Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200229166> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W4200229166 endingPage "3" @default.
- W4200229166 startingPage "3" @default.
- W4200229166 abstract "Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease." @default.
- W4200229166 created "2021-12-31" @default.
- W4200229166 creator A5003516929 @default.
- W4200229166 creator A5004550929 @default.
- W4200229166 creator A5050756789 @default.
- W4200229166 creator A5059247248 @default.
- W4200229166 creator A5069431519 @default.
- W4200229166 date "2021-12-22" @default.
- W4200229166 modified "2023-09-26" @default.
- W4200229166 title "Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning" @default.
- W4200229166 cites W2066267578 @default.
- W4200229166 cites W2473156356 @default.
- W4200229166 cites W2765600881 @default.
- W4200229166 cites W2915638174 @default.
- W4200229166 cites W2962766617 @default.
- W4200229166 cites W2987761081 @default.
- W4200229166 cites W3004558629 @default.
- W4200229166 cites W3005863531 @default.
- W4200229166 cites W3021970402 @default.
- W4200229166 cites W3026381557 @default.
- W4200229166 cites W3040466010 @default.
- W4200229166 cites W3080996525 @default.
- W4200229166 cites W3093722261 @default.
- W4200229166 cites W3112760320 @default.
- W4200229166 cites W3115190122 @default.
- W4200229166 cites W3120255147 @default.
- W4200229166 cites W3121264466 @default.
- W4200229166 cites W3134209837 @default.
- W4200229166 cites W3139727712 @default.
- W4200229166 cites W3148302677 @default.
- W4200229166 cites W3157905703 @default.
- W4200229166 cites W3158114921 @default.
- W4200229166 cites W3196180073 @default.
- W4200229166 cites W3196840230 @default.
- W4200229166 cites W3205172723 @default.
- W4200229166 doi "https://doi.org/10.3390/drones6010003" @default.
- W4200229166 hasPublicationYear "2021" @default.
- W4200229166 type Work @default.
- W4200229166 citedByCount "10" @default.
- W4200229166 countsByYear W42002291662022 @default.
- W4200229166 countsByYear W42002291662023 @default.
- W4200229166 crossrefType "journal-article" @default.
- W4200229166 hasAuthorship W4200229166A5003516929 @default.
- W4200229166 hasAuthorship W4200229166A5004550929 @default.
- W4200229166 hasAuthorship W4200229166A5050756789 @default.
- W4200229166 hasAuthorship W4200229166A5059247248 @default.
- W4200229166 hasAuthorship W4200229166A5069431519 @default.
- W4200229166 hasBestOaLocation W42002291661 @default.
- W4200229166 hasConcept C108583219 @default.
- W4200229166 hasConcept C118518473 @default.
- W4200229166 hasConcept C119857082 @default.
- W4200229166 hasConcept C138885662 @default.
- W4200229166 hasConcept C154945302 @default.
- W4200229166 hasConcept C166957645 @default.
- W4200229166 hasConcept C194051981 @default.
- W4200229166 hasConcept C205649164 @default.
- W4200229166 hasConcept C2776214188 @default.
- W4200229166 hasConcept C2778137410 @default.
- W4200229166 hasConcept C41008148 @default.
- W4200229166 hasConcept C41895202 @default.
- W4200229166 hasConceptScore W4200229166C108583219 @default.
- W4200229166 hasConceptScore W4200229166C118518473 @default.
- W4200229166 hasConceptScore W4200229166C119857082 @default.
- W4200229166 hasConceptScore W4200229166C138885662 @default.
- W4200229166 hasConceptScore W4200229166C154945302 @default.
- W4200229166 hasConceptScore W4200229166C166957645 @default.
- W4200229166 hasConceptScore W4200229166C194051981 @default.
- W4200229166 hasConceptScore W4200229166C205649164 @default.
- W4200229166 hasConceptScore W4200229166C2776214188 @default.
- W4200229166 hasConceptScore W4200229166C2778137410 @default.
- W4200229166 hasConceptScore W4200229166C41008148 @default.
- W4200229166 hasConceptScore W4200229166C41895202 @default.
- W4200229166 hasIssue "1" @default.
- W4200229166 hasLocation W42002291661 @default.
- W4200229166 hasLocation W42002291662 @default.
- W4200229166 hasOpenAccess W4200229166 @default.
- W4200229166 hasPrimaryLocation W42002291661 @default.
- W4200229166 hasRelatedWork W3014300295 @default.
- W4200229166 hasRelatedWork W3164822677 @default.
- W4200229166 hasRelatedWork W4223943233 @default.
- W4200229166 hasRelatedWork W4225161397 @default.
- W4200229166 hasRelatedWork W4250304930 @default.
- W4200229166 hasRelatedWork W4312200629 @default.
- W4200229166 hasRelatedWork W4360585206 @default.
- W4200229166 hasRelatedWork W4364306694 @default.
- W4200229166 hasRelatedWork W4380075502 @default.
- W4200229166 hasRelatedWork W4380086463 @default.
- W4200229166 hasVolume "6" @default.
- W4200229166 isParatext "false" @default.
- W4200229166 isRetracted "false" @default.
- W4200229166 workType "article" @default.