgi ir tas, ka bezpilota lidapar\u0101tu (UAV) redzei j\u0101tiek gal\u0101 ar vides main\u012bgumu: spilgtu saules gaismu, \u0113n\u0101m, v\u0113ja izrais\u012btu kust\u012bbu izpl\u016b\u0161anu, p\u0101rkl\u0101jo\u0161u lapu aizsegumu vai augstuma un le\u0146\u0137a izmai\u0146\u0101m. Objektu izm\u0113ri at\u0161\u0137iras (tuvpl\u0101na nez\u0101les pretstat\u0101 att\u0101l\u0101m kait\u0113k\u013cu grup\u0101m), t\u0101p\u0113c detektoriem j\u0101sp\u0113j tikt gal\u0101 ar daudzm\u0113roga elementiem.<\/p>\n

Visbeidzot, lauksaimniec\u012bbas bezpilota lidapar\u0101tu (UAV) misijas ietver stingrus kompromisus starp precizit\u0101ti, latentumu un ener\u0123iju. Lai nepaman\u012btu nez\u0101les vai kait\u0113k\u013cus, ir nepiecie\u0161ama augsta noteik\u0161anas precizit\u0101te, ta\u010du \u013coti dzi\u013ca t\u012bkla darb\u012bba var \u0101tri iztuk\u0161ot akumulatoru. T\u0101p\u0113c noteik\u0161anas modelim ir j\u0101b\u016bt \u0101tram un energoefekt\u012bvam, vienlaikus pietiekami prec\u012bzam uzdevuma veik\u0161anai. \u0160\u012bs stingr\u0101s pras\u012bbas izce\u013c, k\u0101p\u0113c lauksaimniec\u012bb\u0101 izmantojamiem bezpilota lidapar\u0101tiem ir nepiecie\u0161ams specializ\u0113ts mode\u013cu dizains.<\/p>\n

Vieglie objektu detektori bezpilota lidapar\u0101tu platform\u0101m<\/h2>\n

Vieglie objektu detektori ir neironu t\u012bkli, kas \u012bpa\u0161i izstr\u0101d\u0101ti darb\u012bbai ar ierobe\u017eotu aparat\u016bru. Tie bie\u017ei izmanto mazus mugurkaulus (piem\u0113ram, MobileNet vai ShuffleNet), samazin\u0101tu sl\u0101\u0146u platumu vai vienk\u0101r\u0161otu kakla\/galvas dizainu. Piem\u0113ram, YOLO saimes mode\u013cos ietilpst \u201cnano\u201d un \u201ctiny\u201d versijas (piem\u0113ram, YOLOv8n, YOLOv5s), kur\u0101m ir maz\u0101k parametru un nepiecie\u0161amas maz\u0101k darb\u012bbu (FLOP).<\/p>\n

\u0160\u0101di detektori var darboties ar desmitiem kadru sekund\u0113 iegult\u0101s aparat\u016bras, piem\u0113ram, NVIDIA Jetson Nano vai Google Coral, ier\u012bc\u0113s. Piem\u0113ram, Ag-YOLO bija piel\u0101gots YOLO b\u0101zes detektors palmu plant\u0101cij\u0101m, kas darboj\u0101s ar \u0101trumu 36,5 kadri sekund\u0113 uz Intel Neural Compute Stick 2 (izmantojot tikai 1,5 W) un sasniedza augstu precizit\u0101ti (F1 = 0,9205). \u0160is modelis izmantoja aptuveni 12 reizes maz\u0101k parametru nek\u0101 YOLOv3-Tiny, vienlaikus divk\u0101r\u0161ojot t\u0101 \u0101trumu.<\/p>\n

\"Vieglie<\/p>\n

\u0160ie piem\u0113ri par\u0101da kompromisus mode\u013ca dizain\u0101: mode\u013ca izm\u0113ra vai sare\u017e\u0123\u012bt\u012bbas samazin\u0101\u0161ana (piem\u0113ram, maz\u0101k sl\u0101\u0146u vai kan\u0101lu) parasti pa\u0101trina secin\u0101jumu izdar\u012b\u0161anu un samazina ener\u0123ijas pat\u0113ri\u0146u, bet var samazin\u0101t precizit\u0101ti. Ag-YOLO upur\u0113ja zin\u0101mu jaudu, lai palielin\u0101tu \u0101trumu un efektivit\u0101ti, tom\u0113r sav\u0101 uzdevum\u0101 joproj\u0101m saglab\u0101ja augstu F1 rezult\u0101tu - 0,92.<\/p>\n

L\u012bdz\u012bgi tika sal\u012bdzin\u0101ti tr\u012bs YOLOv7 varianti nez\u0101\u013cu noteik\u0161anai: pilnais YOLOv7 sasniedza 83% precizit\u0101ti, savuk\u0101rt maz\u0101ks YOLOv7-w6 t\u012bkls sasniedza 63% precizit\u0101ti. Tas ilustr\u0113 visp\u0101r\u012bgu vieglmet\u0101la detektoru ierobe\u017eojumu: mode\u013ci, kas noregul\u0113ti vienai videi vai objekta tipam, var darboties slikt\u0101k cit\u0101. Detektors, kas ir sa\u0161aurin\u0101ts \u0101truma d\u0113\u013c, var palaist gar\u0101m smalkas nor\u0101des (piem\u0113ram, mazas vai mask\u0113tas nez\u0101les), t\u0101d\u0113j\u0101di ietekm\u0113jot notur\u012bbu da\u017e\u0101dos apst\u0101k\u013cos.<\/p>\n

Lauksaimniec\u012bb\u0101 \u0161ie visp\u0101r\u012bgie vieglie t\u012bkli var neb\u016bt optim\u0101li bez papildu piel\u0101go\u0161anas. Piem\u0113ram, YOLOv7 modelis, kas iepriek\u0161 apm\u0101c\u012bts ar bie\u017ei izmantotiem datu kopumiem, var nepiln\u012bgi apstr\u0101d\u0101t kult\u016braugu att\u0113lu unik\u0101l\u0101s tekst\u016bras un m\u0113rogus. T\u0101d\u0113\u013c ir nepiecie\u0161ama mode\u013ca arhitekt\u016bras optimiz\u0101cija atbilsto\u0161i uzdevumiem un platformai. Manu\u0101la regul\u0113\u0161ana (sl\u0101\u0146u, filtru utt. mai\u0146a) katram jaunam drona tipam vai kult\u016braugu \u0161\u0137irnei ir darbietilp\u012bga. Tas motiv\u0113 automatiz\u0113tas metodes, piem\u0113ram, izvieto\u0161anai piel\u0101gotus NAS, lai atrastu lab\u0101ko izm\u0113ra, precizit\u0101tes un robustuma l\u012bdzsvaru konkr\u0113tai bezpilota lidapar\u0101tu platformai un lauksaimniec\u012bbas pielietojumam.<\/p>\n

Neironu arhitekt\u016bras mekl\u0113\u0161ana bezpilota lidapar\u0101tu (UAV) balst\u012bt\u0101s redzes sist\u0113m\u0101s<\/h2>\n

