FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3092458520> ?p ?o ?g. }

• W3092458520 abstract "Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively." @default.
• W3092458520 created "2020-10-15" @default.
• W3092458520 creator A5027774051 @default.
• W3092458520 creator A5044525643 @default.
• W3092458520 creator A5049366215 @default.
• W3092458520 creator A5050950821 @default.
• W3092458520 creator A5056138775 @default.
• W3092458520 creator A5060576851 @default.
• W3092458520 creator A5068227773 @default.
• W3092458520 date "2020-01-01" @default.
• W3092458520 modified "2023-10-12" @default.
• W3092458520 title "Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images" @default.
• W3092458520 cites W1677182931 @default.
• W3092458520 cites W1964615151 @default.
• W3092458520 cites W1980148204 @default.
• W3092458520 cites W1984652067 @default.
• W3092458520 cites W1989863789 @default.
• W3092458520 cites W1992159527 @default.
• W3092458520 cites W1998918746 @default.
• W3092458520 cites W2013577270 @default.
• W3092458520 cites W2017121615 @default.
• W3092458520 cites W2028551675 @default.
• W3092458520 cites W2042846957 @default.
• W3092458520 cites W2085635066 @default.
• W3092458520 cites W2096151163 @default.
• W3092458520 cites W2097117768 @default.
• W3092458520 cites W2098977695 @default.
• W3092458520 cites W2108692874 @default.
• W3092458520 cites W2114456168 @default.
• W3092458520 cites W2118246710 @default.
• W3092458520 cites W2119128301 @default.
• W3092458520 cites W2133059825 @default.
• W3092458520 cites W2141417970 @default.
• W3092458520 cites W2147800946 @default.
• W3092458520 cites W2158511186 @default.
• W3092458520 cites W2171711114 @default.
• W3092458520 cites W2183182206 @default.
• W3092458520 cites W2259717889 @default.
• W3092458520 cites W2514863413 @default.
• W3092458520 cites W2533591126 @default.
• W3092458520 cites W2570343428 @default.
• W3092458520 cites W2603364874 @default.
• W3092458520 cites W2604086375 @default.
• W3092458520 cites W2612420228 @default.
• W3092458520 cites W2768265868 @default.
• W3092458520 cites W2770111316 @default.
• W3092458520 cites W2807400690 @default.
• W3092458520 cites W2808376087 @default.
• W3092458520 cites W2869418531 @default.
• W3092458520 cites W2897239220 @default.
• W3092458520 cites W2897981848 @default.
• W3092458520 cites W2909259645 @default.
• W3092458520 cites W2947521956 @default.
• W3092458520 cites W3034231071 @default.
• W3092458520 cites W3106098994 @default.
• W3092458520 doi "https://doi.org/10.34133/2020/6323965" @default.
• W3092458520 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7706348" @default.
• W3092458520 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33313561" @default.
• W3092458520 hasPublicationYear "2020" @default.
• W3092458520 type Work @default.
• W3092458520 sameAs 3092458520 @default.
• W3092458520 citedByCount "8" @default.
• W3092458520 countsByYear W30924585202021 @default.
• W3092458520 countsByYear W30924585202022 @default.
• W3092458520 countsByYear W30924585202023 @default.
• W3092458520 crossrefType "journal-article" @default.
• W3092458520 hasAuthorship W3092458520A5027774051 @default.
• W3092458520 hasAuthorship W3092458520A5044525643 @default.
• W3092458520 hasAuthorship W3092458520A5049366215 @default.
• W3092458520 hasAuthorship W3092458520A5050950821 @default.
• W3092458520 hasAuthorship W3092458520A5056138775 @default.
• W3092458520 hasAuthorship W3092458520A5060576851 @default.
• W3092458520 hasAuthorship W3092458520A5068227773 @default.
• W3092458520 hasBestOaLocation W30924585201 @default.
• W3092458520 hasConcept C116834253 @default.
• W3092458520 hasConcept C12267149 @default.
• W3092458520 hasConcept C153180895 @default.
• W3092458520 hasConcept C154945302 @default.
• W3092458520 hasConcept C31972630 @default.
• W3092458520 hasConcept C41008148 @default.
• W3092458520 hasConcept C59822182 @default.
• W3092458520 hasConcept C81363708 @default.
• W3092458520 hasConcept C86803240 @default.
• W3092458520 hasConceptScore W3092458520C116834253 @default.
• W3092458520 hasConceptScore W3092458520C12267149 @default.
• W3092458520 hasConceptScore W3092458520C153180895 @default.
• W3092458520 hasConceptScore W3092458520C154945302 @default.
• W3092458520 hasConceptScore W3092458520C31972630 @default.
• W3092458520 hasConceptScore W3092458520C41008148 @default.
• W3092458520 hasConceptScore W3092458520C59822182 @default.
• W3092458520 hasConceptScore W3092458520C81363708 @default.
• W3092458520 hasConceptScore W3092458520C86803240 @default.
• W3092458520 hasFunder F4320321001 @default.
• W3092458520 hasLocation W30924585201 @default.
• W3092458520 hasLocation W30924585202 @default.
• W3092458520 hasLocation W30924585203 @default.
• W3092458520 hasOpenAccess W3092458520 @default.
• W3092458520 hasPrimaryLocation W30924585201 @default.
• W3092458520 hasRelatedWork W2041399278 @default.
• W3092458520 hasRelatedWork W2099369243 @default.

• next