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

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.
DOI Link 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.

Sicre, C.M.[Claire Marais], Baup, F.[Frédéric], Fieuzal, R.[Rémy],
Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images,
PandRS(94), No. 1, 2014, pp. 127-142.
Elsevier DOI 1407
Crop monitoring BibRef

Vidovic, I.[Ivan], Cupec, R.[Robert], Hocenski, Ž.[Željko],
Crop row detection by global energy minimization,
PR(55), No. 1, 2016, pp. 68-86.
Elsevier DOI 1604
Agricultural automation BibRef

Ronchetti, G.[Giulia], Mayer, A.[Alice], Facchi, A.[Arianna], Ortuani, B.[Bianca], Sona, G.[Giovanna],
Crop Row Detection through UAV Surveys to Optimize On-Farm Irrigation Management,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

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

Chen, P.F.[Peng-Fei], Ma, X.[Xiao], Wang, F.Y.[Fang-Yong], Li, J.[Jing],
A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Yu, Y.[Yue], Bao, Y.[Yidan], Wang, J.C.[Ji-Chun], Chu, H.J.[Hang-Jian], Zhao, N.[Nan], He, Y.[Yong], Liu, Y.F.[Yu-Fei],
Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103

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.C.[Byung-Chul], 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.Y.[Chang-Ying], 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.L.[Jin-Long], 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.J.[Jian-Jian], 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

Assunção, E.[Eduardo], Gaspar, P.D.[Pedro D.], Mesquita, R.[Ricardo], Simões, M.P.[Maria P.], Alibabaei, K.[Khadijeh], Veiros, A.[André], Proença, H.[Hugo],
Real-Time Weed Control Application Using a Jetson Nano Edge Device and a Spray Mechanism,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209

Septiarini, F.[Fradina], Dewi, T.[Tresna], Rusdianasari,
Design of a solar-powered mobile manipulator using fuzzy logic controller of agriculture application,
IJCVR(12), No. 5, 2022, pp. 506-531.
DOI Link 2209

Lambertini, A.[Alessandro], Mandanici, E.[Emanuele], Tini, M.A.[Maria Alessandra], Vittuari, L.[Luca],
Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Dericquebourg, E.[Eric], Hafiane, A.[Adel], Canals, R.[Raphael],
Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211

Morris, J.R.[Jesse R.], Petersen, S.L.[Steven L.], Madsen, M.D.[Matthew D.], McMillan, B.R.[Brock R.], Eggett, D.L.[Dennis L.], Lawrence, C.R.[C. Russell],
Monitoring Seedling Emergence, Growth, and Survival Using Repeat High-Resolution Imagery,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212

Lombardi, F.[Federico], Ortuani, B.[Bianca], Facchi, A.[Arianna], Lualdi, M.[Maurizio],
Assessing the Perspectives of Ground Penetrating Radar for Precision Farming,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212

Tauber, M.[Markus], Gollan, B.[Benedikt], Schmittner, C.[Christoph], Knopf, P.[Philipp],
Passive Precision Farming Reshapes the Agricultural Sector,
Computer(56), No. 1, January 2023, pp. 120-124.
Costs, Sociology, Ecosystems, Cyber-physical systems, Product design, Safety, Quality assessment BibRef

Li, N.[Na], Zhang, Y.[Yunlin], Shi, K.[Kun], Zhang, Y.[Yibo], Sun, X.[Xiao], Wang, W.J.[Wei-Jia], Qian, H.M.[Hai-Ming], Yang, H.[Huayin], Niu, Y.K.[Yong-Kang],
Real-Time and Continuous Tracking of Total Phosphorus Using a Ground-Based Hyperspectral Proximal Sensing System,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301

Ma, B.D.[Bao-Dong], Liu, Q.[Quan], Jiang, Z.W.[Zi-Wei], Che, D.[Defu], Qiu, K.[Kehan], Shang, X.X.[Xiang-Xiang],
Energy-Efficient 3D Path Planning for Complex Field Scenes Using the Digital Model with Landcover and Terrain,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303

Rodriguez-Vazquez, J.[Javier], Fernandez-Cortizas, M.[Miguel], Perez-Saura, D.[David], Molina, M.[Martin], Campoy, P.[Pascual],
Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images,
RS(15), No. 6, 2023, pp. 1700.
DOI Link 2304

Kang, M.Z.[Meng-Zhen], Wang, X.J.[Xiu-Juan], Wang, H.Y.[Hao-Yu], Hua, J.[Jing], de Reffye, P.[Philippe], Wang, F.Y.[Fei-Yue],
The Development of AgriVerse: Past, Present, and Future,
SMCS(53), No. 6, June 2023, pp. 3718-3727.
Computational modeling, Biological systems, Biomass, Biological system modeling, Production, Plants (biology), plant model BibRef

Li, F.[Fei], Cen, C.J.[Chao-Jun], Zhang, X.X.[Xin-Xin], Li, Z.B.[Zhen-Bo],
Dual-attention global domain adaptation for mariculture image enhancement,
IET-IPR(17), No. 6, 2023, pp. 1668-1680.
DOI Link 2305
computer vision, domain adaptation, image enhancement BibRef

