Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees

doi:10.1016/J.COMPAG.2012.12.002

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Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees

Journal Article•DOI•

Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees

Francisco Garcia-Ruiz¹, Sindhuja Sankaran², Joe Mari Maja², Won Suk Lee², Jesper Rasmussen¹, Reza Ehsani² - Show less +2 more•Institutions (2)

University of Copenhagen¹, University of Florida²

01 Feb 2013-Computers and Electronics in Agriculture (Elsevier Science Publishers B. V.)-Vol. 91, pp 106-115

TL;DR: High-resolution aerial sensing has good prospect for the detection of HLB-infected trees and among the tested classification algorithms, support vector machine (SVM) with kernel resulted in better performance than other methods such as SVM (linear), linear discriminant analysis and quadratic discriminantAnalysis.

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About: This article is published in Computers and Electronics in Agriculture.The article was published on 2013-02-01. It has received 281 citations till now.

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Journal Article•DOI•

Using Deep Learning for Image-Based Plant Disease Detection

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Sharada P. Mohanty, David P. Hughes¹, Marcel Salathé²•Institutions (2)

Pennsylvania State University¹, École Polytechnique Fédérale de Lausanne²

22 Sep 2016-Frontiers in Plant Science

TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.

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Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

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2,150 citations

Book•

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

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Hazim Shakhatreh¹, Ahmad Sawalmeh², Ahmad Sawalmeh³, Ala Al-Fuqaha⁴, Ala Al-Fuqaha⁵, Zuochao Dou, Eyad Almaita⁶, Issa Khalil⁷, Noor Shamsiah Othman², Abdallah Khreishah⁸, Mohsen Guizani⁹ - Show less +7 more•Institutions (9)

Yarmouk University¹, Universiti Tenaga Nasional², Northern Borders University³, Khalifa University⁴, Western Michigan University⁵, Tafila Technical University⁶, Qatar Computing Research Institute⁷, New Jersey Institute of Technology⁸, University of Idaho⁹

26 Aug 2021

TL;DR: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection.

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Abstract: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

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901 citations

Cites background or methods from "Comparison of two aerial imaging pl..."

...Another challenge, when UAVs are used to cover large areas, is that it needs to return many times to the charging station for recharging [105], [109], [112]....
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...PRECISION AGRICULTURE UAVs can be utilized in precision agriculture (PA) for crop management and monitoring [105], [106], weed detection [107], irrigation scheduling [108], disease detection [109], pesticide spraying [105] and gathering data from ground sensors (moisture, soil properties, etc....
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Journal Article•DOI•

Machine Learning for High-Throughput Stress Phenotyping in Plants

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Arti Singh¹, Baskar Ganapathysubramanian¹, Asheesh K. Singh¹, Soumik Sarkar¹•Institutions (1)

Iowa State University¹

01 Feb 2016-Trends in Plant Science

TL;DR: This work provides a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

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633 citations

Cites background or methods from "Comparison of two aerial imaging pl..."

...Identification SVM UAV- and aircraft-based sensors Citrus Huanglongbing (HLB) Disease [37]...
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...Machine Learning (ML) Tools for High-Throughput Stress Phenotyping (A) (B) (C) (D) Spa al coverage (dimensions of field) Tempo ral cov erage ( crop cy cle) Iden fica on Quan fica on Predic on Quan fica on Predic on Sudden death syndrome (SDS) (bio c stress) Iron deficiency chlorosis (IDC) (abio c stress) IDC Class 1 (no stress) IDC Class 2 (moderate stress) IDC Class 3 (completely stressed) Bacterial pustule (bio c stress) Classifica on Iden fica on Classifica on IDC (% severity examples) 0.2% Severity 5.3% Severity 17.8% Severity 98.2% Severity 54.2% Severity Weather data ML algorithm Modeling Early stress predic on Stress image Stress severity data K–NN SVM RF BCK–means K–NN SOM LDA/ QDA SVM ANN K–means SOM DLA DLA GMM BN BM Naïve Bayer DBN K–means HC MF LR,NLR Lin R Used in plant stress phenotyping No evidence of use in plant stress phenotyping Arrows represent mul ple variants of a method SVM CRF LDA Key: ANN K–NN SOM CNN DNN BC DT RF HMM Lat DA PCA/ ICA SVM Genera ve and unsupervised Discrimina ve and unsupervised Discrimina ve and supervised Genera ve and supervised SVM ANN Barley, riceChili pepper, olive, tomato....
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...BC Arabidopsis, chili pepper, clover, oilseed rape, sugar beet, tomato Apple, barley, citrus, chili pepper, co on, clover, oilseed rape, orchid, rice, spinach, sugar beet, sunflower, tomato, wheat LDA/ QDA Trends in Plant Science, February 2016, Vol. 21, No. 2 111 [11], hyperspectral [12–15], thermal [16,17], fluorescence [16,18], and 3D laser scanning [19] to trichromatic (RGB) [20] imaging in conjunction with advanced autonomous vehicles, have truly opened up the possibility of high-throughput stress phenotyping (HTSP)....
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...Results showed that a (non-linear) SVM with kernel worked better than (linear) SVM, LDA, and QDA [37]....
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...Diseased regions on the tomato canopy were identified using the preprocessed tomato images by a SOM model [20]....
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Journal Article•DOI•

Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs)

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Gonzalo Pajares¹•Institutions (1)

Complutense University of Madrid¹

01 Apr 2015-Photogrammetric Engineering and Remote Sensing

TL;DR: An overview of different areas of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments is presented, each independent from the others so that the reader does not need to read the full paper when a specific application is of interest.

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Abstract: Remotely Piloted Aircraft (RPA) is presently in continuous development at a rapid pace. Unmanned Aerial Vehicles (UAVs) or more extensively Unmanned Aerial Systems (UAS) are platforms considered under the RPAs paradigm. Simultaneously, the development of sensors and instruments to be installed onboard such platforms is growing exponentially. These two factors together have led to the increasing use of these platforms and sensors for remote sensing applications with new potential. Thus, the overall goal of this paper is to provide a panoramic overview about the current status of remote sensing applications based on unmanned aerial platforms equipped with a set of specific sensors and instruments. First, some examples of typical platforms used in remote sensing are provided. Second, a description of sensors and technologies is explored which are onboard instruments specifically intended to capture data for remote sensing applications. Third, multi-UAVs in collaboration, coordination, and cooperation in remote sensing are considered. Finally, a collection of applications in several areas are proposed, where the combination of unmanned platforms and sensors, together with methods, algorithms, and procedures provide the overview in very different remote sensing applications. This paper presents an overview of different areas, each independent from the others, so that the reader does not need to read the full paper when a specific application is of interest

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587 citations

Journal Article•DOI•

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

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Guijun Yang¹, Jiangang Liu¹, Chunjiang Zhao¹, Zhenhong Li², Yanbo Huang³, Yu Haiyang¹, Bo Xu¹, Xiaodong Yang¹, Dongmei Zhu, Xiaoyan Zhang⁴, Ruyang Zhang, Haikuan Feng¹, Zhao Xiaoqing¹, Zhenhai Li¹, Heli Li¹, Hao Yang¹ - Show less +12 more•Institutions (4)

Center for Information Technology¹, Newcastle University², United States Department of Agriculture³, Nanjing Agricultural University⁴

30 Jun 2017-Frontiers in Plant Science

TL;DR: The current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed and can provide theoretical and technical support to promote the applications of Uav-R SPs for crop phenotypesing.

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Abstract: Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

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441 citations

Cites methods from "Comparison of two aerial imaging pl..."

...…multi-band imaging sensor was deployed to acquire high-resolution aerial imaging for Huanglongbing (HLB) detection, which yielding that there’s significant difference for the 710 nm spectral reflectance and the NIR-R index values between healthy and HLB-infected trees (Garcia-Ruiz et al., 2013)....
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References

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Open Access

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Journal Article•DOI•

Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

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J.A.J. Berni, Pablo J. Zarco-Tejada, Lola Suarez, Elias Fereres

10 Feb 2009-IEEE Transactions on Geoscience and Remote Sensing

TL;DR: The ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors is demonstrated, demonstrating comparable estimations, if not better, than those obtained by traditional manned airborne sensors.

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Abstract: Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.

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1,106 citations

Journal Article•DOI•

Review: A review of advanced techniques for detecting plant diseases

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Sindhuja Sankaran¹, Ashish Mishra¹, Reza Ehsani¹, Cristina E. Davis²•Institutions (2)

University of Florida¹, University of California, Davis²

01 Jun 2010-Computers and Electronics in Agriculture

TL;DR: In this article, the authors present a review of the currently used technologies that can be used for developing a ground-based sensor system to assist in monitoring health and diseases in plants under field conditions.

...read moreread less

965 citations

Journal Article•DOI•

Asian citrus psyllids (sternorrhyncha: psyllidae) and greening disease of citrus: a literature review and assessment of risk in florida

[...]

Susan E. Halbert¹, K. L. Manjunath²•Institutions (2)

Florida Department of Agriculture and Consumer Services¹, University of Florida²

01 Sep 2004-Florida Entomologist

TL;DR: Management of citrus greening disease is difficult and requires an integrated approach including use of clean stock, elimination of inoculum via voluntary and regulatory means, use of pesticides to control psyllid vectors in the citrus crop, and biological control of psyllID vectors in non-crop reservoirs.

