Detection of Vegetation Using Unmanned Aerial Vehicles Images: A Systematic Review

In recent years, the use of unmanned aerial vehicles for the development of agricultural and forestry applications has become popular. Given the importance of plants in both rural and urban environments, the extraction of information from data obtained by remote sensing is a highly relevant area of research. Such information is vital in urban and rural planning activities, the establishment of sustainable ecological niches, estimation of crop yields, the realization of forest inventories, the supervision of agricultural management systems, and some other agricultural applications. In this paper, we present a systematic review of the literature regarding the use of unmanned aerial vehicles in applications related to the detection of vegetation and plants inventory. Likewise, an analysis of the software used and the main computational and statistical techniques for the processing of images taken from unmanned aerial vehicles is made.

[1]  Zifeng Guo,et al.  A framework for the management of agricultural resources with automated aerial imagery detection , 2019, Comput. Electron. Agric..

[2]  Xin Shen,et al.  Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests , 2019, Remote. Sens..

[3]  Francisco Herrera,et al.  Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning , 2019, Remote. Sens..

[4]  Le Wang,et al.  Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges , 2019, Remote Sensing of Environment.

[5]  Edison Pignaton de Freitas,et al.  A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms , 2019, Journal of Intelligent & Robotic Systems.

[6]  Wilson Castro,et al.  Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces , 2019, IEEE Access.

[7]  Imran Ahmed,et al.  Automatic Detection and Segmentation of Lentil Crop Breeding Plots From Multi-Spectral Images Captured by UAV-Mounted Camera , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[8]  Lorenzo Comba,et al.  Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture , 2018, Comput. Electron. Agric..

[9]  Serdar Selim,et al.  Semi-automatic Tree Detection from Images of Unmanned Aerial Vehicle Using Object-Based Image Analysis Method , 2018, Journal of the Indian Society of Remote Sensing.

[10]  Guilherme Vicentim Nardari,et al.  Crop Anomaly Identification with Color Filters and Convolutional Neural Networks , 2018, 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE).

[11]  Adel Hafiane,et al.  Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images , 2018, Remote. Sens..

[12]  Hao Yang,et al.  Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation , 2018, Remote. Sens..

[13]  Xiaolin Zhu,et al.  Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier , 2018, International Journal of Remote Sensing.

[14]  Ali Ozgun Ok,et al.  Combining Orientation Symmetry and LM Cues for the Detection of Citrus Trees in Orchards From a Digital Surface Model , 2018, IEEE Geoscience and Remote Sensing Letters.

[15]  Dilek Koc-San,et al.  Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform , 2018, Comput. Electron. Agric..

[16]  Bazila Bashir,et al.  Tree Crown Detection, Delineation and Counting in UAV Remote Sensed Images: A Neural Network Based Spectral–Spatial Method , 2018, Journal of the Indian Society of Remote Sensing.

[17]  Ana Paula Dalla Corte,et al.  TREEDETECTION: AUTOMATIC TREE DETECTION USING UAV-BASED DATA , 2018, FLORESTA.

[18]  Chongcheng Chen,et al.  Individual Tree Crown Detection and Delineation From Very-High-Resolution UAV Images Based on Bias Field and Marker-Controlled Watershed Segmentation Algorithms , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  J. Alex Thomasson,et al.  Disease detection and mitigation in a cotton crop with UAV remote sensing , 2018, Commercial + Scientific Sensing and Imaging.

[20]  Aleksandra Pizurica,et al.  Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Emanuel Peres,et al.  A pilot digital image processing approach for detecting vineyard parcels in Douro region through high-resolution aerial imagery , 2018, ICGDA.

[22]  Vitor C. Guizilini,et al.  Failure Detection in Row Crops From UAV Images Using Morphological Operators , 2018, IEEE Geoscience and Remote Sensing Letters.

[23]  Vinod P V,et al.  CNN Based Technique for Automatic Tree Counting Using Very High Resolution Data , 2018, 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C).

[24]  Naser El-Sheimy,et al.  A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms , 2018, Sensors.

