19.7.3.7.2 Precision Agriculture Tools

Chapter Contents (Back)
Precision Agriculture. Agriculture Tools. Various tools.

Pajares, G., Tellaeche, A., Burgosartizzu, X.P., Ribeiro, A.,
Design of a computer vision system for a differential spraying operation in precision agriculture using hebbian learning,
IET-CV(1), No. 3-4, December 2007, pp. 93-99.
DOI Link 0905
BibRef

Burgos-Artizzu, X.P.[Xavier P.], Ribeiro, A.[Angela], Tellaeche, A.[Alberto], Pajares, G.[Gonzalo], Fernandez-Quintanilla, C.[Cesar],
Analysis of natural images processing for the extraction of agricultural elements,
IVC(28), No. 1, Januray 2010, pp. 138-149.
Elsevier DOI 1001
Computer vision; Precision agriculture; Weed detection; Parameter setting; Genetic algorithms BibRef

Honkavaara, E.[Eija], Saari, H.[Heikki], Kaivosoja, J.[Jere], Pölönen, I.[Ilkka], Hakala, T.[Teemu], Litkey, P.[Paula], Mäkynen, J.[Jussi], Pesonen, L.[Liisa],
Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture,
RS(5), No. 10, 2013, pp. 5006-5039.
DOI Link 1311
BibRef

Honkavaara, E., Kaivosoja, J., Mäkynen, J., Pellikka, I., Pesonen, L., Saari, H., Salo, H., Hakala, T., Marklelin, L., Rosnell, T.,
Hyperspectral Reflectance Signatures and Point Clouds for Precision Agriculture by light Weight UAV Imaging System,
AnnalsPRS(I-7), No. 2012, pp. 353-358.
HTML Version. 1209
BibRef

Yang, C.H.[Cheng-Hai], Everitt, J.H., Du, Q.[Qian], Luo, B.[Bin], Chanussot, J.,
Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture,
PIEEE(100), No. 3, March 2013, pp. 582-592.
IEEE DOI 1303
BibRef

Kang, J.[Jian], Fernandez-Beltran, R.[Ruben], Hong, D.F.[Dan-Feng], Chanussot, J.[Jocelyn], Plaza, A.[Antonio],
Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval,
GeoRS(59), No. 5, May 2021, pp. 4355-4369.
IEEE DOI 2104
Semantics, Feature extraction, Deep learning, Extraterrestrial measurements, Training, Remote sensing, remote sensing (RS) BibRef

Hong, D.F.[Dan-Feng], Gao, L.R.[Lian-Ru], Yokoya, N.[Naoto], Yao, J.[Jing], Chanussot, J.[Jocelyn], Du, Q.[Qian], Zhang, B.[Bing],
More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification,
GeoRS(59), No. 5, May 2021, pp. 4340-4354.
IEEE DOI 2104
Feature extraction, Laser radar, Synthetic aperture radar, Machine learning, Task analysis, Remote sensing, Earth, synthetic aperture radar (SAR) BibRef

Lyle, G., Lewis, M., Ostendorf, B.,
Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale,
RS(5), No. 4, April 2013, pp. 1549-1567.
DOI Link 1305
BibRef

Koenig, K.[Kristina], Höfle, B.[Bernhard], Hämmerle, M.[Martin], Jarmer, T.[Thomas], Siegmann, B.[Bastian], Lilienthal, H.[Holger],
Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture,
PandRS(104), No. 1, 2015, pp. 112-125.
Elsevier DOI 1505
Terrestrial laser scanning BibRef

Candiago, S.[Sebastian], Remondino, F.[Fabio], de Giglio, M.[Michaela], Dubbini, M.[Marco], Gattelli, M.[Mario],
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images,
RS(7), No. 4, 2015, pp. 4026-4047.
DOI Link 1505
BibRef

Ivanov, S.[Stepan], Bhargava, K.[Kriti], Donnelly, W.[William],
Precision Farming: Sensor Analytics,
IEEE_Int_Sys(30), No. 4, July 2015, pp. 76-80.
IEEE DOI 1506
Data integration BibRef