Neironu arhitekt\u016bras mekl\u0113\u0161ana (NAS) ir automatiz\u0113ta metode neironu t\u012bklu arhitekt\u016bru projekt\u0113\u0161anai. T\u0101 viet\u0101, lai manu\u0101li iestat\u012btu sl\u0101\u0146u, filtru un savienojumu skaitu, NAS izmanto algoritmus (pastiprin\u0101\u0161anas m\u0101c\u012b\u0161anos, evol\u016bcijas metodes vai gradientu mekl\u0113\u0161anu), lai izp\u0113t\u012btu iesp\u0113jamo dizainu telpu un atrastu t\u0101dus, kas optimiz\u0113 izv\u0113l\u0113to m\u0113r\u0137i (piem\u0113ram, precizit\u0101ti).<\/p>\n

NAS jau ir ticis izmantots mobilaj\u0101m ier\u012bc\u0113m draudz\u012bgu t\u012bklu izveidei. Piem\u0113ram, Google MnasNet bija novatorisks \u201cplatformu apzino\u0161s\u201d NAS, kas m\u0113r\u0137\u012b tie\u0161i iek\u013c\u0101va re\u0101lu ier\u012bces latentumu. MnasNet mekl\u0113\u0161anas laik\u0101 katram kandid\u0101ta modelim izm\u0113r\u012bja secin\u0101jumu laiku Google Pixel t\u0101lrun\u012b un sal\u012bdzin\u0101ja precizit\u0101ti ar \u0161o izm\u0113r\u012bto latentumu. Rezult\u0101ts bija CNN saime, kas bija gan \u0101tra, gan prec\u012bza mobilaj\u0101 aparat\u016br\u0101, p\u0101rsp\u0113jot manu\u0101li izstr\u0101d\u0101tus MobileNets un NASNet mode\u013cus ImageNet platform\u0101.<\/p>\n

Tom\u0113r visp\u0101r\u012bgas NAS pieejas, piem\u0113ram, MnasNet, koncentr\u0113jas uz visp\u0101r\u012bgiem redzes uzdevumiem (ImageNet klasifik\u0101cija vai COCO noteik\u0161ana) un visp\u0101r\u012bgu aparat\u016bru (piem\u0113ram, mobilajiem t\u0101lru\u0146iem). Bezpilota lidapar\u0101tu (UAV) kult\u016braugu uzraudz\u012bbai probl\u0113ma ir specializ\u0113t\u0101ka. M\u0113s v\u0113lamies detektorus, kas ir optimiz\u0113ti konkr\u0113t\u0101m objektu klas\u0113m (augiem, nez\u0101l\u0113m, kait\u0113k\u013ciem) un piel\u0101goti bezpilota lidapar\u0101ta sensoriem un lidojuma profilam. Standarta NAS, kas optimiz\u0113 tikai precizit\u0101ti vai visp\u0101r\u012bgu latentumu, var nepaman\u012bt t\u0101das nianses k\u0101 mazu objektu noteik\u0161ana vai ener\u0123ijas ierobe\u017eojumi.<\/p>\n

Turkl\u0101t tradicion\u0101l\u0101s NAS metodes var b\u016bt \u013coti d\u0101rgas skait\u013co\u0161anas zi\u0146\u0101 (bie\u017ei vien prasa vair\u0101kas dienas lielos GPU klasteros), kas lauksaimniec\u012bbas p\u0113tniekiem ne vienm\u0113r ir praktiski. T\u0101p\u0113c bezpilota lidapar\u0101tu (UAV) redzei ir nepiecie\u0161ami uzdevumam specifiski NAS ietvari. Tiem j\u0101ietver ar UAV saist\u012bti krit\u0113riji un j\u0101b\u016bt p\u0113c iesp\u0113jas efekt\u012bv\u0101kiem.<\/p>\n

Visos gad\u012bjumos ierobe\u017eojumu apzin\u0101\u0161an\u0101s ir kritiski svar\u012bga: NAS ir j\u0101apzin\u0101s m\u0113r\u0137a ier\u012bces ierobe\u017eojumi (l\u012bdz\u012bgi k\u0101 MnasNet) un bezpilota lidapar\u0101tu uzdevumu re\u0101llaika pras\u012bbas lidojuma laik\u0101. Ja mekl\u0113\u0161ana ir p\u0101r\u0101k l\u0113na vai ignor\u0113 ener\u0123ijas pat\u0113ri\u0146u, ieg\u016btais modelis var nedarboties labi lauka apst\u0101k\u013cos.<\/p>\n

Praks\u0113 NAS bezpilota lidapar\u0101tu (UAV) redzei mekl\u0113\u0161anas metrik\u0101 iek\u013cautu aparat\u016bras latentumu un ener\u0123iju. Piem\u0113ram, var\u0113tu izm\u0113r\u012bt kandid\u0101ta detektora kadru \u0101trumu faktiskaj\u0101 drona dator\u0101 (piem\u0113ram, NVIDIA Jetson) un izmantot to k\u0101 r\u0101d\u012bt\u0101ju. Tas at\u0161\u0137iras no vienk\u0101r\u0161u starpniekservera, piem\u0113ram, FLOP, izmanto\u0161anas, kas neuztver re\u0101lo \u0101trumu.<\/p>\n

T\u0101d\u0113j\u0101di NAS var atkl\u0101t arhitekt\u016bras, kas vislab\u0101k izmanto ier\u012bces iesp\u0113jas. Rezum\u0113jot, NAS pied\u0101v\u0101 veidu, k\u0101 autom\u0101tiski izstr\u0101d\u0101t detektorus bezpilota lidapar\u0101tiem (UAV), ta\u010du tas ir j\u0101piel\u0101go, lai \u0146emtu v\u0113r\u0101 bezpilota lidapar\u0101tiem rakstur\u012bgos uzdevumus un efektivit\u0101tes pras\u012bbas.<\/p>\n

Izvieto\u0161anai piel\u0101gota NAS: pamatprincipi<\/h2>\n

Izvieto\u0161anai piel\u0101gots NAS papla\u0161ina aparat\u016brai piel\u0101gotu NAS, iek\u013caujot izvieto\u0161anas kontekstu un vides ierobe\u017eojumus projekt\u0113\u0161anas proces\u0101. Citiem v\u0101rdiem sakot, tas \u0146em v\u0113r\u0101 ne tikai drona aparat\u016bru (procesora\/grafisk\u0101 procesora \u0101trumu, atmi\u0146as ierobe\u017eojumus, ener\u0123ijas bud\u017eetu), bet ar\u012b to, ar ko bezpilota lidapar\u0101ts faktiski saskarsies lauk\u0101. Tas noz\u012bm\u0113 skaidru optimiz\u0101ciju t\u0101diem r\u0101d\u012bt\u0101jiem k\u0101 secin\u0101jumu latentums m\u0113r\u0137a ier\u012bc\u0113, ener\u0123ijas pat\u0113ri\u0146\u0161 un atmi\u0146as izmanto\u0161ana, vienlaikus cen\u0161oties sasniegt augstu noteik\u0161anas precizit\u0101ti.<\/p>\n