[25]  R. Lucas,et al.  Managing mangrove forests from the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia , 2018 .

[26]  Maoguo Gong,et al.  Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Peter Surový,et al.  Automatic detection and quantification of wild game crop damage using an unmanned aerial vehicle (UAV) equipped with an optical sensor payload: a case study in wheat , 2018 .

[28]  Hemerson Pistori,et al.  Weed detection in soybean crops using ConvNets , 2017, Comput. Electron. Agric..

[29]  Edward J. Delp,et al.  Counting plants using deep learning , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[30]  Vitor Campanholo Guizilini,et al.  Automatic detection of fruits in coffee crops from aerial images , 2017, 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR).

[31]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[32]  Xanthoula Eirini Pantazi,et al.  Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images , 2017, Sensors.

[33]  Liviu Theodor Ene,et al.  Use of partial-coverage UAV data in sampling for large scale forest inventories , 2017 .

[34]  Gérard Dedieu,et al.  Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery , 2017, Remote. Sens..

[35]  Matthew Bardeen,et al.  Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard , 2017, Remote. Sens..

[36]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[37]  Moncef Gabbouj,et al.  Automated tree detection and density calculation using unmanned aerial vehicles , 2016, 2016 Visual Communications and Image Processing (VCIP).

[38]  Brendan F. Kohrn,et al.  Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology , 2016, Applications in Plant Sciences.

[39]  Pedro Antonio Gutiérrez,et al.  A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method , 2015, Appl. Soft Comput..

[40]  Terje Gobakken,et al.  Inventory of Small Forest Areas Using an Unmanned Aerial System , 2015, Remote. Sens..

[41]  Pedro Antonio Gutiérrez,et al.  An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and Object-Based Analysis , 2015, IWANN.

[42]  Jorge Torres-Sánchez,et al.  An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops , 2015, Comput. Electron. Agric..

[43]  Lorenzo Comba,et al.  Vineyard detection from unmanned aerial systems images , 2015, Comput. Electron. Agric..

[44]  Fabio Tozeto Ramos,et al.  Automatic detection of Ceratocystis wilt in Eucalyptus crops from aerial images , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[45]  Jorge Torres-Sánchez,et al.  Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution , 2015, Sensors.

[46]  Naif Alajlan,et al.  Efficient Framework for Palm Tree Detection in UAV Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  Barbara Koch,et al.  Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds , 2014 .

[48]  Naif Alajlan,et al.  An automatic approach for palm tree counting in UAV images , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[49]  Claes Wohlin,et al.  Guidelines for snowballing in systematic literature studies and a replication in software engineering , 2014, EASE '14.

[50]  Arko Lucieer,et al.  Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .

[52]  R A Diaz-Varela,et al.  Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. , 2014, Journal of environmental management.

[53]  Pablo J. Zarco-Tejada,et al.  High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices , 2013 .

[54]  Khairul Nizam Tahar,et al.  An Evaluation on Fixed Wing and Multi-Rotor UAV Images Using Photogrammetric Image Processing , 2013 .

[55]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[56]  Salah Sukkarieh,et al.  Multi-class predictive template for tree crown detection , 2012 .

[57]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[58]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[59]  Liang Han,et al.  Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network , 2019, Remote. Sens..

[60]  Andréa Britto Mattos,et al.  Automatic Citrus Tree Detection from UAV Images based on Convolutional Neural Networks , 2018 .

[61]  E. Honkavaara,et al.  Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft , 2018 .

[62]  W. E. Santiago,et al.  Identification of weeds in sugarcane fields through images taken by UAV and Random Forest classifier , 2016 .

[63]  Ahmad Kamal Nasir,et al.  Precision Forestry: Trees Counting in Urban Areas Using Visible Imagery based on an Unmanned Aerial Vehicle , 2016 .

[64]  Yunjun Yao,et al.  Use of UAV oblique imaging for the detection of individual trees in residential environments , 2015 .

[65]  Görres Grenzdörffer,et al.  THE PHOTOGRAMMETRIC POTENTIAL OF LOW-COST UAVs IN FORESTRY AND AGRICULTURE , 2008 .