Zhang, T.[Ting], Jiao, X.H.[Xiao-Hong], Zhang, Y.H.[Ya-Hui],
Internal-Model-Principle-Based Fast Adaptive Iterative Learning Trajectory Tracking Control for Autonomous Farming Vehicle Under Alignment Condition and Input Constraint,
SMCS(53), No. 6, June 2023, pp. 3588-3599.
Farming, Process control, Trajectory, Trajectory tracking, Vehicle dynamics, Control systems, Convergence, internal model principle (IMP) BibRef

Feng, Y.X.[Ying-Xiang], Chen, W.[Wei], Ma, Y.[Yiru], Zhang, Z.[Ze], Gao, P.[Pan], Lv, X.[Xin],
Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Cordier, M.[Mathis], Torres, C.[Cindy], Rasti, P.[Pejman], Rousseau, D.[David],
On the Use of Circadian Cycles to Monitor Individual Young Plants,
RS(15), No. 11, 2023, pp. 2704.
DOI Link 2306

Saad, W.H.B.M.[Wira Hidayat Bin Mohd], Razak, M.H.B.A.[Muhammad Haziq Bin Abd], Zainudin, M.N.S.[Muhammad Noorazlan Shah], Radzi, S.B.A.[Syafeeza Binti Ahmad], Razak, M.S.J.B.A.[Muhammad Shah Jehan Bin Abd],
RGB-depth map formation from cili-padi plant imaging using stereo vision camera,
IJCVR(13), No. 4, 2023, pp. 343-358.
DOI Link 2307

Takekawa, J.Y.[John Y.], Hagani, J.S.[Jason S.], Edmunds, T.J.[Timothy J.], Collins, J.M.[Jesirae M.], Chappell, S.C.[Steven C.], Reynolds, W.H.[William H.],
The Sky Is Not the Limit: Use of a Spray Drone for the Precise Application of Herbicide and Control of an Invasive Plant in Managed Wetlands,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308

Kouadio, L.[Louis], El Jarroudi, M.[Moussa], Belabess, Z.[Zineb], Laasli, S.E.[Salah-Eddine], Roni, M.Z.K.[Md Zohurul Kadir], Amine, I.D.I.[Ibn Dahou Idrissi], Mokhtari, N.[Nourreddine], Mokrini, F.[Fouad], Junk, J.[Jürgen], Lahlali, R.[Rachid],
A Review on UAV-Based Applications for Plant Disease Detection and Monitoring,
RS(15), No. 17, 2023, pp. 4273.
DOI Link 2310

Sharma, P.[Purushottam], Kumar, M.[Manoj], Sharma, R.[Richa], Bhushan, S.[Shashi], Gupta, S.I.[Sun-Il],
An automated system to detect crop diseases using deep learning,
IJCVR(13), No. 5, 2023, pp. 556-571.
DOI Link 2310

Imangholiloo, M.[Mohammad], Luoma, V.[Ville], Holopainen, M.[Markus], Vastaranta, M.[Mikko], Mäkeläinen, A.[Antti], Koivumäki, N.[Niko], Honkavaara, E.[Eija], Khoramshahi, E.[Ehsan],
A New Approach for Feeding Multispectral Imagery into Convolutional Neural Networks Improved Classification of Seedlings,
RS(15), No. 21, 2023, pp. 5233.
DOI Link 2311

Cho, H.M.[Hyun-Min], Park, J.W.[Jin-Woo], Lee, J.S.[Jung-Soo], Han, S.K.[Sang-Kyun],
Assessment of the GNSS-RTK for Application in Precision Forest Operations,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401

Sanaeifar, A.[Alireza], Yang, C.[Ce], Min, A.[An], Jones, C.R.[Colin R.], Michaels, T.E.[Thomas E.], Krueger, Q.J.[Quinton J.], Barnes, R.[Robert], Velte, T.J.[Toby J.],
Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401

Zuckerman, N.[Noa], Cohen, Y.[Yafit], Alchanatis, V.[Victor], Lensky, I.M.[Itamar M.],
Toward Precision Agriculture in Outdoor Vertical Greenery Systems (VGS): Monitoring and Early Detection of Stress Events,
RS(16), No. 2, 2024, pp. 302.
DOI Link 2402

Luo, J.[Jiayun], Li, B.Y.[Bo-Yang], Leung, C.[Cyril],
A Survey of Computer Vision Technologies in Urban and Controlled-environment Agriculture,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link 2402
flower and fruit detection, growth monitoring, pest and disease detection, multimodality, Agriculture 5.0 BibRef

Nijak, M.[Mateusz], Skrzypczy?ski, P.[Piotr], ?wian, K.[Krzysztof], Zawada, M.[Micha?], Szymczyk, S.[Sebastian], Wojciechowski, J.[Jacek],
On the Importance of Precise Positioning in Robotised Agriculture,
RS(16), No. 6, 2024, pp. 985.
DOI Link 2403

