Computer Vision and Less Complex Image Analyses to Monitor Potato Traits in Fields.
Crops
Drone
Feature extraction
Field
Image analysis
Imaging sensors
Machine learning
Plant phenotyping
Potato
UAV/UAS
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2021
2021
Historique:
entrez:
27
8
2021
pubmed:
28
8
2021
medline:
12
1
2022
Statut:
ppublish
Résumé
Field phenotyping of crops has recently gained considerable attention leading to the development of new protocols for recording plant traits of interest. Phenotyping in field conditions can be performed by various cameras, sensors, and imaging platforms. In this chapter, practical aspects as well as advantages and disadvantages of aboveground phenotyping platforms are highlighted with a focus on drone-based imaging and relevant image analysis for field conditions. It includes useful planning tips for experimental design as well as protocols, sources, and tools for image acquisition, preprocessing, feature extraction, and machine learning highlighting the possibilities with computer vision. Several open and free resources are given to speed up data analysis for biologists.This chapter targets professionals and researchers with limited computational background performing or wishing to perform phenotyping of field crops, especially with a drone-based platform. The advice and methods described focus on potato but can mostly be used for field phenotyping of any crops.
Identifiants
pubmed: 34448165
doi: 10.1007/978-1-0716-1609-3_13
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
273-299Informations de copyright
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.
Références
Roeder AHK, Cunha A, Burl MC, Meyerowitz EM (2012) A computational image analysis glossary for biologists. Development 139:3071–3080. https://doi.org/10.1242/dev.076414
doi: 10.1242/dev.076414
pubmed: 22872081
Walter A, Liebisch F, Hund A (2015) Plant phenotyping: from bean weighing to image analysis. Plant Methods 11. https://doi.org/10.1186/s13007-015-0056-8
Fahlgren N, Gehan MA, Baxter I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99. https://doi.org/10.1016/j.pbi.2015.02.006
doi: 10.1016/j.pbi.2015.02.006
pubmed: 25733069
Chawade A, Van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R (2019) High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy 9. https://doi.org/10.3390/agronomy9050258
Gao J, Westergaard JC, Sundmark EHR, Bagge M, Liljeroth E, Alexandersson E (2021) Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowl Based Syst 214:106723. https://doi.org/10.1016/j.knosys.2020.106723
doi: 10.1016/j.knosys.2020.106723
Franceschini MHD, Bartholomeus H, van Apeldoorn DF, Suomalainen J, Kooistra L (2019) Feasibility of unmanned aerial vehicle optical imagery for early detection and severity assessment of late blight in potato. Remote Sens 11. https://doi.org/10.3390/rs11030224
Alexandersson E, Liljeroth E, Piikki K, Söderström M, Bagge O, Blomquist H, Persson M, Antkowiak P (2019) EnBlightMe!—an automated support system for potato late blight detection. https://www.vinnova.se/en/p/enblightme%2D%2D-an-automated-support-system-for-potato-late-blight-detection/ . Accessed 5 Nov 2019
Polder G, Blok PM, de Villiers HAC, van der Wolf JM, Kamp J (2019) Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:1–13. https://doi.org/10.3389/fpls.2019.00209
doi: 10.3389/fpls.2019.00209
Gao J, Liao W, Nuyttens D, Lootens P, Vangeyte J, Pižurica A, He Y, Pieters JG (2018) Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. Int J Appl Earth Obs Geoinf 67:43–53. https://doi.org/10.1016/j.jag.2017.12.012
doi: 10.1016/j.jag.2017.12.012
Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G (2018) A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Comput Ind 98:80–89. https://doi.org/10.1016/j.compind.2018.03.001
doi: 10.1016/j.compind.2018.03.001
Azizi A, Abbaspour-Gilandeh Y, Nooshyar M, Afkari-Sayah A (2016) Identifying potato varieties using machine vision and artificial neural networks. Int J Food Prop 19:618–635. https://doi.org/10.1080/10942912.2015.1038834
doi: 10.1080/10942912.2015.1038834
Su Q, Kondo N, Li M, Sun H, Al Riza DF, Habaragamuwa H (2018) Potato quality grading based on machine vision and 3D shape analysis. Comput Electron Agric 152:261–268. https://doi.org/10.1016/j.compag.2018.07.012
doi: 10.1016/j.compag.2018.07.012
Sankaran S, Khot LR, Espinoza CZ, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Pavek MJ (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur J Agron 70:112–123. https://doi.org/10.1016/j.eja.2015.07.004
doi: 10.1016/j.eja.2015.07.004
Liu Z, Gao J, Yang G, Zhang H, He Y (2016) Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci Rep 6:20410. https://doi.org/10.1038/srep20410
doi: 10.1038/srep20410
pubmed: 26864172
pmcid: 4750055
García-Santillán I, Peluffo-Ordoñez D, Caranqui V, Pusdá M, Garrido F, Granda P (2018) Computer vision-based method for automatic detection of crop rows in potato fields. In: Adv. intell. syst. comput., pp 355–366. https://doi.org/10.1007/978-3-319-73450-7_34
Dijkstra K, van de Loosdrecht J, Schomaker LRB, Wiering MA (2019) Centroidnet: a deep neural network for joint object localization and counting. In: Eur. conf. mach. learn. princ. pract. knowl. discov. databases, pp 585–601. https://doi.org/10.1007/978-3-030-10997-4_36
Madec S, Jin X, Lu H, De Solan B, Liu S, Duyme F, Heritier E, Baret F (2019) Ear density estimation from high resolution RGB imagery using deep learning technique. Agric For Meteorol 264:225–234. https://doi.org/10.1016/j.agrformet.2018.10.013
doi: 10.1016/j.agrformet.2018.10.013
Sara Mardanisamani FM, Kassani SH, Rajapaksa S, Duddu H, Wang M, Shirtliffe S, Ryu S, Josuttes A, Zhang T, Vail S, Pozniak C, Parkin I, Stavness I, Eramian M (2019) Crop lodging prediction from UAV-acquired images of wheat and canola using a DCNN augmented with handcrafted texture features. In: Proc. IEEE conf. comput. vis. pattern recognit. work.