Piem\u0113ram, NAS laik\u0101 katru kandid\u0101ta modeli var\u0113tu izvietot uz bezpilota lidapar\u0101tam pievienotas Jetson Nano ier\u012bces un re\u0123istr\u0113t t\u0101 re\u0101lo secin\u0101jumu laiku un ener\u0123ijas pat\u0113ri\u0146u. \u0160\u012b emp\u012brisk\u0101 atgriezenisk\u0101 saite pal\u012bdz mekl\u0113t mode\u013cus, kas patie\u0161\u0101m atbilst izvieto\u0161anas krit\u0113rijiem.<\/p>\n

\"Izvieto\u0161anai<\/p>\n

Aparat\u016bras l\u012bmen\u012b str\u0101d\u0101jo\u0161as NAS (piem\u0113ram, MnasNet) koncentr\u0113jas uz ier\u012bces metriku, savuk\u0101rt izvieto\u0161anas l\u012bmen\u012b str\u0101d\u0101jo\u0161as NAS iet t\u0101l\u0101k: t\u0101s var \u0146emt v\u0113r\u0101 sensoru ievades raksturlielumus (piem\u0113ram, att\u0113la iz\u0161\u0137irtsp\u0113ju, multispektr\u0101los kan\u0101lus) un lietojumprogrammu latentuma m\u0113r\u0137us (nepiecie\u0161amie kadri sekund\u0113). T\u0101s var pat iek\u013caut lidojuma ierobe\u017eojumus, piem\u0113ram, maksim\u0101li pie\u013caujamo atmi\u0146u, vai nov\u0113rt\u0113jumus simul\u0113tas v\u0113ja vibr\u0101cijas vai kust\u012bbas izpl\u016b\u0161anas apst\u0101k\u013cos.<\/p>\n

Izvieto\u0161anai paredz\u0113ts NAS var\u0113tu sod\u012bt arhitekt\u016bras, kas p\u0101rsniedz, piem\u0113ram, 5\u00a0W jaudas pat\u0113ri\u0146u vai kur\u0101m nepiecie\u0161ams vair\u0101k atmi\u0146as nek\u0101 dronam. T\u0101d\u0113j\u0101di mekl\u0113\u0161ana dabiski tiek novirz\u012bta uz praktiskiem mode\u013ciem bezpilota lidapar\u0101ta (UAV) darb\u012bbas lauk\u0101. B\u016bt\u012bb\u0101 izvieto\u0161anai paredz\u0113ts NAS ir par mode\u013ca dizaina un re\u0101l\u0101s lieto\u0161anas saiknes nosl\u0113g\u0161anu. T\u0101 viet\u0101, lai izv\u0113l\u0113tos arhitekt\u016bru atsevi\u0161\u0137i un cer\u0113tu, ka t\u0101 darbosies, mekl\u0113\u0161anas laik\u0101 tas sistem\u0101tiski ietver test\u0113\u0161anu re\u0101l\u0101s ier\u012bc\u0113s.<\/p>\n

Piem\u0113ram, Kerec et al. (2026) izmantoja \u0161\u0101du sist\u0113mu, lai mekl\u0113tu bezpilota lidapar\u0101tu detektoru: vi\u0146i balst\u012bj\u0101s uz YOLOv8n b\u0101zes l\u012bniju, bet mekl\u0113\u0161an\u0101 iek\u013c\u0101va Jetson Nano latentumu un ener\u0123iju. Ieg\u016btajam modelim bija par 37% maz\u0101k GFLOP un par 61% maz\u0101k parametru nek\u0101 YOLOv8n, un mAP kritums bija tikai 1,96%. Tas skaidri par\u0101da, k\u0101 izvieto\u0161anas ierobe\u017eojumi virz\u012bja NAS uz daudz viegl\u0101ku un \u0101tr\u0101ku t\u012bklu.<\/p>\n

Izvieto\u0161anas zi\u0146\u0101 apzin\u012bgu NAS loma prec\u012bz\u0101s lauksaimniec\u012bbas uzraudz\u012bb\u0101<\/h2>\n

Izvieto\u0161anai piel\u0101gota NAS sist\u0113ma var iev\u0113rojami uzlabot bezpilota lidapar\u0101tu (UAV) kult\u016braugu uzraudz\u012bbu, piel\u0101gojot detektorus lauksaimniec\u012bbas apst\u0101k\u013ciem. Piem\u0113ram, mekl\u0113\u0161ana var dot priek\u0161roku arhitekt\u016br\u0101m, kas izce\u013cas ar sp\u0113ju noteikt mazus, pl\u0101nus objektus (piem\u0113ram, \u0161auras nez\u0101les vai tievus kukur\u016bzas st\u0101dus) vai at\u0161\u0137irt augus no augsnes fona. T\u0101 var piel\u0101got t\u012bkla dzi\u013cumu un uztveres laukus tipiskajam lidojuma augstumam: zem\u0101 augstum\u0101 objekti aizpilda att\u0113lu un var b\u016bt nepiecie\u0161amas s\u012bkas deta\u013cas, savuk\u0101rt liel\u0101k\u0101 augstum\u0101 t\u012bklam vajadz\u0113tu b\u016bt labam maza m\u0113roga noteik\u0161an\u0101. Izvieto\u0161anai piel\u0101gota NAS sist\u0113ma var iekod\u0113t \u0161\u012bs pras\u012bbas sav\u0101 mekl\u0113\u0161anas telp\u0101.<\/p>\n

\u0100trums ir kritiski svar\u012bgs lauka apst\u0101k\u013cos. Iedom\u0101jieties, ka drons atkl\u0101j kait\u0113k\u013cu uzliesmojumu; ja modelis ir pietiekami \u0101trs, lai apstr\u0101d\u0101tu video, piem\u0113ram, ar 30 kadriem sekund\u0113, tas var br\u012bdin\u0101t pilotu vai ierosin\u0101t t\u016bl\u012bt\u0113ju \u0101rst\u0113\u0161anas darb\u012bbu. Testos NAS izstr\u0101d\u0101ts modelis darboj\u0101s par 28% \u0101tr\u0101k uz Jetson Nano nek\u0101 standarta YOLOv8n, pateicoties t\u0101 optimiz\u0113tajai arhitekt\u016brai. Tas ar\u012b pat\u0113r\u0113ja par 18,5% maz\u0101k ener\u0123ijas ONNX darb\u012bbas laik\u0101, kas noz\u012bm\u0113, ka drons var lidot ilg\u0101k ar to pa\u0161u akumulatoru. \u0160ie ieguvumi padara l\u0113mumu pie\u0146em\u0161anu lidojuma laik\u0101 viegl\u0101ku un pagarina misijas ilgumu.<\/p>\n

