Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281752662> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4281752662 endingPage "107088" @default.
- W4281752662 startingPage "107088" @default.
- W4281752662 abstract "Early grapevine yield forecasting at satisfactory accuracy is among the major trends in precision viticulture research. Conventionally, yield is estimated manually through extrapolation right before harvest, which is mostly inaccurate, requires considerable labor as well as resources, and is often destructive. The number of flowers per vine is one of the main determinants of grapevine yield and can be used as an early indicator for viticulture yield forecasting. In the present study, a non-invasive, automated image analysis framework was proposed for quantifying flowers in unstructured RGB images of grapevine canopies. Images were automatically acquired under field conditions at night, using a mobile sensing platform equipped with artificial illumination. Due to the small shape and dense distribution of individual flowers (a few hundred per inflorescence) and the similar hue of all plant organs in the fore- and background, efficient flower quantification in images is challenging. To overcome this, we adopted a two-step segmentation approach in our algorithm. First, image regions containing inflorescences were recognized and extracted by segmenting each instance of inflorescences in the image using a mask region-based convolutional neural network (Mask R-CNN), followed by test-time augmentation post-processing to achieve high accuracy. Finally, individual flowers in the extracted inflorescences were detected and quantified using another Mask R-CNN model preceded by a contrast enhancement pre-processing operation. For efficient segmentation and quantification of individual flowers, a high-resolution full image was split down into smaller patches and processed in multiple iterations during inference. The algorithm yielded significant performances, with F1 score values of 0.943 and 0.903 for inflorescence segmentation and single flower detection tasks, respectively, against a test set of 75 images from three different cultivars. A determination coefficient (R2) of 0.98 and a normalized root mean square error of 12.24% were obtained in the test set between the automatic flower number quantification and manual counts. In conclusion, the proposed algorithm constitutes a promising approach for automatically predicting yield potential in the early stages of grapevine development, and it can be used for objective monitoring and optimal management of commercial vineyards." @default.
- W4281752662 created "2022-06-13" @default.
- W4281752662 creator A5035192712 @default.
- W4281752662 creator A5080607707 @default.
- W4281752662 creator A5082336948 @default.
- W4281752662 date "2022-07-01" @default.
- W4281752662 modified "2023-10-18" @default.
- W4281752662 title "Deep learning-based accurate grapevine inflorescence and flower quantification in unstructured vineyard images acquired using a mobile sensing platform" @default.
- W4281752662 cites W1489141615 @default.
- W4281752662 cites W1529582020 @default.
- W4281752662 cites W1536680647 @default.
- W4281752662 cites W1873259364 @default.
- W4281752662 cites W1892082890 @default.
- W4281752662 cites W1974198285 @default.
- W4281752662 cites W1986630321 @default.
- W4281752662 cites W2006976511 @default.
- W4281752662 cites W2010210524 @default.
- W4281752662 cites W2028045291 @default.
- W4281752662 cites W2123820573 @default.
- W4281752662 cites W2147394055 @default.
- W4281752662 cites W2167594433 @default.
- W4281752662 cites W2186007545 @default.
- W4281752662 cites W2199275510 @default.
- W4281752662 cites W2341763449 @default.
- W4281752662 cites W2371286447 @default.
- W4281752662 cites W2516151456 @default.
- W4281752662 cites W2604559464 @default.
- W4281752662 cites W2773985483 @default.
- W4281752662 cites W2809959615 @default.
- W4281752662 cites W2905387399 @default.
- W4281752662 cites W2943309330 @default.
- W4281752662 cites W2963881378 @default.
- W4281752662 cites W2972898997 @default.
- W4281752662 cites W2997004889 @default.
- W4281752662 cites W3012478585 @default.
- W4281752662 cites W3017378934 @default.
- W4281752662 cites W3088481470 @default.
- W4281752662 cites W3090825734 @default.
- W4281752662 cites W3095523211 @default.
- W4281752662 cites W3158325015 @default.
- W4281752662 cites W3196732803 @default.
- W4281752662 doi "https://doi.org/10.1016/j.compag.2022.107088" @default.
- W4281752662 hasPublicationYear "2022" @default.
- W4281752662 type Work @default.
- W4281752662 citedByCount "6" @default.
- W4281752662 countsByYear W42817526622023 @default.
- W4281752662 crossrefType "journal-article" @default.
- W4281752662 hasAuthorship W4281752662A5035192712 @default.
- W4281752662 hasAuthorship W4281752662A5080607707 @default.
- W4281752662 hasAuthorship W4281752662A5082336948 @default.
- W4281752662 hasBestOaLocation W42817526621 @default.
- W4281752662 hasConcept C124504099 @default.
- W4281752662 hasConcept C144027150 @default.
- W4281752662 hasConcept C153180895 @default.
- W4281752662 hasConcept C154945302 @default.
- W4281752662 hasConcept C178165689 @default.
- W4281752662 hasConcept C2780924976 @default.
- W4281752662 hasConcept C31972630 @default.
- W4281752662 hasConcept C41008148 @default.
- W4281752662 hasConcept C59822182 @default.
- W4281752662 hasConcept C81363708 @default.
- W4281752662 hasConcept C86803240 @default.
- W4281752662 hasConcept C89600930 @default.
- W4281752662 hasConceptScore W4281752662C124504099 @default.
- W4281752662 hasConceptScore W4281752662C144027150 @default.
- W4281752662 hasConceptScore W4281752662C153180895 @default.
- W4281752662 hasConceptScore W4281752662C154945302 @default.
- W4281752662 hasConceptScore W4281752662C178165689 @default.
- W4281752662 hasConceptScore W4281752662C2780924976 @default.
- W4281752662 hasConceptScore W4281752662C31972630 @default.
- W4281752662 hasConceptScore W4281752662C41008148 @default.
- W4281752662 hasConceptScore W4281752662C59822182 @default.
- W4281752662 hasConceptScore W4281752662C81363708 @default.
- W4281752662 hasConceptScore W4281752662C86803240 @default.
- W4281752662 hasConceptScore W4281752662C89600930 @default.
- W4281752662 hasLocation W42817526621 @default.
- W4281752662 hasOpenAccess W4281752662 @default.
- W4281752662 hasPrimaryLocation W42817526621 @default.
- W4281752662 hasRelatedWork W1669643531 @default.
- W4281752662 hasRelatedWork W1982826852 @default.
- W4281752662 hasRelatedWork W2005437358 @default.
- W4281752662 hasRelatedWork W2008656436 @default.
- W4281752662 hasRelatedWork W2023558673 @default.
- W4281752662 hasRelatedWork W2110230079 @default.
- W4281752662 hasRelatedWork W2134924024 @default.
- W4281752662 hasRelatedWork W2517104666 @default.
- W4281752662 hasRelatedWork W2613186388 @default.
- W4281752662 hasRelatedWork W1967061043 @default.
- W4281752662 hasVolume "198" @default.
- W4281752662 isParatext "false" @default.
- W4281752662 isRetracted "false" @default.
- W4281752662 workType "article" @default.