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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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] Citrus sinensis [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] comptage [Termes descripteurs IGN] cultures [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] gestion durable [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] maïs (céréale) [Termes descripteurs 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]

Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 557-569 Titre : Precise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering Type de document : Article/Communication Auteurs : Ali Ozgun Ok, Auteur ; Asli Ozdarici-Ok, Auteur Année de publication : 2020 Article en page(s) : pp 557-569 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] Citrus limon [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] extraction d'arbres [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] Turquie Résumé : (Auteur) In this study, we present an original unified strategy for the precise extraction of individual citrus fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the circular shape information with the knowledge of the local maxima in the DSM has been used for the detection of the candidate trees. An active contour is applied within each detected region to extract the borders of the objects. Thereafter, all extracted objects are seamlessly divided into clusters considering a new feature data set formed by (1) the properties of trees, (2) planting parameters, and (3) neighborhood relations. This original clustering stage has led to two new contributions: (1) particular objects or clustered structures having distinctive characters and relationships other than the citrus objects can be identified and eliminated, and (2) the information revealed by clustering can be used to recover missing citrus objects within and/or nearby each cluster. The main finding of this research is that a successful clustering can provide valuable input for identifying incorrect and missing information in terms of citrus tree extraction. The proposed strategy is validated in eight test sites selected from the northern part of Mersin province of Turkey. The results achieved are also compared with the state-of-the-art methods developed for tree extraction, and the success of the proposed unified strategy is clearly highlighted. Numéro de notice : A2020-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.557 date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.557 Format de la ressource électronique : LUR article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95933 [article]

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A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106 Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] citrus (genre) [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] verger Résumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525 [article]

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Analysis of full-waveform LiDAR data for classification of an orange orchard scene / Karolina D. Fieber in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 63 - 82 Titre : Analysis of full-waveform LiDAR data for classification of an orange orchard scene Type de document : Article/Communication Auteurs : Karolina D. Fieber, Auteur ; Ian J. Davenport, Auteur ; James M. Ferryman, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 63 - 82 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Citrus sinensis [Termes descripteurs IGN] classification [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] lidar à retour d'onde complète [Termes descripteurs IGN] verger Résumé : (Auteur) Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient Y was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and Y. For single-peak waveforms the scatterplot of Y versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return Y values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the Y versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient Y of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties. Numéro de notice : A2013-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32550 [article]

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Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 71 (July 2012) . - pp 47 - 61 Titre : Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices Type de document : Article/Communication Auteurs : S. Stagakis, Auteur ; V. Gonzales-Dugo, Auteur ; P. Cid, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 47 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Citrus sinensis [Termes descripteurs IGN] cultures irriguées [Termes descripteurs IGN] drone [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] Photochemical reflectance index [Termes descripteurs IGN] Séville [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] verger Résumé : (Auteur) This paper deals with the monitoring of water status and the assessment of the effect of stress on citrus fruit quality using structural and physiological remote sensing indices. Four flights were conducted over a citrus orchard in 2009 using an unmanned aerial vehicle (UAV) carrying a multispectral camera with six narrow spectral bands in the visible and near infrared. Physiological indices such as the Photochemical Reflectance Index (PRI570), a new structurally robust PRI formulation that uses the 515 nm as the reference band (PRI515), and a chlorophyll ratio (R700/R670) were compared against the Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI) and Modified Triangular Vegetation Index (MTVI) canopy structural indices for their performance in tracking water status and the effects of sustained water stress on fruit quality at harvest. The irrigation setup in the commercial orchard was compared against a treatment scheduled to satisfy full requirements (based on estimated crop evapotranspiration) using two regulated deficit irrigation (RDI) strategies. The water status of the trees throughout the experiment was monitored with frequent field measurements of stem water potential (?x), while titratable acidity (TA) and total soluble solids (TSS) were measured at harvest on selected trees from each irrigation treatment. The high spatial resolution of the multispectral imagery (30 cm pixel size) enabled identification of pure tree crown components, extracting the tree reflectance from shaded, sunlit and aggregated pixels. The physiological and structural indices were then calculated from each tree at the following levels: (i) pure sunlit tree crown, (ii) entire crown, aggregating the within-crown shadows, and (iii) simulating a lower resolution pixel, including tree crown, sunlit and shaded soil pixels. The resulting analysis demonstrated that both PRI formulations were able to track water status, except when water stress altered canopy structure. In such cases, PRI570 was more affected than PRI515 by the structural changes caused by sustained water stress throughout the season. Both PRI formulations were proven to serve as pre-visual water stress indicators linked to fruit quality TSS and TA parameters (r2 = 0.69 for PRI515 vs TSS; r2 = 0.58 vs TA). In contrast, the chlorophyll (R700/R670) and structural indices (NDVI, RDVI, MTVI) showed poor relationships with fruit quality and water status levels (r2 = 0.04 for NDVI vs TSS; r2 = 0.19 vs TA). The two PRI formulations showed strong relationships with the field-measured fruit quality parameters in September, the beginning of stage III, which appeared to be the period most sensitive to water stress and the most critical for assessing fruit quality in citrus. Both PRI515 and PRI570 showed similar performance for the two scales assessed (sunlit crown and entire crown), demonstrating that within-crown component separation is not needed in citrus tree crowns where the shaded vegetation component is small. However, the simulation conducted through spatial resampling on tree + soil aggregated pixels revealed that the physiological indices were highly affected by soil reflectance and between-tree shadows, showing that for TSS vs PRI515 the relationship dropped from r2 = 0.69 to r2 = 0.38 when aggregating soil + crown components. This work confirms a previous study that demonstrated the link between PRI570, water stress, and fruit quality, while also making progress in assessing the new PRI formulation (PRI515), the within-crown shadow effects on the physiological indices, and the need for high resolution imagery to target individual tree crowns for the purpose of evaluating the effects of water stress on fruit quality in citrus. Numéro de notice : A2012-347 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31793 [article]

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Development of daily spatial heat unit mapping from monthly climatic surfaces for the Australian continent / Nicholas C. Coops in International journal of geographical information science IJGIS, vol 15 n° 4 (june 2001)

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Significance of the remotely sensed thermal infrared measurements obtained over a citrus orchard / J.A. Sobrino in ISPRS Journal of photogrammetry and remote sensing, vol 44 n° 6 (March 1990)

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Efficacy of densitometric and multispectral techniques for monitoring infestations of citrus snow scale on citrus bark / L.I. Terry in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 10 (october 1989)

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Determination of frosts in orange groves from NOAA-9 AVHRR data / V. Caselles in Remote sensing of environment, vol 29 n° 2 (01/08/1989)

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Use of aerial color infrared photography, dual color video, and a computer system for property appraisal of citrus groves / C.H. Blazquez in Photogrammetric Engineering & Remote Sensing, PERS, vol 54 n° 2 (february 1988)

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