V\u0113l viena priek\u0161roc\u012bba ir robustums. T\u0101 k\u0101 izvieto\u0161anai piel\u0101gots NAS ietver faktisku ier\u012bces nov\u0113rt\u0113\u0161anu, mekl\u0113\u0161ana var ietvert testus da\u017e\u0101dos apst\u0101k\u013cos. Piem\u0113ram, t\u0101 var simul\u0113t v\u0101ju apgaismojumu vai iek\u013caut apm\u0101c\u012bbas att\u0113lus no r\u012btausmas un kr\u0113slas, nodro\u0161inot, ka gal\u012bgais detektors saglab\u0101 precizit\u0101ti re\u0101los laika apst\u0101k\u013cos un apgaismojuma izmai\u0146\u0101s. Darbs par\u0101d\u012bja, ka no NAS ieg\u016btais detektors ir labi visp\u0101rin\u0101ms: vi\u0146i to p\u0101rbaud\u012bja ar diviem da\u017e\u0101diem kult\u016braugu datu kopumiem (kvie\u0161u galvi\u0146\u0101m un kokvilnas st\u0101diem) un abos konstat\u0113ja labu veiktsp\u0113ju.<\/p>\n

\"Izvieto\u0161anas<\/p>\n

Tas liek dom\u0101t, ka izvieto\u0161anas zi\u0146\u0101 apzin\u0101tas NAS sist\u0113mas pal\u012bdz\u0113ja atrast kop\u012bgas, lauksaimniec\u012bbai noder\u012bgas funkcijas, uzlabojot visp\u0101rin\u0101\u0161anu uz jauniem laukiem. Kopum\u0101 izvieto\u0161anas zi\u0146\u0101 apzin\u0101tas NAS sist\u0113mas pal\u012bdz l\u012bdzsvarot precizit\u0101ti ar ilg\u0101ku lidojuma laiku. Samazinot skait\u013co\u0161anas apjomu, droni pat\u0113r\u0113 maz\u0101k ener\u0123ijas un var aptvert liel\u0101ku plat\u012bbu ar vienu akumulatora uzl\u0101di, vienlaikus joproj\u0101m dro\u0161i nosakot kult\u016braugus un kait\u0113k\u013cus.<\/p>\n

Lauksaimniec\u012bbas bezpilota lidapar\u0101tu detektoru mekl\u0113\u0161anas telpas dizains<\/h2>\n

Svar\u012bga izvieto\u0161anai piel\u0101gota NAS sast\u0101vda\u013ca ir mekl\u0113\u0161anas telpa \u2014 iesp\u0113jamo t\u012bkla dizainu kopums, ko t\u0101 apsver. Bezpilota lidapar\u0101tu (UAV) kult\u016braugu detektoru mekl\u0113\u0161anas telpu var izveidot, iek\u013caujot \u0161aj\u0101 jom\u0101 daudzsolo\u0161as arhitekt\u016bras. Galven\u0101s da\u013cas ietver:<\/p>\n

1. Mugurkaula dizains:<\/strong> Mugurkauls ir iez\u012bmju ieguv\u0113js. Bezpilota lidapar\u0101tiem (UAV) var\u0113tu iek\u013caut vieglus konvolucion\u0101rus pamatblokus, piem\u0113ram, dzi\u013cum\u0101 atdal\u0101mus konvol\u016bcijas blokus (k\u0101 to izmanto MobileNet) vai apgrieztus atlikumu blokus. Apgrieztie atlikumi un line\u0101rie sa\u0161aurin\u0101jumi (MobileNetV2 stil\u0101) ir labi paz\u012bstami ar savu mobilo efektivit\u0101ti. Mekl\u0113\u0161anas telpa var\u0113tu \u013caut main\u012bt katra bloka platumu (kan\u0101lu skaitu) un dzi\u013cumu, lai tas atbilstu UAV skait\u013co\u0161anas bud\u017eetam. Var\u0113tu iek\u013caut ar\u012b uzman\u012bbas vai transformatora iedvesmotus modu\u013cus, ja UAV tos var at\u013cauties ar mazu ener\u0123ijas pat\u0113ri\u0146u.<\/p>\n

2. Kakla dizains:<\/strong> Daudzi objektu detektori izmanto iez\u012bmju piram\u012bdas (FPN) vai ce\u013cu apkopo\u0161anas t\u012bklus, lai apvienotu daudzm\u0113roga iez\u012bmes. Mekl\u0113\u0161an\u0101 var\u0113tu izp\u0113t\u012bt vienk\u0101r\u0161otus FPN vai vieglu iez\u012bmju apkopo\u0161anu. Piem\u0113ram, var\u0113tu b\u016bt iesp\u0113jams izmantot viena m\u0113roga galvu sal\u012bdzin\u0101jum\u0101 ar daudzm\u0113roga galv\u0101m. Telpa var\u0113tu \u013caut apvienot sl\u0101\u0146us vai izlaist savienojumus, kas pal\u012bdz noteikt da\u017e\u0101da izm\u0113ra objektus.<\/p>\n

3. Galvas dizains:<\/strong> Ar\u012b noteik\u0161anas galvi\u0146u (klasifik\u0101cijas un kastes regresijas sl\u0101\u0146us) var main\u012bt. Bezpilota lidapar\u0101tiem, kas mekl\u0113 vien\u0101dus laukus, var\u0113tu pietikt ar vienk\u0101r\u0161\u0101ku galvi\u0146u. Ta\u010du, lai no\u0137ertu mazus nez\u0101les, mekl\u0113\u0161an\u0101 var iek\u013caut papildu konvolucion\u0101los sl\u0101\u0146us vai da\u017e\u0101das enkuru sh\u0113mas.<\/p>\n

4. Vieglas darb\u012bbas:<\/strong> Mekl\u0113\u0161anas telpa var nep\u0101rprotami at\u013caut tikai zemas izmaksas. Piem\u0113ram, izv\u0113l\u0113ties starp 3\u00d73 konversiju un l\u0113t\u0101ku 1\u00d73+3\u00d71 faktoriz\u0113tu konversiju vai iek\u013caut GhostNet modu\u013cus. T\u0101 var ar\u012b at\u013caut mazus kodola izm\u0113rus vai samazin\u0101tas dimensijas, lai ierobe\u017eotu apr\u0113\u0137inus. Visas \u0161\u012bs izv\u0113les nosaka aparat\u016bra. Telpa var aizliegt jebkuru sl\u0101\u0146u konfigur\u0101ciju, kas p\u0101rsniedz drona atmi\u0146as ierobe\u017eojumu vai paredzamo ener\u0123ijas slieksni.<\/p>\n

R\u016bp\u012bgi izstr\u0101d\u0101jot \u0161o mekl\u0113\u0161anas telpu, NAS process tiek virz\u012bts uz efekt\u012bv\u0101m, bet lietder\u012bg\u0101m arhitekt\u016br\u0101m. Rezult\u0101ts var\u0113tu b\u016bt jauna bloku kombin\u0101cija, kas nav \u0146emta v\u0113r\u0101 standarta mode\u013cos. Vislab\u0101k atrastais detektors izmantoja piel\u0101gotas bloku izv\u0113les, kas samazin\u0101ja GFLOP par 37% un parametrus par 61%, sal\u012bdzinot ar YOLOv8n.<\/p>\n

