FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366293732> ?p ?o ?g. }

• W4366293732 abstract "The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries." @default.
• W4366293732 created "2023-04-20" @default.
• W4366293732 creator A5011159160 @default.
• W4366293732 creator A5013168917 @default.
• W4366293732 creator A5063616750 @default.
• W4366293732 creator A5071137947 @default.
• W4366293732 creator A5083574013 @default.
• W4366293732 creator A5090761389 @default.
• W4366293732 date "2023-04-18" @default.
• W4366293732 modified "2023-09-26" @default.
• W4366293732 title "Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images" @default.
• W4366293732 cites W2014847057 @default.
• W4366293732 cites W2023912087 @default.
• W4366293732 cites W2116627051 @default.
• W4366293732 cites W2188225820 @default.
• W4366293732 cites W2395611524 @default.
• W4366293732 cites W2412782625 @default.
• W4366293732 cites W2465503420 @default.
• W4366293732 cites W2583513334 @default.
• W4366293732 cites W2604086375 @default.
• W4366293732 cites W2613013283 @default.
• W4366293732 cites W2904918578 @default.
• W4366293732 cites W2919115771 @default.
• W4366293732 cites W2964025915 @default.
• W4366293732 cites W2964121744 @default.
• W4366293732 cites W2970979186 @default.
• W4366293732 cites W2977208407 @default.
• W4366293732 cites W2979941465 @default.
• W4366293732 cites W2982457828 @default.
• W4366293732 cites W2995059005 @default.
• W4366293732 cites W3000627240 @default.
• W4366293732 cites W3008439211 @default.
• W4366293732 cites W3009441850 @default.
• W4366293732 cites W3139711038 @default.
• W4366293732 cites W3194224793 @default.
• W4366293732 cites W3195067547 @default.
• W4366293732 cites W3202224891 @default.
• W4366293732 cites W4213446434 @default.
• W4366293732 cites W4220777013 @default.
• W4366293732 cites W4224269597 @default.
• W4366293732 cites W4229370562 @default.
• W4366293732 cites W4298137862 @default.
• W4366293732 doi "https://doi.org/10.3389/fpls.2023.1124939" @default.
• W4366293732 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37426958" @default.
• W4366293732 hasPublicationYear "2023" @default.
• W4366293732 type Work @default.
• W4366293732 citedByCount "0" @default.
• W4366293732 crossrefType "journal-article" @default.
• W4366293732 hasAuthorship W4366293732A5011159160 @default.
• W4366293732 hasAuthorship W4366293732A5013168917 @default.
• W4366293732 hasAuthorship W4366293732A5063616750 @default.
• W4366293732 hasAuthorship W4366293732A5071137947 @default.
• W4366293732 hasAuthorship W4366293732A5083574013 @default.
• W4366293732 hasAuthorship W4366293732A5090761389 @default.
• W4366293732 hasBestOaLocation W43662937321 @default.
• W4366293732 hasConcept C111919701 @default.
• W4366293732 hasConcept C116834253 @default.
• W4366293732 hasConcept C118505674 @default.
• W4366293732 hasConcept C127162648 @default.
• W4366293732 hasConcept C153180895 @default.
• W4366293732 hasConcept C154945302 @default.
• W4366293732 hasConcept C173163844 @default.
• W4366293732 hasConcept C202444582 @default.
• W4366293732 hasConcept C205649164 @default.
• W4366293732 hasConcept C2524010 @default.
• W4366293732 hasConcept C2777210771 @default.
• W4366293732 hasConcept C33923547 @default.
• W4366293732 hasConcept C41008148 @default.
• W4366293732 hasConcept C58640448 @default.
• W4366293732 hasConcept C59822182 @default.
• W4366293732 hasConcept C64543145 @default.
• W4366293732 hasConcept C76155785 @default.
• W4366293732 hasConcept C81363708 @default.
• W4366293732 hasConcept C81669768 @default.
• W4366293732 hasConcept C86803240 @default.
• W4366293732 hasConcept C9652623 @default.
• W4366293732 hasConceptScore W4366293732C111919701 @default.
• W4366293732 hasConceptScore W4366293732C116834253 @default.
• W4366293732 hasConceptScore W4366293732C118505674 @default.
• W4366293732 hasConceptScore W4366293732C127162648 @default.
• W4366293732 hasConceptScore W4366293732C153180895 @default.
• W4366293732 hasConceptScore W4366293732C154945302 @default.
• W4366293732 hasConceptScore W4366293732C173163844 @default.
• W4366293732 hasConceptScore W4366293732C202444582 @default.
• W4366293732 hasConceptScore W4366293732C205649164 @default.
• W4366293732 hasConceptScore W4366293732C2524010 @default.
• W4366293732 hasConceptScore W4366293732C2777210771 @default.
• W4366293732 hasConceptScore W4366293732C33923547 @default.
• W4366293732 hasConceptScore W4366293732C41008148 @default.
• W4366293732 hasConceptScore W4366293732C58640448 @default.
• W4366293732 hasConceptScore W4366293732C59822182 @default.
• W4366293732 hasConceptScore W4366293732C64543145 @default.
• W4366293732 hasConceptScore W4366293732C76155785 @default.
• W4366293732 hasConceptScore W4366293732C81363708 @default.
• W4366293732 hasConceptScore W4366293732C81669768 @default.
• W4366293732 hasConceptScore W4366293732C86803240 @default.
• W4366293732 hasConceptScore W4366293732C9652623 @default.
• W4366293732 hasLocation W43662937321 @default.
• W4366293732 hasLocation W43662937322 @default.
• W4366293732 hasLocation W43662937323 @default.

• next