Centre dedocumentation
scientifique

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 1 - 17 Titre : A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; Diogo Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] carte agricole [Termes IGN] Citrus sinensis [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comptage [Termes IGN] cultures [Termes IGN] détection d'objet [Termes IGN] extraction de la végétation [Termes IGN] gestion durable [Termes IGN] image captée par drone [Termes IGN] maïs (céréale) [Termes IGN] rendement agricole Résumé : (auteur) Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch; which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-measure equal to 0.876. These results were superior to the results from other deep networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and dataset. For the plantation-row detection, our approach returned precision, recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test the robustness of our model with a different type of agriculture, we performed the same task in the citrus orchard dataset. It returned an MAE equal to 1.409 citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and F-measure of 0.965. For the citrus plantation-row detection, our approach resulted in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964, respectively. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops. The method proposed here may be applied to future decision-making models and could contribute to the sustainable management of agricultural systems. Numéro de notice : A2021-205 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.024 Date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97171 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 322 Titre : A density-based algorithm for the detection of individual trees from LiDAR data Type de document : Article/Communication Auteurs : Melissa Latella, Auteur ; Fabio Sola, Auteur ; Carlo Camporeal, Auteur Année de publication : 2021 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] comptage [Termes IGN] densité de la végétation [Termes IGN] détection d'arbres [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt de feuillus [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] semis de points [Termes IGN] sous-étage Résumé : (auteur) Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. Numéro de notice : A2021-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13020322 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020322 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97146 [article]

Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery / Debasish Chakraborty in Geocarto international, vol 36 n° 1 ([01/01/2021])  

Public [article]  inGeocarto international > vol 36 n° 1 [01/01/2021] . - pp 110 - 120 Titre : Steps-based tree crown delineation by analyzing local minima for counting the trees in very high resolution satellite imagery Type de document : Article/Communication Auteurs : Debasish Chakraborty, Auteur ; Pranshu Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 110 - 120 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arborescence [Termes IGN] comptage [Termes IGN] détection de contours [Termes IGN] houppier [Termes IGN] image à haute résolution [Termes IGN] image à très haute résolution [Termes IGN] image satellite [Termes IGN] image Worldview [Termes IGN] itération [Termes IGN] segmentation d'image Résumé : (Auteur) In this study primarily, high-resolution (HR) satellite image is segmented into tree and non-tree regions. Thereafter plots the local minima in the segmented image. Point surrounded by the higher intensity values is called as local minima. The local minimum is the starting point for marking the tree crown boundary. The adjacent darker points along the local minima are plotted in a specific direction for marking the tree crown boundary. Subsequently a seven steps iterative procedure is followed for delineating and counting the tree crowns. The validation of the method is done on WorldView-2 data. Numéro de notice : A2021-054 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1611947 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1611947 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96779 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2021011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The challenge of robust trait estimates with deep learning on high resolution RGB images / Etienne David (2021)  

Public Titre : The challenge of robust trait estimates with deep learning on high resolution RGB images Type de document : Thèse/HDR Auteurs : Etienne David, Auteur ; Frédéric Baret, Directeur de thèse Editeur : Avignon : Université d'Avignon Année de publication : 2021 Importance : 145 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université d'Avignon, spécialité Sciences Agronomiques Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] base de données d'images [Termes IGN] blé (céréale) [Termes IGN] céréales [Termes IGN] comptage [Termes IGN] cultures [Termes IGN] densité de la végétation [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] jeu de données [Termes IGN] surveillance agricole Index. décimale : THESE Thèses et HDR Résumé : (auteur) High throughput plant phenotyping, especially in the context of open field acquisitions, relies on the interpretation of data from different sensors implemented on various vectors such as tractors, robots or drones. Initially, these data were interpreted using remote sensing algorithms that exploit the spatial resolution of the signal. Since 2015, however, progresses of ”Deep Learning”, based on the training on examples, has already obtained promising results for measuring the rate of cover, counting plants or organs. It uses learned convolution layers, can take advantage of the spatial organization of the signal. The advantage of these methods is that they are based on Red-Green-Blue (RGB) sensors, which are much less expensive than multi- or hyperspectral imagers. However, these methods are sensitive to changes in the distribution between the data used in training and the predicted data. In practice, variable prediction errors from site to site can be observed using these methods. The objective of the thesis is to understand the causes of these variations and propose solutions for reliable phenotypic trait estimates using Deep Learning. The study focuses on detecting plants and organs from high-resolution RGB images acquired in the field. Our work first focused on the constitution of diversified image databases from different locations and stages of development for plant emergence (maize, beet, sunflower) and wheat ears, which allowed the publication of two annotated databases, grouping 27 acquisition sessions for thedrone and 47 for the ear detection. The datasets demonstrate the performances difference between the published results and ours due to the change in distribution. To go beyond the limits of the usual methods, we organized two data competitions, the Global Wheat Challenges, in 2020 and 2021, which allowed us to obtain solutions trained for robustness on a different data set than the training one. The analysis of the solutions showed the importance of the training strategies for robustness beyond the architectures used. We have also shown that these solutions can be effectively deployed as a replacement for manual counting. Finally, we have demonstrated the inefficiency of training functions designed for robust training. Our work opens the prospect of a better evaluation of Deep Learning in the context of high-throughput phenotyping and thus of confidence in its use in real-life conditions. Note de contenu : 1- Introduction 2- Evaluation of the robustness of handcrafted and deep learning methods for plant density estimation 3- Design of a large and diverse dataset for training and evaluating deep learning models: application to wheat head detection 4- Competition design to train robust Deep Learn model: the example of the Global Wheat Challenges 5- GlobalWheat-Wilds: Global Wheat Head Dataset as a benchmark of in-the-wild distribution shifts 6- Conclusion and perspectives Numéro de notice : 15244 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences Agronomiques : Avignon : 2021 Organisme de stage : Laboratoire EMMAH DOI : sans En ligne : https://hal.inrae.fr/tel-03431192v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100610

Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 73 - 83 Titre : Counting of grapevine berries in images via semantic segmentation using convolutional neural networks Type de document : Article/Communication Auteurs : Laura Zabawa, Auteur ; Anna Kicherer, Auteur ; Lasse Klingbeil, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] comptage [Termes IGN] échantillon [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction semi-automatique [Termes IGN] régression [Termes IGN] rendement agricole [Termes IGN] segmentation sémantique [Termes IGN] traitement d'image [Termes IGN] viticulture Résumé : (auteur) The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH. Numéro de notice : A2020-252 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.002 Date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.002 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94996 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020063	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020062	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Cattle detection and counting in UAV images based on convolutional neural networks / Wen Shao in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

Permalink

WeCount, le trafic compté par les citoyens / Victor Oxombre (2020) 

Permalink

Détection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018) 

Permalink

Fragmentation linéaire, restauration d’une continuité écologique / Marie-Lou Lamy (2018) 

Permalink

Wildlife management using aiborne Lidar / Joan Hagar in GIM international [en ligne], vol 30 n° 7 (July 2016)

Permalink

Complexité algorithmique / Sylvain Perifel (2014)

Permalink

Localisation des troupeaux d'herbivores au Zimbabwe : composer avec l'imprécision / Elodie Buard in Mappemonde, n° 107 (2012-3)  

Permalink