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Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning [article]

Junfeng Gao, Jesper Cairo Westergaard, Ea Hoegh Riis Sundmark, Merethe Bagge, Erland Liljeroth, Erik Alexandersson
2020 bioRxiv   pre-print
To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected ~500 field RGB images in  ...  This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery for crop resistance breeding in field environments  ...  We thank Mathieu Gremillet for field assistance, Hanne Grethe Kirk at Danespo for visual scoring of disease, and Linnea Almqvist from SLU for providing image  ... 
doi:10.1101/2020.08.27.263186 fatcat:tz35frgqmna2vne7zkicfsasxe

Autonomous Detection of Plant Disease Symptoms Directly from Aerial Imagery

Harvey Wu, Tyr Wiesner-Hanks, Ethan L. Stewart, Chad DeChant, Nicholas Kaczmar, Michael A. Gore, Rebecca J. Nelson, Hod Lipson
2019 tppj  
Core Ideas • A deep learning model identified plant disease in UAV images with 95% accuracy. • Transfer learning allowed for faster model optimization. • This method detected plant disease symptoms at  ...  Acknowledgments This work was supported by the US National Science Foundation National Robotics Initiative Grant no. 1527232 (M.A. Gore, R.J. Nelson, and H. Lipson).  ...  This is currently achieved by human experts visually estimating disease severity by eye late in the growing season, a method subject to high inter-and intra-rater variation (Poland and Nelson, 2011) .  ... 
doi:10.2135/tppj2019.03.0006 fatcat:gf66uq5vdjh23gpousjsr542ie

Resources for image-based high throughput phenotyping in crops and data sharing challenges

Monica F Danilevicz, Philipp E Bayer, Benjamin J Nestor, Mohammed Bennamoun, David Edwards
2021 Plant Physiology  
, which can be associated with environmental and genotypic data.  ...  In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement  ...  Nat Clim Chang 3: 955-957 861 Gašparović M, Zrinjski M, Barković Đ, Radočaj D (2020) An automatic method for 862 weed mapping in oat fields based on UAV imagery.  ... 
doi:10.1093/plphys/kiab301 pmid:34608963 pmcid:PMC8561249 fatcat:n3xbdcw635fc3oldmbcbvagp7a

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

Bhuwan Kashyap, Ratnesh Kumar
2021 Inventions  
This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops.  ...  Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/inventions6020029 doaj:bad904d96b2b4bc1852ecd689b5fe63d fatcat:k7qcrjbsfnb35kr3jpvnqckpai

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

Chenglong Zhang, João Valente, Lammert Kooistra, Leifeng Guo, Wensheng Wang
2021 Precision Agriculture  
Then up-to-date applications supported by UAVs in orchard management are described, focusing on the diversity of data-processing techniques, including monitoring efficiency and accuracy.  ...  With the goal of identifying the gaps and examining the opportunities for UAV-based orchard management, this study also discusses the performance of emerging technologies and compare similar research providing  ...  Acknowledgments This research was supported by Inner Mongolia Autonomous Region Major Sci-  ... 
doi:10.1007/s11119-021-09813-y fatcat:kq5gqg7vy5gzjemmuzthrqbrfm