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Abstract: The Asian citrus psyllid, Diaphorina citri Kuwayama, was discovered in Florida in 1998. It can be one of the most serious pests of citrus if the pathogens that cause citrus greening disease (huanglongbing) are present. Citrus greening recently has been reported in Brazil by Fundecitrus, Brazil. The establishment of D. citri in Florida increases the possibility that the disease may become established. Diaphorina citri can be separated from about 13 other species of psyllids reported on citrus. The biology of D. citri makes it ideally suited to the Florida climate. Only two species, D. citri and Trioza erytreae (del Guercio), have been implicated in spread of citrus greening, a disease caused by highly fastidious phloem-inhabiting bacteria. The disease is characterized by blotchy mottle on the leaves, and misshapen, poorly colored off-tasting fruit. In areas where the disease is endemic, citrus trees may live for only 5-8 years and never bear usable fruit. The disease occurs throughout much of Asia and Africa south of the Sahara Desert, on several small islands in the Indian Ocean, and in the Saudi Arabian Peninsula. Transmission of citrus greening occurs primarily via infective citrus psyllids and grafting. It is transmissible experimentally through dodder and might be transmitted by seed from infected plants and transovarially in psyllid vectors. Citrus greening disease is restricted to Citrus and close citrus relatives because of the narrow host range of the psyllid vectors. Management of citrus greening disease is difficult and requires an integrated approach including use of clean stock, elimination of inoculum via voluntary and regulatory means, use of pesticides to control psyllid vectors in the citrus crop, and biological control of psyllid vectors in non-crop reservoirs. There is no place in the world where citrus greening disease occurs that it is under completely successful management. Eradication of citrus greening disease may be possible if it is detected early. Research is needed on rapid and robust diagnosis, disease epidemiology, and psyllid vector control.

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946 citations

"Comparison of two aerial imaging pl..." refers background in this paper

...HLB is caused by a bacterium and was first found in Florida in August of 2005, although the insect vector of this disease, psyllid (Diaphorina citri) was found back in 1998 (Halbert and Manjunath, 2004; Gottwald, 2010)....
[...]

Journal Article•DOI•

Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera

[...]

Pablo J. Zarco-Tejada¹, Victoria González-Dugo¹, J.A.J. Berni¹•Institutions (1)

Spanish National Research Council¹

15 Feb 2012-Remote Sensing of Environment

TL;DR: In this article, the remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery.

...read moreread less

715 citations

"Comparison of two aerial imaging pl..." refers background in this paper

...Potential applications of aerial remote sensing platforms have opened in the last few years with the availability of smaller autonomous aerial platforms capable of flying at low altitudes and diverse set of miniaturized sensors (Berni et al., 2008; Zarco-Tejada et al., 2012)....
[...]
..., 2011), water stress detection and decision support (Sepulcre-Cantó et al., 2006; Berni et al., 2009a,b; Zarco-Tejada et al., 2012) and yield estimation (Swain et al....
[...]
...…detection and mapping for site specific herbicide application (Kazmi et al., 2011; Fernandez-Quintanilla et al., 2011), water stress detection and decision support (Sepulcre-Cantó et al., 2006; Berni et al., 2009a,b; Zarco-Tejada et al., 2012) and yield estimation (Swain et al., 2010) among others....
[...]

Journal Article•DOI•

Current Epidemiological Understanding of Citrus Huanglongbing

[...]

Tim R. Gottwald¹•Institutions (1)

United States Department of Agriculture¹

05 Aug 2010-Annual Review of Phytopathology

TL;DR: Huanglongbing (HLB) is the most destructive citrus pathosystem worldwide and all infected commercial citrus industries continue to decline owing to inadequate current control methods.

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Abstract: Huanglongbing (HLB) is the most destructive citrus pathosystem worldwide. Previously known primarily from Asia and Africa, it was introduced into the Western Hemisphere in 2004. All infected commercial citrus industries continue to decline owing to inadequate current control methods. HLB increase and regional spatial spread, related to vector populations, are rapid compared with other arboreal pathosystems. Disease dynamics result from multiple simultaneous spatial processes, suggesting that psyllid vector transmission is a continuum from local area to very long distance. Evolutionarily, HLB appears to have originated as an insect endosymbiont that has moved into plants. Lack of exposure of citrus to the pathogen prior to approximately 100 years ago did not provide sufficient time for development of resistance. A prolonged incubation period and regional dispersal make eradication nonviable. Multiple asymptomatic infections per symptomatic tree, incomplete systemic distribution within trees, and prolonged incubation period make detection difficult and greatly complicate disease control.

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714 citations