RETRACTED: A deep learning approach based on graphs to detect plantation lines
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Post-publication record
- Nature
- Retraction
- Reason
- Date of Article and/or Notice Unknown;Rogue Editor;
- Date
- 11/14/2024 0:00
- Flagged by OpenAlex?
- Yes
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Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.228 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.
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The record
- Venue
- Heliyon
- Topic
- Remote Sensing and LiDAR Applications
- Field
- Environmental Science
- Canadian institutions
- University of Waterloo
- Funders
- Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do SulMinistero degli Affari Esteri e della Cooperazione InternazionaleConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidade Federal de Mato Grosso do SulCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Keywords
- Artificial intelligenceRGB color modelComputer scienceEnhanced Data Rates for GSM EvolutionFeature (linguistics)GeneralizationLine (geometry)Displacement (psychology)PixelPattern recognition (psychology)Deep learningGraphAerial imageComputer visionImage (mathematics)Machine learningMathematicsTheoretical computer scienceGeometry
- Has abstract in OpenAlex
- yes