Tas bija iesp\u0113jams, jo NAS var\u0113ja kombin\u0113t mugurkaula un galvas elementus, iev\u0113rojot bezpilota lidapar\u0101tu (UAV) ierobe\u017eojumus. Rezum\u0113jot, lauksaimniec\u012bbas bezpilota lidapar\u0101tu (UAV) detektoru mekl\u0113\u0161anas telpa koncentr\u0113jas uz m\u0113rogojamiem, viegliem pamatelementiem un daudzpak\u0101pju apstr\u0101di, kas viss ir borta aparat\u016bras iesp\u0113ju robe\u017e\u0101s.<\/p>\n

Optimiz\u0101cijas m\u0113r\u0137i un ierobe\u017eojumi<\/h2>\n

Izvieto\u0161anas zi\u0146\u0101 apzin\u012bg\u0101m NAS sist\u0113m\u0101m ir j\u0101sp\u0113j apvienot vair\u0101kus m\u0113r\u0137us. Galvenais m\u0113r\u0137is parasti ir noteik\u0161anas precizit\u0101te (piem\u0113ram, vid\u0113j\u0101 precizit\u0101te, mAP), kas m\u0113r\u012bta kult\u016braugu uzraudz\u012bbas datu kop\u0101s. Piem\u0113ram, mAP@50 (precizit\u0101te pie 50% IOU) ir izplat\u012bts r\u0101d\u012bt\u0101js. NAS optimiz\u0113tajam modelim mAP@50 kritums bija tikai 1,96% sal\u012bdzin\u0101jum\u0101 ar b\u0101zes YOLOv8n, kas ir \u013coti mazs zaud\u0113jums, \u0146emot v\u0113r\u0101 sasniegto pieaugumu. Tiek \u0146emta v\u0113r\u0101 ar\u012b precizit\u0101te un atcer\u0113\u0161an\u0101s (vai F1 r\u0101d\u012bt\u0101js) galvenaj\u0101s klas\u0113s (nez\u0101les, kult\u016braugi).<\/p>\n

Vienlaikus ir j\u0101optimiz\u0113 latentums un ener\u0123ija. Latentums ir secin\u0101juma laiks katram att\u0113lam; iegultam GPU tas var b\u016bt 20\u201350 ms vai vair\u0101k. Zem\u0101ks latentums noz\u012bm\u0113 liel\u0101ku kadru \u0101trumu. Ener\u0123ijas pat\u0113ri\u0146\u0161 (d\u017eouli uz kadru) ir \u013coti svar\u012bgs lidojuma iztur\u012bbai. Atmi\u0146as nospiedums (parametru skaits, mode\u013ca lielums) ir v\u0113l viens ierobe\u017eojums; mode\u013ciem ir j\u0101iek\u013caujas ier\u012bces RAM. T\u0101p\u0113c NAS parasti nosaka m\u0113r\u0137i vai sodu \u0161iem ierobe\u017eojumiem.<\/p>\n

Piem\u0113ram, jebkur\u0161 modelis, kas ir l\u0113n\u0101ks par noteiktu slieksni vai p\u0101rsniedz parametra bud\u017eetu, var tikt pazemin\u0101ts. Tas faktiski p\u0101rv\u0113r\u0161 NAS par daudzm\u0113r\u0137u optimiz\u0101cijas probl\u0113mu: maksimiz\u0113t precizit\u0101ti, vienlaikus samazinot latentumu, ener\u0123iju un izm\u0113ru.<\/p>\n

Praktiski to var\u0113tu izdar\u012bt, izmantojot m\u0113r\u0137u sv\u0113rto summu vai stingrus ierobe\u017eojumus. Da\u017eas metodes pie\u0161\u0137ir lielu sodu jebkuram kandid\u0101tam, kas p\u0101rsniedz bezpilota lidapar\u0101ta jaudas ierobe\u017eojumu. Citas tie\u0161i apr\u0113\u0137ina ener\u0123ijas metriku: mode\u013ci tika test\u0113ti ONNX izpildlaik\u0101, lai izm\u0113r\u012btu \u201cenergoefektivit\u0101ti\u201d, un lab\u0101kais modelis bija par +18,5% energoefekt\u012bv\u0101ks nek\u0101 YOLOv8n. \u0160is bija viens no m\u0113r\u0137iem, kas vad\u012bja vi\u0146u mekl\u0113jumus.<\/p>\n

Atrastos kompromisus var vizualiz\u0113t uz Pareto robe\u017eas: vien\u0101 gal\u0101 - \u0101rk\u0101rt\u012bgi \u0101tri mazi mode\u013ci ar zem\u0101ku precizit\u0101ti; otr\u0101 gal\u0101 - lieli, prec\u012bzi mode\u013ci, kas ir p\u0101r\u0101k l\u0113ni vai daudz ener\u0123ijas pat\u0113r\u0113jo\u0161i dronam. Izvieto\u0161anai apzin\u012bgs NAS m\u0113r\u0137is ir atrast optim\u0101lo punktu uz \u0161\u012bs robe\u017eas, kas atbilst re\u0101laj\u0101m misijas priorit\u0101t\u0113m (piem\u0113ram, neliels precizit\u0101tes zudums lielam pa\u0101trin\u0101jumam). Rezum\u0113jot, NAS j\u0101\u0146em v\u0113r\u0101 precizit\u0101tes r\u0101d\u012bt\u0101ji (mAP, F1) un secin\u0101jumu ierobe\u017eojumi (ms uz kadru, d\u017eouli uz kadru, atmi\u0146a). \u0160\u012b l\u012bdzsvarot\u0101 optimiz\u0101cija ir tas, kas padara modeli patiesi gatavu izvieto\u0161anai bezpilota lidapar\u0101tu lieto\u0161anai.<\/p>\n

Apm\u0101c\u012bba un nov\u0113rt\u0113\u0161ana re\u0101listiskos lauksaimniec\u012bbas apst\u0101k\u013cos<\/h2>\n

Lai NAS atrastie detektori darbotos labi, tie ir j\u0101apm\u0101ca un j\u0101p\u0101rbauda ar re\u0101listiskiem lauksaimniec\u012bbas datiem. Tas noz\u012bm\u0113 izmantot datu kopas, kas atspogu\u013co re\u0101lu lauku main\u012bgumu: da\u017e\u0101das kult\u016braugu sugas, aug\u0161anas stadijas, gadalaikus, apgaismojuma apst\u0101k\u013cus un augstumus. Piem\u0113ram, apm\u0101c\u012bba, izmantojot tikai jaunu kukur\u016bzas dzinumu att\u0113lus, var netikt visp\u0101rin\u0101ta uz nobriedu\u0161\u0101m kvie\u0161u galvi\u0146\u0101m. Laukam reprezentat\u012bvas datu kopas nodro\u0161ina, ka modelis apg\u016bst saimniec\u012bbai svar\u012bgas paz\u012bmes. Apm\u0101c\u012bbas laik\u0101 var izmantot ar\u012b datu palielin\u0101\u0161anu (nejau\u0161as kult\u016bras, spilgtuma izmai\u0146as, kust\u012bbas izpl\u016b\u0161ana), lai atdarin\u0101tu dronu kust\u012bbu un apgaismojumu.<\/p>\n

