Three-dimensional location methods for the vision system of strawberry-harvesting robots: development and comparison.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 764, doi. 10.1007/s11119-022-09974-4
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- Publication type:
- Article
Works matching IS 13852256 AND DT 2023 AND VI 24 AND IP 2
Results: 17
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Novel attitude control of Korean cabbage harvester using backstepping control.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 744, doi. 10.1007/s11119-022-09973-5
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- Article
3
Clustered tomato detection and picking point location using machine learning-aided image analysis for automatic robotic harvesting.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 727, doi. 10.1007/s11119-022-09972-6
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- Article
4
Improved estimation of herbaceous crop aboveground biomass using UAV-derived crop height combined with vegetation indices.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 587, doi. 10.1007/s11119-022-09960-w
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- Article
5
Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 705, doi. 10.1007/s11119-022-09970-8
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- Article
6
Spatial variability of soil properties, nitrogen balance and nitrate leaching using digital methods on heterogeneous arable fields in southern Germany.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 647, doi. 10.1007/s11119-022-09967-3
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- Publication type:
- Article
7
iDVS: interactive 2D and 3D visualizations of proximal sensor data for rapid characterization of soil profiles.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 627, doi. 10.1007/s11119-022-09962-8
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- Article
8
Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?
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- Precision Agriculture, 2023, v. 24, n. 2, p. 607, doi. 10.1007/s11119-022-09961-9
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- Article
9
Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on-farm precision experimentation.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 677, doi. 10.1007/s11119-022-09968-2
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- Article
10
Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 560, doi. 10.1007/s11119-022-09959-3
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- Article
11
Assessing the precision irrigation potential for increasing crop yield and water savings through simulation.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 533, doi. 10.1007/s11119-022-09958-4
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- Article
12
On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 511, doi. 10.1007/s11119-022-09955-7
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- Article
13
Early/late fusion structures with optimized feature selection for weed detection using visible and thermal images of paddy fields.
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- Precision Agriculture, 2023, v. 24, n. 2, p. 482, doi. 10.1007/s11119-022-09954-8
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- Publication type:
- Article
14
Multi-species weed density assessment based on semantic segmentation neural network.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 458, doi. 10.1007/s11119-022-09953-9
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- Article
15
Automatic monitoring of flying vegetable insect pests using an RGB camera and YOLO-SIP detector.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 436, doi. 10.1007/s11119-022-09952-w
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- Publication type:
- Article
16
Identification of pathogens in corn using near-infrared UAV imagery and deep learning.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 783, doi. 10.1007/s11119-022-09951-x
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- Article
17
Early yield prediction in different grapevine varieties using computer vision and machine learning.
- Published in:
- Precision Agriculture, 2023, v. 24, n. 2, p. 407, doi. 10.1007/s11119-022-09950-y
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- Publication type:
- Article