Matches in SemOpenAlex for { <https://semopenalex.org/work/W3193011253> ?p ?o ?g. }
- W3193011253 endingPage "467" @default.
- W3193011253 startingPage "458" @default.
- W3193011253 abstract "Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention." @default.
- W3193011253 created "2021-08-16" @default.
- W3193011253 creator A5025593507 @default.
- W3193011253 creator A5027618321 @default.
- W3193011253 creator A5030075617 @default.
- W3193011253 date "2021-09-01" @default.
- W3193011253 modified "2023-10-16" @default.
- W3193011253 title "Automated Pest Detection With DNN on the Edge for Precision Agriculture" @default.
- W3193011253 cites W1964906997 @default.
- W3193011253 cites W2003007706 @default.
- W3193011253 cites W2016348716 @default.
- W3193011253 cites W2025070345 @default.
- W3193011253 cites W2051846739 @default.
- W3193011253 cites W2051864286 @default.
- W3193011253 cites W2058610492 @default.
- W3193011253 cites W2061013853 @default.
- W3193011253 cites W2067361549 @default.
- W3193011253 cites W2088049833 @default.
- W3193011253 cites W2104879371 @default.
- W3193011253 cites W2134769985 @default.
- W3193011253 cites W2183182206 @default.
- W3193011253 cites W2205610530 @default.
- W3193011253 cites W2285671993 @default.
- W3193011253 cites W2414497598 @default.
- W3193011253 cites W2488196994 @default.
- W3193011253 cites W2528149772 @default.
- W3193011253 cites W2593776451 @default.
- W3193011253 cites W2766191760 @default.
- W3193011253 cites W2789000374 @default.
- W3193011253 cites W2794026873 @default.
- W3193011253 cites W2806033632 @default.
- W3193011253 cites W2883992358 @default.
- W3193011253 cites W2887338779 @default.
- W3193011253 cites W2889024854 @default.
- W3193011253 cites W2889638344 @default.
- W3193011253 cites W2894759909 @default.
- W3193011253 cites W2938719104 @default.
- W3193011253 cites W2963163009 @default.
- W3193011253 cites W2967435224 @default.
- W3193011253 cites W2968162147 @default.
- W3193011253 cites W2968729794 @default.
- W3193011253 cites W2987383153 @default.
- W3193011253 cites W2988271927 @default.
- W3193011253 cites W3005103743 @default.
- W3193011253 cites W3022470745 @default.
- W3193011253 cites W3024847709 @default.
- W3193011253 cites W3025474997 @default.
- W3193011253 cites W3042909640 @default.
- W3193011253 cites W3089559094 @default.
- W3193011253 cites W3098579617 @default.
- W3193011253 cites W3104805932 @default.
- W3193011253 cites W3108690123 @default.
- W3193011253 cites W3128141573 @default.
- W3193011253 cites W3144897352 @default.
- W3193011253 cites W4234536190 @default.
- W3193011253 doi "https://doi.org/10.1109/jetcas.2021.3101740" @default.
- W3193011253 hasPublicationYear "2021" @default.
- W3193011253 type Work @default.
- W3193011253 sameAs 3193011253 @default.
- W3193011253 citedByCount "36" @default.
- W3193011253 countsByYear W31930112532022 @default.
- W3193011253 countsByYear W31930112532023 @default.
- W3193011253 crossrefType "journal-article" @default.
- W3193011253 hasAuthorship W3193011253A5025593507 @default.
- W3193011253 hasAuthorship W3193011253A5027618321 @default.
- W3193011253 hasAuthorship W3193011253A5030075617 @default.
- W3193011253 hasBestOaLocation W31930112531 @default.
- W3193011253 hasConcept C118518473 @default.
- W3193011253 hasConcept C119857082 @default.
- W3193011253 hasConcept C127413603 @default.
- W3193011253 hasConcept C149635348 @default.
- W3193011253 hasConcept C154945302 @default.
- W3193011253 hasConcept C162307627 @default.
- W3193011253 hasConcept C18903297 @default.
- W3193011253 hasConcept C201995342 @default.
- W3193011253 hasConcept C22508944 @default.
- W3193011253 hasConcept C2776451879 @default.
- W3193011253 hasConcept C2780451532 @default.
- W3193011253 hasConcept C41008148 @default.
- W3193011253 hasConcept C59822182 @default.
- W3193011253 hasConcept C6557445 @default.
- W3193011253 hasConcept C86803240 @default.
- W3193011253 hasConcept C88463610 @default.
- W3193011253 hasConceptScore W3193011253C118518473 @default.
- W3193011253 hasConceptScore W3193011253C119857082 @default.
- W3193011253 hasConceptScore W3193011253C127413603 @default.
- W3193011253 hasConceptScore W3193011253C149635348 @default.
- W3193011253 hasConceptScore W3193011253C154945302 @default.
- W3193011253 hasConceptScore W3193011253C162307627 @default.
- W3193011253 hasConceptScore W3193011253C18903297 @default.
- W3193011253 hasConceptScore W3193011253C201995342 @default.
- W3193011253 hasConceptScore W3193011253C22508944 @default.
- W3193011253 hasConceptScore W3193011253C2776451879 @default.
- W3193011253 hasConceptScore W3193011253C2780451532 @default.
- W3193011253 hasConceptScore W3193011253C41008148 @default.
- W3193011253 hasConceptScore W3193011253C59822182 @default.
- W3193011253 hasConceptScore W3193011253C6557445 @default.
- W3193011253 hasConceptScore W3193011253C86803240 @default.