Literature DB >> 30694129

Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping.

Anne-Katrin Mahlein1.   

Abstract

Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.

Entities:  

Year:  2016        PMID: 30694129     DOI: 10.1094/PDIS-03-15-0340-FE

Source DB:  PubMed          Journal:  Plant Dis        ISSN: 0191-2917            Impact factor:   4.438


  61 in total

1.  The persistent threat of emerging plant disease pandemics to global food security.

Authors:  Jean B Ristaino; Pamela K Anderson; Daniel P Bebber; Kate A Brauman; Nik J Cunniffe; Nina V Fedoroff; Cambria Finegold; Karen A Garrett; Christopher A Gilligan; Christopher M Jones; Michael D Martin; Graham K MacDonald; Patricia Neenan; Angela Records; David G Schmale; Laura Tateosian; Qingshan Wei
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-08       Impact factor: 11.205

Review 2.  Species-independent analytical tools for next-generation agriculture.

Authors:  Tedrick Thomas Salim Lew; Rajani Sarojam; In-Cheol Jang; Bong Soo Park; Naweed I Naqvi; Min Hao Wong; Gajendra P Singh; Rajeev J Ram; Oded Shoseyov; Kazuki Saito; Nam-Hai Chua; Michael S Strano
Journal:  Nat Plants       Date:  2020-11-30       Impact factor: 15.793

3.  A light-induced decrease in the photochemical reflectance index (PRI) can be used to estimate the energy-dependent component of non-photochemical quenching under heat stress and soil drought in pea, wheat, and pumpkin.

Authors:  Lyubov Yudina; Ekaterina Sukhova; Ekaterina Gromova; Vladimir Nerush; Vladimir Vodeneev; Vladimir Sukhov
Journal:  Photosynth Res       Date:  2020-02-10       Impact factor: 3.573

4.  Detection of Fusarium infected seeds of cereal plants by the fluorescence method.

Authors:  Alexey Dorokhov; Maksim Moskovskiy; Mikhail Belyakov; Alexander Lavrov; Victor Khamuev
Journal:  PLoS One       Date:  2022-07-01       Impact factor: 3.752

5.  Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris.

Authors:  Mónica Pineda; María Luisa Pérez-Bueno; Matilde Barón
Journal:  Front Plant Sci       Date:  2022-06-23       Impact factor: 6.627

Review 6.  Field-Effect Transistor-Based Biosensors for Environmental and Agricultural Monitoring.

Authors:  Giulia Elli; Saleh Hamed; Mattia Petrelli; Pietro Ibba; Manuela Ciocca; Paolo Lugli; Luisa Petti
Journal:  Sensors (Basel)       Date:  2022-05-31       Impact factor: 3.847

7.  ATR-FTIR spectroscopy non-destructively detects damage-induced sour rot infection in whole tomato fruit.

Authors:  Paul Skolik; Martin R McAinsh; Francis L Martin
Journal:  Planta       Date:  2018-11-28       Impact factor: 4.116

Review 8.  Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits.

Authors:  Md Sultan Mahmud; Azlan Zahid; Long He; Phillip Martin
Journal:  Sensors (Basel)       Date:  2021-05-08       Impact factor: 3.576

9.  Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Authors:  Mariela Fernández-Campos; Yu-Ting Huang; Mohammad R Jahanshahi; Tao Wang; Jian Jin; Darcy E P Telenko; Carlos Góngora-Canul; C D Cruz
Journal:  Front Plant Sci       Date:  2021-06-17       Impact factor: 5.753

Review 10.  Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology.

Authors:  Kuo-Szu Chiang; Clive H Bock
Journal:  Trop Plant Pathol       Date:  2021-07-13       Impact factor: 2.404

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