Houborg, R.[Rasmus], McCabe, M.F.[Matthew F.],
High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture,
RS(8), No. 9, 2016, pp. 768.
DOI Link 1610
BibRef

Sa, I.[Inkyu], Popovic, M.[Marija], Khanna, R.[Raghav], Chen, Z.[Zetao], Lottes, P.[Philipp], Liebisch, F.[Frank], Nieto, J.[Juan], Stachniss, C.[Cyrill], Walter, A.[Achim], Siegwart, R.[Roland],
WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Deng, L.[Lei], Mao, Z.H.[Zhi-Hui], Li, X.J.[Xiao-Juan], Hu, Z.[Zhuowei], Duan, F.[Fuzhou], Yan, Y.[Yanan],
UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras,
PandRS(146), 2018, pp. 124-136.
Elsevier DOI 1812
Multispectral camera, Unmanned Aerial Vehicle (UAV), Remote sensing, Vegetation index, SPAD value BibRef

Rodrigues, F.A.[Francelino A.], Blasch, G.[Gerald], Defourny, P.[Pierre], Ortiz-Monasterio, J.I.[J. Ivan], Schulthess, U.[Urs], Zarco-Tejada, P.J.[Pablo J.], Taylor, J.A.[James A.], Gérard, B.[Bruno],
Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Aragon, B.[Bruno], Houborg, R.[Rasmus], Tu, K.[Kevin], Fisher, J.B.[Joshua B.], McCabe, M.[Matthew],
CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Prey, L.[Lukas], Schmidhalter, U.[Urs],
Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat,
PandRS(149), 2019, pp. 176-187.
Elsevier DOI 1903
Digital agriculture, Phenomics, Spectral resampling, Satellite vegetation indices, Precision farming, Yield prediction BibRef

Messina, G.[Gaetano], Modica, G.[Giuseppe],
Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Osco, L.P.[Lucas Prado], dos Santos de Arruda, M.[Mauro], Gonçalves, D.N.[Diogo Nunes], Dias, A.[Alexandre], Batistoti, J.[Juliana], de Souza, M.[Mauricio], Gomes, F.D.G.[Felipe David Georges], Ramos, A.P.M.[Ana Paula Marques], de Castro Jorge, L.A.[Lúcio André], Liesenberg, V.[Veraldo], Li, J.[Jonathan], Ma, L.F.[Ling-Fei], Marcato, J.[José], Gonçalves, W.N.[Wesley Nunes],
A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery,
PandRS(174), 2021, pp. 1-17.
Elsevier DOI 2103
Deep learning, UAV imagery, Object detection, Remote sensing, Precision agriculture BibRef

Solano-Correa, Y.T., Bovolo, F., Bruzzone, L., Fernández-Prieto, D.,
A Method for the Analysis of Small Crop Fields in Sentinel-2 Dense Time Series,
GeoRS(58), No. 3, March 2020, pp. 2150-2164.
IEEE DOI 2003
Nonparametric regression, precision agriculture, satellite image time series (SITS), Sentinel-2, vegetation phenology BibRef

Sishodia, R.P.[Rajendra P.], Ray, R.L.[Ram L.], Singh, S.K.[Sudhir K.],
Applications of Remote Sensing in Precision Agriculture: A Review,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Zhao, W.[Wei], Yamada, W.[William], Li, T.[Tianxin], Digman, M.[Matthew], Runge, T.[Troy],
Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning: A Case Study of Bale Detection,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Belcore, E.[Elena], Angeli, S.[Stefano], Colucci, E.[Elisabetta], Musci, M.A.[Maria Angela], Aicardi, I.[Irene],
Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Suleymanov, A.[Azamat], Abakumov, E.[Evgeny], Suleymanov, R.[Ruslan], Gabbasova, I.[Ilyusya], Komissarov, M.[Mikhail],
The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Delavarpour, N.[Nadia], Koparan, C.[Cengiz], Nowatzki, J.[John], Bajwa, S.[Sreekala], Sun, X.[Xin],
A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Wan, S.[Shiuan], Yeh, M.L.[Mei-Ling], Ma, H.L.[Hong-Lin],
An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Junos, M.H.[Mohamad Haniff], Khairuddin, A.S.M.[Anis Salwa Mohd], Thannirmalai, S.[Subbiah], Dahari, M.[Mahidzal],
An optimized YOLO-based object detection model for crop harvesting system,
IET-IPR(15), No. 9, 2021, pp. 2112-2125.
DOI Link 2106
BibRef

