Precision Agriculture Tools

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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

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
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

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

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.

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.
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.
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

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

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

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

Deng, L.[Lei], Mao, Z.H.[Zhi-Hui], Li, X.J.[Xiao-Juan], Hu, Z.W.[Zhuo-Wei], Duan, F.Z.[Fu-Zhou], 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

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

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

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.
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

Zhao, W.[Wei], Yamada, W.[William], Li, T.X.[Tian-Xin], 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

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

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

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

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

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

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

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

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

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

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

Liu, J.[Jia], Xiang, J.[Jianjian], Jin, Y.J.[Yong-Jun], Liu, R.H.[Ren-Hua], Yan, J.[Jining], Wang, L.[Lizhe],
Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Lu, H.[Hao], Liu, L.[Liang], Li, Y.N.[Ya-Nan], Zhao, X.M.[Xiao-Ming], Wang, X.Q.[Xi-Qing], Cao, Z.G.[Zhi-Guo],
TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression,
GeoRS(60), 2022, pp. 1-15.
Image segmentation, Agriculture, Plants (biology), Annotations, Tools, Data visualization, Ear, Explainable counting, wheat ear BibRef

Teucher, M.[Mike], Thürkow, D.[Detlef], Alb, P.[Philipp], Conrad, C.[Christopher],
Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture: Progress towards Digital Agriculture,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Proctor, C.[Cameron], Pereira, C.[Cedelle], Jin, T.[Tian], Lim, G.[Gloria], He, Y.H.[Yu-Hong],
Linking the Spectra of Decomposing Litter to Ecosystem Processes: Tandem Close-Range Hyperspectral Imagery and Decomposition Metrics,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Radocaj, D.[Dorijan], Jurišic, M.[Mladen], Gašparovic, M.[Mateo],
The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

di Gennaro, S.F.[Salvatore Filippo], Toscano, P.[Piero], Gatti, M.[Matteo], Poni, S.[Stefano], Berton, A.[Andrea], Matese, A.[Alessandro],
Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Guan, Z.[Zhen], Abd-Elrahman, A.[Amr], Whitaker, V.[Vance], Agehara, S.[Shinsuke], Wilkinson, B.[Benjamin], Gastellu-Etchegorry, J.P.[Jean-Philippe], Dewitt, B.[Bon],
Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Wang, D.S.[Da-Shuai], Cao, W.[Wujing], Zhang, F.[Fan], Li, Z.L.[Zhuo-Lin], Xu, S.[Sheng], Wu, X.Y.[Xin-Yu],
A Review of Deep Learning in Multiscale Agricultural Sensing,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Alibabaei, K.[Khadijeh], Gaspar, P.D.[Pedro D.], Lima, T.M.[Tânia M.], Campos, R.M.[Rebeca M.], Girão, I.[Inês], Monteiro, J.[Jorge], Lopes, C.M.[Carlos M.],
A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202

Donati, C.[Cesare], Mammarella, M.[Martina], Comba, L.[Lorenzo], Biglia, A.[Alessandro], Gay, P.[Paolo], Dabbene, F.[Fabrizio],
3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Wang, Z.H.[Zhi-Hua], Zhao, Z.[Zhan], Yin, C.L.[Cheng-Long],
Fine Crop Classification Based on UAV Hyperspectral Images and Random Forest,
IJGI(11), No. 4, 2022, pp. xx-yy.
DOI Link 2205

Singh, A.P.[Abhaya Pal], Yerudkar, A.[Amol], Mariani, V.[Valerio], Iannelli, L.[Luigi], Glielmo, L.[Luigi],
A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Gilliot, J.M.[Jean-Marc], Hadjar, D.[Dalila], Michelin, J.[Joël],
Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206

Petsoulas, C.[Christos], Evangelou, E.[Eleftherios], Tsitouras, A.[Alexandros], Aschonitis, V.[Vassilis], Kargiotidou, A.[Anastasia], Khah, E.[Ebrahim], Pavli, O.I.[Ourania I.], Vlachostergios, D.N.[Dimitrios N.],
Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206

Bagha, H.[Hamid], Yavari, A.[Ali], Georgakopoulos, D.[Dimitrios],
IoT-based Plant Health Analysis using Optical Sensors in Precision Agriculture,
Reflectivity, Plants (biology), Sociology, Crops, Production, Autonomous aerial vehicles, Data models, IoT, Optical Data Analysis BibRef

Bai, C.H.[Chia-Hung], Prakosa, S.W.[Setya Widyawan], Hsieh, H.Y.[He-Yen], Leu, J.S.[Jenq-Shiou], Fang, W.H.[Wen-Hsien],
Progressive Contextual Excitation for Smart Farming Application,
Springer DOI 2112

Nuthalapati, S.V.[Sai Vidyaranya], Tunga, A.[Anirudh],
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications,
Measurement, Computational modeling, Plants (biology), Computer architecture, Performance gain, Transformers, Feature extraction BibRef

Mei, J.[Jie], Sun, K.Q.[Kai-Qiong], Xu, X.[Xin],
Combing Color Index and Region Growing with Simple Non-iterative Clustering for Plant Segmentation,
Image segmentation, Image color analysis, Plants (biology), Clustering methods, Crops, Agriculture, segmentation BibRef

Ni, J.Y.[Jian-Yuan], Zhu, Z.[Zanbo], Zhou, X.G.[Xin-Gen], Dou, F.[Fugen], Yang, Y.B.[Yu-Bin], Wilson, L.T.[Lloyd T.], Samonte, S.O.P.[Stanley Omar PB.], Wang, J.[Jing], Zhang, J.[Jing],
Ridge Detection and Perceptual Grouping Based Automatic Counting of Rice Seedlings Using UAV Images,
Image segmentation, Unmanned aerial vehicles, Skeleton, rice seedlings counting, unmanned aerial vehicle, perceptual grouping 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,
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,
Springer DOI 1909

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,
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

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,
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

Bachmann, F., Herbst, R., Gebbers, R., Hafner, V.V.,
Micro UAV Based Georeferenced Orthophoto Generation in VIS + NIR for Precision Agriculture,
HTML Version. 1311

Guo, T., Kujirai, T., Watanabe, T.,
Mapping Crop Status From An Unmanned Aerial Vehicle For Precision Agriculture Applications,
DOI Link 1209

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,

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Food Descriptions, Dishes, Recipe Generation .

Last update:Aug 11, 2022 at 11:48:53