\"Apm\u0101c\u012bba<\/p>\n

Veicot nov\u0113rt\u0113\u0161anu, ir svar\u012bgi p\u0101rbaud\u012bt modeli p\u0113c iesp\u0113jas re\u0101l\u0101kos apst\u0101k\u013cos. Simul\u0101cijas r\u012bki var pal\u012bdz\u0113t (piem\u0113ram, virtu\u0101la drona lido\u0161ana virs 3D laukiem), ta\u010du re\u0101li lidojumu testi ir zelta standarts. Sal\u012bdzino\u0161\u0101 nov\u0113rt\u0113\u0161ana uz borta tiek veikta, darbinot modeli uz faktisk\u0101s bezpilota lidapar\u0101ta aparat\u016bras. P\u0113c NAS vi\u0146i izvietoja kandid\u0101tu uz Jetson Nano un izm\u0113r\u012bja par 28,1% \u0101tr\u0101ku secin\u0101jumu veik\u0161anu (sal\u012bdzin\u0101jum\u0101 ar b\u0101zes YOLOv8n) un lab\u0101ku ener\u0123ijas pat\u0113ri\u0146u. \u0160\u0101da veida re\u0101l\u0101s ier\u012bces atgriezenisk\u0101 saite apstiprina, ka mekl\u0113\u0161anas rezult\u0101t\u0101 tika ieg\u016bts modelis, kas patie\u0161\u0101m atbilst pras\u012bb\u0101m.<\/p>\n

Ar\u012b visp\u0101rin\u0101\u0161ana ir \u013coti svar\u012bga. Modeli var mekl\u0113t un apm\u0101c\u012bt vienai kult\u016brai (piem\u0113ram, kvie\u0161iem), bet lauksaimniekiem ir nepiecie\u0161ami detektori, kas darbojas da\u017e\u0101dos laukos. P\u0113t\u012bjums demonstr\u0113ja sp\u0113c\u012bgu visp\u0101rin\u0101\u0161anu starp kult\u016br\u0101m: no NAS ieg\u016btais detektors, kas apm\u0101c\u012bts vienam uzdevumam, joproj\u0101m labi darboj\u0101s ar citu kult\u016bru datu kopu (kokvilnas st\u0101diem) bez atk\u0101rtotas apm\u0101c\u012bbas. Tas liecina, ka izvieto\u0161anai piel\u0101gotas NAS var rad\u012bt stabilas arhitekt\u016bras. Tom\u0113r dom\u0113nu mai\u0146as (piem\u0113ram, p\u0101reja no kukur\u016bzas laukiem uz aug\u013cu d\u0101rziem) joproj\u0101m var pras\u012bt prec\u012bzu noregul\u0113\u0161anu vai turpm\u0101ku mekl\u0113\u0161anu. Ieteicama ar\u012b test\u0113\u0161ana da\u017e\u0101dos gadalaikos (vasaras un rudens att\u0113li).<\/p>\n

Visbeidzot, katrs jauns modelis pirms ievie\u0161anas ir j\u0101p\u0101rbauda bezpilota lidapar\u0101tu (UAV) platform\u0101. Tas ietver t\u0101 precizit\u0101tes un \u0101truma re\u0123istr\u0113\u0161anu dronos, aparat\u016bras nep\u0101rkar\u0161anas nodro\u0161in\u0101\u0161anu un jaudas pat\u0113ri\u0146a p\u0101rbaudi. Tikai tad lauksaimnieki var tam uztic\u0113ties kritiski svar\u012bgas uzraudz\u012bbas veik\u0161anai. Apvienojot lauka apst\u0101k\u013ciem atbilsto\u0161u apm\u0101c\u012bbu un stingru aparat\u016bras nov\u0113rt\u0113jumu, ievie\u0161anai piel\u0101gotas NAS sist\u0113mas (NAS) nodro\u0161ina detektorus, kas ir ne tikai teor\u0113tiski efekt\u012bvi, bet ar\u012b p\u0101rbaud\u012bti praks\u0113.<\/p>\n

Priek\u0161roc\u012bbas sal\u012bdzin\u0101jum\u0101 ar manu\u0101li konstru\u0113tiem bezpilota lidapar\u0101tu detektoriem<\/h2>\n

Izvieto\u0161anai piel\u0101gotam NAS ir vair\u0101kas nep\u0101rprotamas priek\u0161roc\u012bbas sal\u012bdzin\u0101jum\u0101 ar tradicion\u0101lajiem, manu\u0101li izstr\u0101d\u0101tajiem bezpilota lidapar\u0101tu mode\u013ciem:<\/p>\n

1. Lab\u0101ki kompromisi veiktsp\u0113jas zi\u0146\u0101:<\/strong> Ar NAS atrastajiem mode\u013ciem ir tendence nodro\u0161in\u0101t augst\u0101kas precizit\u0101tes, \u0101truma un energoefektivit\u0101tes kombin\u0101cijas. Piem\u0113ram, lab\u0101kais modelis darboj\u0101s par 28% \u0101tr\u0101k un pat\u0113r\u0113ja par 18,5% maz\u0101k ener\u0123ijas uz Jetson Nano nek\u0101 manu\u0101li izv\u0113l\u0113tais YOLOv8n b\u0101zes modelis, vienlaikus zaud\u0113jot tikai ~2% noteik\u0161anas mAP. \u0160\u0101du l\u012bdzsvaru manu\u0101li pan\u0101kt b\u016btu \u013coti gr\u016bti.<\/p>\n

2. Uzlabota visp\u0101rin\u0101\u0161ana:<\/strong> NAS atkl\u0101tie mode\u013ci var b\u016bt lab\u0101k piel\u0101gojami jauniem apst\u0101k\u013ciem, jo mekl\u0113\u0161an\u0101 var iek\u013caut da\u017e\u0101dus datus vai m\u0113r\u0137us. Autom\u0101tiski izstr\u0101d\u0101tais detektors labi visp\u0101rin\u0101ja rezult\u0101tus da\u017e\u0101diem kult\u016braugu veidiem (kvie\u0161iem un kokvilnai) un apgaismojuma apst\u0101k\u013ciem. \u0160\u012b pla\u0161\u0101 robustums ir \u013coti svar\u012bgs, kad lidojumi saskaras ar negaid\u012bt\u0101m ain\u0101m.<\/p>\n

3. Samazin\u0101tas in\u017eeniertehnisk\u0101s p\u016bles:<\/strong> NAS automatiz\u0113 daudzas izm\u0113\u0123in\u0101jumu un k\u013c\u016bdu metodes. T\u0101 viet\u0101, lai manu\u0101li piel\u0101gotu sl\u0101\u0146u izm\u0113rus un p\u0101rbaud\u012btu daudzus kandid\u0101tus, izvieto\u0161anai piel\u0101gots NAS iterat\u012bvi izp\u0113ta iesp\u0113jas un atrod jums lab\u0101ko dizainu. Tas ietaupa izstr\u0101des laiku un zin\u0101\u0161anas, atvieglojot detektoru atjaunin\u0101\u0161anu jauniem uzdevumiem vai aparat\u016brai.<\/p>\n