Vayssade, J.A.[Jehan-Antoine], Paoli, J.N.[Jean-Noël], Gée, C.[Christelle], Jones, G.[Gawain],
DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Ullo, S.L.[Silvia Liberata], Sinha, G.R.,
Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Kim, B.[Byungchul], Jang, J.[Jaesu], Kim, S.[Sangjo], Hwang, S.[Seonmin], Shin, M.[Moonsun],
Design of an ICT convergence farm machinery for an automatic agricultural planter,
IJCVR(11), No. 4, 2021, pp. 448-460.
DOI Link 2108
BibRef

Xu, R.[Rui], Li, C.[Changying], Bernardes, S.[Sergio],
Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Huang, X.[Xin], Dong, X.Y.[Xiao-Ya], Ma, J.[Jing], Liu, K.[Kuan], Ahmed, S.[Shibbir], Lin, J.[Jinlong], Qiu, B.[Baijing],
The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef


Akiva, P.[Peri], Planche, B.[Benjamin], Roy, A.[Aditi], Dana, K.[Kristin], Oudemans, P.[Peter], Mars, M.[Michael],
AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk,
WACV21(2492-2501)
IEEE DOI 2106
Economics, Temperature distribution, Irrigation, Image segmentation, Object segmentation, Data collection BibRef

Razaak, M.[Manzoor], Kerdegari, H.[Hamideh], Davies, E.[Eleanor], Abozariba, R.[Raouf], Broadbent, M.[Matthew], Mason, K.[Katy], Argyriou, V.[Vasileios], Remagnino, P.[Paolo],
An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies,
CAIPWS19(109-119).
Springer DOI 1909
BibRef

Rezende Silva, G.[Gustavo], Cunha Escarpinati, M.[Mauricio], Duarte Abdala, D.[Daniel], Rezende Souza, I.[Iuri],
Definition of Management Zones Through Image Processing for Precision Agriculture,
WVC17(150-154)
IEEE DOI 1804
agriculture, autonomous aerial vehicles, crops, farming, remotely operated vehicles, robot vision, vegetation mapping, NDVI, k-means clustering BibRef

Lukas, V., Novák, J., Neudert, L., Svobodova, I., Rodriguez-Moreno, F., Edrees, M., Kren, J.,
The Combination Of UAV Survey And Landsat Imagery For Monitoring Of Crop Vigor In Precision Agriculture,
ISPRS16(B8: 953-957).
DOI Link 1610
BibRef

Abuleil, A.M.[Ammar M.], Taylor, G.W.[Graham W.], Moussa, M.[Medhat],
An Integrated System for Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles: A Case Study in Precision Agriculture,
CRV15(277-284)
IEEE DOI 1507
Accuracy BibRef

Erena, M., Montesinos, S., Portillo, D., Alvarez, J., Marin, C., Fernandez, L., Henarejos, J.M., Ruiz, L.A.,
Configuration And Specifications Of An Unmanned Aerial Vehicle For Precision Agriculture,
ISPRS16(B1: 809-816).
DOI Link 1610
BibRef

Bachmann, F., Herbst, R., Gebbers, R., Hafner, V.V.,
Micro UAV Based Georeferenced Orthophoto Generation in VIS + NIR for Precision Agriculture,
UAV-g13(11-16).
HTML Version. 1311
BibRef

Guo, T., Kujirai, T., Watanabe, T.,
Mapping Crop Status From An Unmanned Aerial Vehicle For Precision Agriculture Applications,
ISPRS12(XXXIX-B1:485-490).
DOI Link 1209
BibRef

Meng, X.L.[Xiao-Lin], Dodson, A., Zhang, J.X.[Ji-Xian], Cai, Y.H.[Yan-Hui], Liu, C.[Chun], Geary, K.,
Geospatial Data Fusion for Precision Agriculture,
ISIDF11(1-4).
IEEE DOI 1111
BibRef

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Last update:Sep 12, 2021 at 22:38:33