Li, D.F.[Dong-Fang], Li, B.[Boliao], Feng, H.[Huaiqu], Kang, S.[Shuo], Wang, J.[Jun], Wei, Z.B.[Zhen-Bo],
Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection,
PandRS(210), 2024, pp. 25-38.
Elsevier DOI Code:
WWW Link. 2404
Global Path planning, 3D path detection, Oblique photography, Deep learning, Image processing, Precision agriculture BibRef

Zhao, R.[Richeng], Yuan, X.J.[Xian-Ju], Yang, Z.P.[Zhan-Peng], Zhang, L.[Lei],
Image-based crop row detection utilizing the Hough transform and DBSCAN clustering analysis,
IET-IPR(18), No. 5, 2024, pp. 1161-1177.
DOI Link 2404
Agricultural engineering, Canny algorithm, crop row detection, DBSCAN clustering, ExGR index, Hough transform, image processing BibRef

Hendra, H.[Hansen], Liu, Y.[Yubin], Ishikawa, R.[Ryoichi], Oishi, T.[Takeshi], Sato, Y.[Yoshihiro],
Quadruped Robot Platform for Selective Pesticide Spraying,
DOI Link 2403
Support vector machines, Machine vision, Spraying, Crops, Training data, Pesticides, Object detection BibRef

Padeiro, C.V.[Carlos Victorino], Komamizu, T.[Takahiro], Ide, I.[Ichiro],
Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection,
DOI Link 2403
Visualization, Plant diseases, Power supplies, Crops, Object detection, Detectors, Network architecture BibRef

Jol, C.[Cees], Wen, J.[Junhan], van Gemert, J.C.[Jan C.],
Non-Destructive Infield Quality Estimation of Strawberries using Deep Architectures,

Farag, M.[Mohamed], Kierdorf, J.[Jana], Roscher, R.[Ribana],
Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods,

Penzel, N.[Niklas], Kierdorf, J.[Jana], Roscher, R.[Ribana], Denzler, J.[Joachim],
Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances,

Pagé-Fortin, M.[Mathieu],
Class-Incremental Learning of Plant and Disease Detection: Growing Branches with Knowledge Distillation,

Zampokas, G.[Georgios], Mariolis, I.[Ioannis], Giakoumis, D.[Dimitrios], Tzovaras, D.[Dimitrios],
Residual Cascade CNN for Detection of Spatially Relevant Objects in Agriculture: The Grape-stem Paradigm,
Springer DOI 2312

Wu, Z.X.[Zheng-Xian], Liu, X.[Xingpeng], Xue, Y.[YiMing], Wen, J.[Juan], Peng, W.L.[Wan-Li],
HDTC: Hybrid Model of Dual-Transformer and Convolutional Neural Network from RGB-D for Detection of Lettuce Growth Traits,

\ Chai, A.Y.H.[Abel Yu Hao], Lee, S.H.[Sue Han], Tay, F.S.[Fei Siang], Then, Y.L.[Yi Lung], Goëau, H.[Hervé], Bonnet, P.[Pierre], Joly, A.[Alexis],
Pairwise Feature Learning for Unseen Plant Disease Recognition,

Goncalves, D.N.[Diogo Nunes], Marcato, J.[Jose], Zamboni, P.[Pedro], Pistori, H.[Hemerson], Li, J.[Jonathan], Nogueira, K.[Keiller], Goncalves, W.N.[Wesley Nunes],
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture,

Panda, S.K.[Shivam K.], Lee, Y.[Yongkyu], Jawed, M.K.[M. Khalid],
Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection,

Tavera, A.[Antonio], Arnaudo, E.[Edoardo], Masone, C.[Carlo], Caputo, B.[Barbara],
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images,
Training, Image segmentation, Adaptation models, Adaptive systems, Semantics, Sampling methods, Loss measurement BibRef

Shen, Y.[Yao], Wang, L.[Lei], Jin, Y.[Yue],
AAFormer: A Multi-Modal Transformer Network for Aerial Agricultural Images,
Image segmentation, Image recognition, Semantics, Transformers, Feature extraction BibRef

Ciarfuglia, T.A.[Thomas A.], Motoi, I.M.[Ionut Marian], Saraceni, L.[Leonardo], Nardi, D.[Daniele],
Pseudo-label Generation for Agricultural Robotics Applications,
Structure from motion, Motion segmentation, Pipelines, Refining, Spraying, Lighting, Pattern recognition BibRef

Daniela, L.[Lovarelli], Daniel, B.[Berckmans], Jacopo, B.[Bacenetti], Marcella, G.[Guarino],
Suggestions for the Environmental Sustainability from Precision Livestock Farming and Replacement in Dairy Cows,
Springer DOI 2208

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), 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

Hassanein, M., Khedr, M., El-Sheimy, N.,
Crop Row Detection Procedure Using Low-cost UAV Imagery System,
DOI Link 1912

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,
DOI Link 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:Apr 18, 2024 at 11:38:49