4. M\u0113rogojam\u012bba:<\/strong> Kad NAS ietvars ir iestat\u012bts, to var izmantot da\u017e\u0101d\u0101m bezpilota lidapar\u0101tu (UAV) platform\u0101m vai misij\u0101m. Piem\u0113ram, viens un tas pats izvieto\u0161anai piel\u0101gotais NAS var\u0113tu mekl\u0113t detektoru, kas noregul\u0113ts uz citu kameras iz\u0161\u0137irtsp\u0113ju vai drona modeli, vienk\u0101r\u0161i mainot ierobe\u017eojumu ievades v\u0113rt\u012bbas. Tas ir daudz m\u0113rogojam\u0101k nek\u0101 t\u012bklu p\u0101rveido\u0161ana no nulles katram scen\u0101rijam.<\/p>\n

Izaicin\u0101jumi un ierobe\u017eojumi<\/h2>\n

Izvieto\u0161anai piel\u0101gots NAS ir sp\u0113c\u012bgs, ta\u010du tas nav br\u012bnuml\u012bdzeklis. Tas ir j\u0101izmanto p\u0101rdom\u0101ti, apzinoties t\u0101 resursu pras\u012bbas un m\u0113r\u0137a vides main\u012bgumu. Neskatoties uz savu daudzsolo\u0161o potenci\u0101lu, izvieto\u0161anai piel\u0101gotam NAS ir ar\u012b izaicin\u0101jumi:<\/p>\n

1. Augstas mekl\u0113\u0161anas izmaksas:<\/strong> NAS var b\u016bt nepiecie\u0161ama iev\u0113rojama skait\u013co\u0161anas jauda. Pat ar efekt\u012bviem algoritmiem arhitekt\u016bras telpas mekl\u0113\u0161ana var aiz\u0146emt daudzas GPU stundas (vai specializ\u0113tus skait\u013co\u0161anas resursus). Ja mekl\u0113\u0161anas pieskait\u0101m\u0101s izmaksas netiek r\u016bp\u012bgi p\u0101rvald\u012btas, da\u017e\u0101m komand\u0101m t\u0101s var b\u016bt p\u0101r\u0101k lielas.<\/p>\n

2. Datu neobjektivit\u0101te un dom\u0113na nob\u012bde:<\/strong> NAS ir tikpat labs, cik labi ir izmantotie dati. Ja apm\u0101c\u012bbas att\u0113li neatspogu\u013co lauka apst\u0101k\u013cus, atrast\u0101 arhitekt\u016bra realit\u0101t\u0113 var nedarboties labi. Piem\u0113ram, modelis, kas piel\u0101gots vienam kult\u016braugu veidam vai vienam \u0123eogr\u0101fiskajam re\u0123ionam, var nepiln\u012bgi p\u0101riet uz citu bez turpm\u0101kas piel\u0101go\u0161anas.<\/p>\n

3. Aparat\u016bras neviendab\u012bgums:<\/strong> Bezpilota lidapar\u0101tu aparat\u016bra ir pieejama da\u017e\u0101d\u0101s versij\u0101s (da\u017e\u0101di iegultie grafiskie procesori, centr\u0101lie procesori, FPGA). Modelis, kas optimiz\u0113ts vienai platei, var neb\u016bt optim\u0101ls citai. Izvieto\u0161anas zi\u0146\u0101 orient\u0113tiem NAS ir vai nu atk\u0101rtoti j\u0101veic mekl\u0113\u0161ana katrai platformai, vai j\u0101izmanto konservat\u012bvi ierobe\u017eojumi, kas atbilst vis\u0101m, kas var ierobe\u017eot veiktsp\u0113ju.<\/p>\n

4. Praktiski ierobe\u017eojumi:<\/strong> Re\u0101la lauksaimniec\u012bbas ievie\u0161ana ietver t\u0101dus jaut\u0101jumus k\u0101 t\u012bkla atjaunin\u0101jumi pa \u0113teru, sist\u0113mas integr\u0101cija ar lidojumu vad\u012bbu un dro\u0161\u012bbas sertifik\u0101cija. Pat lab\u0101kais NAS modelis ir j\u0101integr\u0113 pilnv\u0113rt\u012bg\u0101 dronu sist\u0113m\u0101. Mode\u013ca atjaunin\u0101jumu koordin\u0113\u0161ana, normat\u012bvo aktu apstiprin\u0101jumi un lauksaimnieku apm\u0101c\u012bba ir netehniski \u0161\u0137\u0113r\u0161\u013ci.<\/p>\n

N\u0101kotnes virzieni<\/h2>\n

N\u0101kotn\u0113, visticam\u0101k, mode\u013ca dizaina, sensoru tehnolo\u0123ijas un bezpilota lidapar\u0101tu vad\u012bbas integr\u0101cija b\u016bs v\u0113l cie\u0161\u0101ka. Izvieto\u0161anai piel\u0101gotas NAS joproj\u0101m b\u016bs galvenais instruments \u0161aj\u0101 kopdizaina proces\u0101. Raugoties n\u0101kotn\u0113, rodas vair\u0101kas aizraujo\u0161as iesp\u0113jas:<\/p>\n

1. Tie\u0161saistes un adapt\u012bv\u0101s NAS:<\/strong> T\u0101 viet\u0101, lai veiktu vienreiz\u0113ju bezsaistes mekl\u0113\u0161anu, n\u0101kotnes sist\u0113mas var\u0113tu piel\u0101got t\u012bklu re\u0101llaik\u0101 vai starp lidojumiem. Piem\u0113ram, drons var\u0113tu s\u0101kt darbu ar b\u0101zes modeli un, izmantojot vieglus NAS algoritmus, piel\u0101goties, lai acumirkl\u012b tiktu gal\u0101 ar jauniem apgaismojuma vai reljefa apst\u0101k\u013ciem. \u0160\u012b \u201cier\u012bc\u0113 ieb\u016bv\u0113t\u0101 NAS\u201d ir \u013coti sare\u017e\u0123\u012bta, ta\u010du t\u0101 var\u0113tu iev\u0113rojami uzlabot piel\u0101go\u0161an\u0101s sp\u0113jas.<\/p>\n

2. Sensoru un mode\u013cu kop\u012bga izstr\u0101de:<\/strong> N\u0101kotnes prec\u012bz\u0101s lauksaimniec\u012bbas sist\u0113mas var\u0113tu kop\u012bgi optimiz\u0113t kameras (RGB, multispektr\u0101l\u0101, infrasarkan\u0101) un neironu t\u012bkla izv\u0113li. Izvieto\u0161anas zi\u0146\u0101 apzin\u012bgs NAS var\u0113tu papla\u0161in\u0101ties, iek\u013caujot sensoru parametrus (piem\u0113ram, izmantot\u0101s spektra joslas) sav\u0101 mekl\u0113\u0161an\u0101, atrodot lab\u0101ko aparat\u016bras un mode\u013ca kombin\u0101ciju.<\/p>\n

