FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313492074> ?p ?o ?g. }

• W4313492074 endingPage "130" @default.
• W4313492074 startingPage "130" @default.
• W4313492074 abstract "In an analysis of the penetration resistance and tillage depth of post-tillage soil, four surface-layer discrimination methods, specifically, three machine learning algorithms—Kmeans, DBSCAN, and GMM—and a curve-fitting method, were used to analyze data collected from the cultivated and uncultivated layers. Among them, the three machine learning algorithms found the boundary between the tilled and untilled layers by analyzing which data points belonged to which layer to determine the depth of the soil in the tilled layer. The curve-fitting method interpreted the intersection among data from the fitted curves of the ploughed layer and the un-ploughed layer as the tillage depth. The three machine learning algorithms were used to process a standard data set for model evaluation. DBSCAN’s discrimination accuracy of this data set reached 0.9890 and its F1 score reached 0.9934, which were superior to those of the other two algorithms. Under standard experimental conditions, the ability of DBSCAN clustering to determine the soil depth was the best among the four discrimination methods, and the discrimination accuracy reached 90.63% when the error was 15 mm. During field-test verification, the discriminative effect of DBSCAN clustering was still the best among the four methods. However, the soil blocks encountered in the field test affected the test data, resulting in large errors in the processing results. Therefore, the combined RANSCA robust regression and DBSCAN clustering algorithm, which can eliminate interference from soil blocks in the cultivated layer and can solve the problem of large depth errors caused by soil blocks in the field, was used to process the data. After testing, when the RANSCA and DBSCAN combined method was used to process all samples in the field and the error was less than 20mm, the accuracy rate reached 82.69%. This combined method improves the applicability of discrimination methods and provides a new method of determining soil depth." @default.
• W4313492074 created "2023-01-06" @default.
• W4313492074 creator A5014243683 @default.
• W4313492074 creator A5034994424 @default.
• W4313492074 creator A5075168365 @default.
• W4313492074 creator A5076277725 @default.
• W4313492074 creator A5080669118 @default.
• W4313492074 creator A5091318238 @default.
• W4313492074 date "2023-01-04" @default.
• W4313492074 modified "2023-09-29" @default.
• W4313492074 title "Tillage-Depth Verification Based on Machine Learning Algorithms" @default.
• W4313492074 cites W1976383685 @default.
• W4313492074 cites W2008909497 @default.
• W4313492074 cites W2039752587 @default.
• W4313492074 cites W2090839416 @default.
• W4313492074 cites W2137799805 @default.
• W4313492074 cites W2140405352 @default.
• W4313492074 cites W2147147599 @default.
• W4313492074 cites W2154137957 @default.
• W4313492074 cites W2160642098 @default.
• W4313492074 cites W2297788108 @default.
• W4313492074 cites W2498094064 @default.
• W4313492074 cites W2528571000 @default.
• W4313492074 cites W2607168025 @default.
• W4313492074 cites W2740924709 @default.
• W4313492074 cites W2785431375 @default.
• W4313492074 cites W2790117078 @default.
• W4313492074 cites W2903784017 @default.
• W4313492074 cites W2982437619 @default.
• W4313492074 cites W2988039629 @default.
• W4313492074 cites W2997475058 @default.
• W4313492074 cites W3004630939 @default.
• W4313492074 cites W3014889141 @default.
• W4313492074 cites W3019913914 @default.
• W4313492074 cites W3048630347 @default.
• W4313492074 cites W3048804154 @default.
• W4313492074 cites W3082776374 @default.
• W4313492074 cites W3113018525 @default.
• W4313492074 cites W3123200913 @default.
• W4313492074 doi "https://doi.org/10.3390/agriculture13010130" @default.
• W4313492074 hasPublicationYear "2023" @default.
• W4313492074 type Work @default.
• W4313492074 citedByCount "1" @default.
• W4313492074 countsByYear W43134920742023 @default.
• W4313492074 crossrefType "journal-article" @default.
• W4313492074 hasAuthorship W4313492074A5014243683 @default.
• W4313492074 hasAuthorship W4313492074A5034994424 @default.
• W4313492074 hasAuthorship W4313492074A5075168365 @default.
• W4313492074 hasAuthorship W4313492074A5076277725 @default.
• W4313492074 hasAuthorship W4313492074A5080669118 @default.
• W4313492074 hasAuthorship W4313492074A5091318238 @default.
• W4313492074 hasBestOaLocation W43134920741 @default.
• W4313492074 hasConcept C104317684 @default.
• W4313492074 hasConcept C11413529 @default.
• W4313492074 hasConcept C119857082 @default.
• W4313492074 hasConcept C153180895 @default.
• W4313492074 hasConcept C154945302 @default.
• W4313492074 hasConcept C16397148 @default.
• W4313492074 hasConcept C185592680 @default.
• W4313492074 hasConcept C18903297 @default.
• W4313492074 hasConcept C33704608 @default.
• W4313492074 hasConcept C33923547 @default.
• W4313492074 hasConcept C41008148 @default.
• W4313492074 hasConcept C46576248 @default.
• W4313492074 hasConcept C55493867 @default.
• W4313492074 hasConcept C58489278 @default.
• W4313492074 hasConcept C63479239 @default.
• W4313492074 hasConcept C73555534 @default.
• W4313492074 hasConcept C86803240 @default.
• W4313492074 hasConcept C94641424 @default.
• W4313492074 hasConceptScore W4313492074C104317684 @default.
• W4313492074 hasConceptScore W4313492074C11413529 @default.
• W4313492074 hasConceptScore W4313492074C119857082 @default.
• W4313492074 hasConceptScore W4313492074C153180895 @default.
• W4313492074 hasConceptScore W4313492074C154945302 @default.
• W4313492074 hasConceptScore W4313492074C16397148 @default.
• W4313492074 hasConceptScore W4313492074C185592680 @default.
• W4313492074 hasConceptScore W4313492074C18903297 @default.
• W4313492074 hasConceptScore W4313492074C33704608 @default.
• W4313492074 hasConceptScore W4313492074C33923547 @default.
• W4313492074 hasConceptScore W4313492074C41008148 @default.
• W4313492074 hasConceptScore W4313492074C46576248 @default.
• W4313492074 hasConceptScore W4313492074C55493867 @default.
• W4313492074 hasConceptScore W4313492074C58489278 @default.
• W4313492074 hasConceptScore W4313492074C63479239 @default.
• W4313492074 hasConceptScore W4313492074C73555534 @default.
• W4313492074 hasConceptScore W4313492074C86803240 @default.
• W4313492074 hasConceptScore W4313492074C94641424 @default.
• W4313492074 hasIssue "1" @default.
• W4313492074 hasLocation W43134920741 @default.
• W4313492074 hasOpenAccess W4313492074 @default.
• W4313492074 hasPrimaryLocation W43134920741 @default.
• W4313492074 hasRelatedWork W1968194344 @default.
• W4313492074 hasRelatedWork W2002078939 @default.
• W4313492074 hasRelatedWork W2350965001 @default.
• W4313492074 hasRelatedWork W2351195899 @default.
• W4313492074 hasRelatedWork W2354555059 @default.
• W4313492074 hasRelatedWork W2357120402 @default.

• next