FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308748696> ?p ?o ?g. }

• W4308748696 endingPage "669" @default.
• W4308748696 startingPage "651" @default.
• W4308748696 abstract "AbstractClimate and agriculture depend on each other. A small change in climate can highly affect agriculture in a positive or negative manner. Agriculture also affects the climate. Artificial Intelligence (AI) algorithms play a very crucial role in agricultural system maintenance and its enhancement. In this paper, we have briefly summarized research done based on application of artificial intelligence techniques in field condition management of the agricultural system. The motive of this paper is to discuss how machine learning technologies are beneficial for crop field management in order to increase agricultural productivity using minimal natural resources like water and land. The field condition management is mainly categorized into two sub-categories: water management and soil health management.KeywordsMachine learningField condition managementWater managementSoil health management" @default.
• W4308748696 created "2022-11-15" @default.
• W4308748696 creator A5008249100 @default.
• W4308748696 creator A5026888765 @default.
• W4308748696 date "2022-11-10" @default.
• W4308748696 modified "2023-09-29" @default.
• W4308748696 title "Climate Dependent Crop Field Condition Management Through Data Modeling" @default.
• W4308748696 cites W1179140510 @default.
• W4308748696 cites W2065165083 @default.
• W4308748696 cites W2149140091 @default.
• W4308748696 cites W2157005989 @default.
• W4308748696 cites W2270460811 @default.
• W4308748696 cites W2273322351 @default.
• W4308748696 cites W2325879942 @default.
• W4308748696 cites W2339447311 @default.
• W4308748696 cites W2399675776 @default.
• W4308748696 cites W2603417106 @default.
• W4308748696 cites W2617732000 @default.
• W4308748696 cites W2619390517 @default.
• W4308748696 cites W2790675133 @default.
• W4308748696 cites W2790861445 @default.
• W4308748696 cites W2802436364 @default.
• W4308748696 cites W2807694716 @default.
• W4308748696 cites W2883090033 @default.
• W4308748696 cites W2884634948 @default.
• W4308748696 cites W2888611374 @default.
• W4308748696 cites W2889246260 @default.
• W4308748696 cites W2893164490 @default.
• W4308748696 cites W2893301845 @default.
• W4308748696 cites W2896625020 @default.
• W4308748696 cites W2901988499 @default.
• W4308748696 cites W2902947938 @default.
• W4308748696 cites W2920825860 @default.
• W4308748696 cites W2921258475 @default.
• W4308748696 cites W2921467030 @default.
• W4308748696 cites W2923053165 @default.
• W4308748696 cites W2951230751 @default.
• W4308748696 cites W2984652957 @default.
• W4308748696 cites W2991192488 @default.
• W4308748696 cites W3004538792 @default.
• W4308748696 cites W3004921227 @default.
• W4308748696 cites W3006263833 @default.
• W4308748696 cites W3010047009 @default.
• W4308748696 cites W3010716126 @default.
• W4308748696 cites W3014183577 @default.
• W4308748696 cites W3016654606 @default.
• W4308748696 cites W3033828772 @default.
• W4308748696 cites W3041846944 @default.
• W4308748696 cites W3088953716 @default.
• W4308748696 cites W3096212062 @default.
• W4308748696 cites W3097935099 @default.
• W4308748696 cites W3157440915 @default.
• W4308748696 cites W3162940529 @default.
• W4308748696 cites W4200160657 @default.
• W4308748696 doi "https://doi.org/10.1007/978-981-19-3148-2_57" @default.
• W4308748696 hasPublicationYear "2022" @default.
• W4308748696 type Work @default.
• W4308748696 citedByCount "0" @default.
• W4308748696 crossrefType "book-chapter" @default.
• W4308748696 hasAuthorship W4308748696A5008249100 @default.
• W4308748696 hasAuthorship W4308748696A5026888765 @default.
• W4308748696 hasConcept C107826830 @default.
• W4308748696 hasConcept C118518473 @default.
• W4308748696 hasConcept C127413603 @default.
• W4308748696 hasConcept C139719470 @default.
• W4308748696 hasConcept C162324750 @default.
• W4308748696 hasConcept C166957645 @default.
• W4308748696 hasConcept C202444582 @default.
• W4308748696 hasConcept C204983608 @default.
• W4308748696 hasConcept C205649164 @default.
• W4308748696 hasConcept C2994553254 @default.
• W4308748696 hasConcept C33923547 @default.
• W4308748696 hasConcept C39432304 @default.
• W4308748696 hasConcept C41008148 @default.
• W4308748696 hasConcept C54286561 @default.
• W4308748696 hasConcept C88463610 @default.
• W4308748696 hasConcept C9652623 @default.
• W4308748696 hasConceptScore W4308748696C107826830 @default.
• W4308748696 hasConceptScore W4308748696C118518473 @default.
• W4308748696 hasConceptScore W4308748696C127413603 @default.
• W4308748696 hasConceptScore W4308748696C139719470 @default.
• W4308748696 hasConceptScore W4308748696C162324750 @default.
• W4308748696 hasConceptScore W4308748696C166957645 @default.
• W4308748696 hasConceptScore W4308748696C202444582 @default.
• W4308748696 hasConceptScore W4308748696C204983608 @default.
• W4308748696 hasConceptScore W4308748696C205649164 @default.
• W4308748696 hasConceptScore W4308748696C2994553254 @default.
• W4308748696 hasConceptScore W4308748696C33923547 @default.
• W4308748696 hasConceptScore W4308748696C39432304 @default.
• W4308748696 hasConceptScore W4308748696C41008148 @default.
• W4308748696 hasConceptScore W4308748696C54286561 @default.
• W4308748696 hasConceptScore W4308748696C88463610 @default.
• W4308748696 hasConceptScore W4308748696C9652623 @default.
• W4308748696 hasLocation W43087486961 @default.
• W4308748696 hasOpenAccess W4308748696 @default.
• W4308748696 hasPrimaryLocation W43087486961 @default.
• W4308748696 hasRelatedWork W1594372992 @default.
• W4308748696 hasRelatedWork W2072020304 @default.

• next