3. Multispektr\u0101l\u0101\/hiperspektr\u0101l\u0101 integr\u0101cija:<\/strong> K\u0101 liecina kokvilnas slim\u012bbas p\u0113t\u012bjums, multispektr\u0101lu att\u0113lu integr\u0113\u0161ana var uzlabot atkl\u0101\u0161anu, \u012bpa\u0161i agr\u012bn\u0101s stadijas probl\u0113mu gad\u012bjum\u0101. N\u0101kotnes NAS var\u0113tu izp\u0113t\u012bt daudzstraumju mode\u013cus, kas apvieno RGB un tuv\u0101 infrasarkan\u0101 starojuma kan\u0101lus, lai uzticam\u0101k noteiktu smalkas augu izmai\u0146as.<\/p>\n

4. Autonom\u0101s l\u0113mumu pie\u0146em\u0161anas procesi:<\/strong> Galu gal\u0101 NAS optimiz\u0113ti detektori var\u0113tu nodro\u0161in\u0101t piln\u012bgu autonomiju. Piem\u0113ram, drons var\u0113tu autom\u0101tiski \u0123ener\u0113t smidzin\u0101\u0161anas pl\u0101nu vai br\u012bdin\u0101t saimniec\u012bbas vad\u012bt\u0101jus, ja tas konstat\u0113 noteiktus apst\u0101k\u013cus. Izvieto\u0161anas zi\u0146\u0101 apzin\u012bgus NAS var\u0113tu papla\u0161in\u0101t l\u012bdz pilniem cauru\u013cvadiem (noteik\u0161anas + darb\u012bbas mode\u013ci), optimiz\u0113jot visu sist\u0113mu.<\/p>\n

5. \u0112tiskie un vides apsv\u0113rumi:<\/strong> Bezpilota lidapar\u0101tiem (UAV) k\u013c\u016bstot arvien sp\u0113j\u012bg\u0101kiem, mums j\u0101\u0146em v\u0113r\u0101 priv\u0101tums, gaisa telpas dro\u0161\u012bba un ietekme uz lauksaimniec\u012bbas darbasp\u0113ku (k\u0101 nor\u0101d\u012bju\u0161i Agravals un Arafats). Svar\u012bgs n\u0101kotnes m\u0113r\u0137is ir nodro\u0161in\u0101t, lai NAS optimiz\u0113ti droni lauksaimniec\u012bb\u0101 tiktu izmantoti atbild\u012bgi.<\/p>\n

Secin\u0101jums<\/h2>\n

Izvieto\u0161anas zi\u0146\u0101 apzin\u012bga NAS ir jaud\u012bga pieeja, lai piel\u0101gotu vieglus objektu detektorus bezpilota lidapar\u0101tu (UAV) kult\u016braugu uzraudz\u012bbai. Iek\u013caujot mekl\u0113\u0161an\u0101 bezpilota lidapar\u0101tu (UAV) aparat\u016bru un misijas ierobe\u017eojumus, t\u0101 \u0123ener\u0113 mode\u013cus, kas ietaupa apr\u0113\u0137inus un ener\u0123iju, neupur\u0113jot lielu precizit\u0101ti. Piem\u0113ram, nesen veikts darbs par\u0101d\u012bja, ka NAS izstr\u0101d\u0101ts detektors izmanto par 37% maz\u0101k FLOP un 61% maz\u0101k parametru nek\u0101 atsauces YOLOv8n, tom\u0113r t\u0101 mAP samazin\u0101j\u0101s tikai par ~2%.<\/p>\n

Faktiskaj\u0101 dronu aparat\u016br\u0101 tas noz\u012bm\u0113ja \u0101tr\u0101ku 28% secin\u0101jumu veik\u0161anu un lab\u0101ku 18% energoefektivit\u0101ti. \u0160\u0101di ieguvumi noz\u012bm\u0113 ilg\u0101ku lidojuma laiku, \u0101tr\u0101ku anal\u012bzi un atsauc\u012bg\u0101ku lauksaimniec\u012bbas atbalstu. Sal\u012bdzinot ar manu\u0101li izstr\u0101d\u0101tiem mode\u013ciem, izvieto\u0161anai piel\u0101gotais NAS nodro\u0161ina lab\u0101ku veiktsp\u0113jas visp\u0101rin\u0101\u0161anu, maz\u0101ku manu\u0101las regul\u0113\u0161anas piep\u016bli un m\u0113rogojam\u012bbu jaun\u0101m bezpilota lidapar\u0101tu platform\u0101m.<\/p>\n

Prec\u012bz\u0101s lauksaimniec\u012bbas kontekst\u0101 \u0161ie uzlabojumi var padar\u012bt bezpilota lidapar\u0101tu (UAV) kult\u016braugu uzraudz\u012bbu praktisk\u0101ku un efekt\u012bv\u0101ku. Droni, kas apr\u012bkoti ar NAS optimiz\u0113tiem detektoriem, var uzticam\u0101k noteikt nez\u0101les, kait\u0113k\u013cus vai stresu, nodro\u0161inot savlaic\u012bgu iejauk\u0161anos, kas ietaupa resursus un palielina ra\u017eu. T\u0101 k\u0101 lauksaimniec\u012bba turpina ieviest dronus un m\u0101ksl\u012bgo intelektu, izvieto\u0161anai apzin\u012bg\u0101m NAS b\u016bs galven\u0101 loma, lai nodro\u0161in\u0101tu, ka mode\u013ci, kas darbojas uz \u0161iem droniem, ir efekt\u012bvi, prec\u012bzi un gatavi darbam lauk\u0101. Tas savieno jaun\u0101kos neironu t\u012bklu p\u0113t\u012bjumus un lauksaimnieku praktisk\u0101s vajadz\u012bbas, pal\u012bdzot veicin\u0101t uz datiem balst\u012btas, prec\u012bz\u0101s lauksaimniec\u012bbas n\u0101kotni.<\/p>","protected":false},"excerpt":{"rendered":"

Bezpilota lidapar\u0101ti (UAV) jeb droni p\u0101rveido m\u016bsdienu lauksaimniec\u012bbu, nodro\u0161inot \u0101tru lauku skatu no gaisa. Tos izmanto, lai sken\u0113tu kult\u016braugu vesel\u012bbu,\u2026<\/p>","protected":false},"author":210157960,"featured_media":12831,"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":[1377],"tags":[],"class_list":["post-12824","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-crop-monitoring"],"acf":[],"yoast_head":"\nRole of Deployment-Aware NAS for Efficient UAV-Based Crop Monitoring - GeoPard<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/lv\/emuars\/izvietosanas-informetu-nasa-loma-efektiva-bezpilota-lidaparatu-uav-kulturaugu-uzraudziba\/\" \/>\n<meta property=\"og:locale\" content=\"lv_LV\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Role of Deployment-Aware NAS for Efficient UAV-Based Crop Monitoring - GeoPard\" \/>\n<meta property=\"og:description\" content=\"Unmanned Aerial Vehicles (UAVs), or drones, are transforming modern agriculture by providing fast, aerial views of fields. 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