Pantazi XE, Moshou D, Tamouridou AA (2019) Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Comput Electron Agric 156:96–104. https://doi.org/10.1016/j.compag.2018.11.005
doi: 10.1016/j.compag.2018.11.005
Islam M, Dinh A, Wahid K, Bhowmik P (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: Can. conf. electr. comput. eng. https://doi.org/10.1109/CCECE.2017.7946594
Gao J (2020) An exploration of the use of machine learning techniques for site-specific weed management. PhD thesis, Ghent University, Ghent, Belgium
Gao JF, Zhang C, Xie CQ, Le Zhu F, Guo ZH, He Y (2015) Prediction of the soluble solid content in sugarcanes by using near infrared hyperspectral imaging system. Spectrosc Spectr Anal 35:2154–2158. https://doi.org/10.3964/j.issn.1000-0593(2015)08-2154-05
doi: 10.3964/j.issn.1000-0593(2015)08-2154-05
López-López M, Calderón R, González-Dugo V, Zarco-Tejada PJ, Fereres E (2016) Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sens 8. https://doi.org/10.3390/rs8040276
ten Harkel J (2019) High-throughput phenotyping and field-based biomass estimation for winter wheat, sugar beet and potatoes using UAV LiDAR. https://www.wur.nl/en/activity/High-throughput-phenotyping-and-field-based-biomass-estimation-for-winter-wheat-sugar-beet-and-potatoes-using-UAV-LiDAR.htm . Accessed 13 Nov 2019
Svensgaard J, Jensen SM, Westergaard JC, Nielsen J, Christensen S, Rasmussen J (2019) Can reproducible comparisons of cereal genotypes be generated in field experiments based on UAV imagery using RGB cameras? Eur J Agron 106:49–57. https://doi.org/10.1016/j.eja.2019.03.006
doi: 10.1016/j.eja.2019.03.006
Zhang Y, Gao J, Cen H, Lu Y, Yu X, He Y, Pieters JG (2019) Automated spectral feature extraction from hyperspectral images to differentiate weedy rice and barnyard grass from a rice crop. Comput Electron Agric 159:42–49. https://doi.org/10.1016/j.compag.2019.02.018
doi: 10.1016/j.compag.2019.02.018
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/TSMC.1979.4310076
doi: 10.1109/TSMC.1979.4310076
Haralick R, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621. https://doi.org/10.1109/TSMC.1973.4309314
doi: 10.1109/TSMC.1973.4309314
Gao J, Nuyttens D, Lootens P, He Y, Pieters JG (2018) Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosyst Eng 170:39–50. https://doi.org/10.1016/j.biosystemseng.2018.03.006
doi: 10.1016/j.biosystemseng.2018.03.006
Lowe A, Harrison N, French AP (2017) Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13. https://doi.org/10.1186/s13007-017-0233-z
Suh HK, Hofstee JW, IJsselmuiden J, van Henten EJ (2018) Sugar beet and volunteer potato classification using bag-of-visual-words model, scale-invariant feature transform, or speeded up robust feature descriptors and crop row information. Biosyst Eng 166:210–226. https://doi.org/10.1016/j.biosystemseng.2017.11.015
doi: 10.1016/j.biosystemseng.2017.11.015
Scharr H, Pridmore T, Tsaftaris SA (2017) Computer vision problems in plant phenotyping, CVPPP 2017: introduction to the CVPPP 2017 workshop papers. In: Proc. 2017 IEEE int. conf. comput. vis. work (ICCVW 2017). https://doi.org/10.1109/ICCVW.2017.236