Literature DB >> 18943352

Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners.

T Kobayashi, E Kanda, K Kitada, K Ishiguro, Y Torigoe.   

Abstract

ABSTRACT Rice reflectance was measured to determine the spectral regions most sensitive to panicle blast infection. Reflectance increased in the 430- to 530-, 580- to 680-, and 1,480- to 2,000-nm regions at the dough stage both in the laboratory and the field as the percentage of diseased spikelets increased. The wavebands of the greatest sensitivity were in the visible region, located near 485 and 675 nm. After the yellow-ripe growth stage, near-infrared rather than visible reflectance responded to panicle blast infections. Ratios of rice reflectance were evaluated as indicators of panicle blast. R470/R570 (reflectance at 470 nm divided by reflectance at 570 nm), R520/R675, and R570/R675 decreased significantly as the incidence of panicle blast increased at the dough stage. At the yellow-ripe stage, R550/R970 and R725/R900 were used to estimate panicle blast severity as measured in terms of the percentage of diseased spikelets. According to the simulation that uses ground-based sensor data, airborne multispectral scanners may be effective in detecting the occurrence of panicle blast using a band combination of 530- to 570- and 650- to 700-nm regions at the dough stage.

Entities:  

Year:  2001        PMID: 18943352     DOI: 10.1094/PHYTO.2001.91.3.316

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  8 in total

1.  Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification.

Authors:  Zhan-yu Liu; Jing-jing Shi; Li-wen Zhang; Jing-feng Huang
Journal:  J Zhejiang Univ Sci B       Date:  2010-01       Impact factor: 3.066

2.  Identification of novel resources for panicle blast resistance from wild rice accessions and mutants of cv. Nagina 22 by syringe inoculation under field conditions.

Authors:  Vishesh Kumar; Pankaj K Singh; Suhas Gorakh Karkute; Mohd Tasleem; Someshwar Bhagat; M Z Abdin; Amitha Mithra Sevanthi; Anil Rai; Tilak Raj Sharma; Nagendra K Singh; Amolkumar U Solanke
Journal:  3 Biotech       Date:  2022-01-31       Impact factor: 2.406

3.  Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression.

Authors:  Zhan-yu Liu; Jing-feng Huang; Jing-jing Shi; Rong-xiang Tao; Wan Zhou; Li-Li Zhang
Journal:  J Zhejiang Univ Sci B       Date:  2007-10       Impact factor: 3.066

Review 4.  Turfgrass Disease Diagnosis: Past, Present, and Future.

Authors:  Tammy Stackhouse; Alfredo D Martinez-Espinoza; Md Emran Ali
Journal:  Plants (Basel)       Date:  2020-11-11

5.  Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures.

Authors:  Irfan Abbas; Jizhan Liu; Muhammad Amin; Aqil Tariq; Mazhar Hussain Tunio
Journal:  Plants (Basel)       Date:  2021-12-01

6.  Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Authors:  Dongxue Zhao; Shuai Feng; Yingli Cao; Fenghua Yu; Qiang Guan; Jinpeng Li; Guosheng Zhang; Tongyu Xu
Journal:  Front Plant Sci       Date:  2022-05-06       Impact factor: 5.753

Review 7.  Current and Prospective Methods for Plant Disease Detection.

Authors:  Yi Fang; Ramaraja P Ramasamy
Journal:  Biosensors (Basel)       Date:  2015-08-06

8.  New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery.

Authors:  Qiong Zheng; Wenjiang Huang; Ximin Cui; Yue Shi; Linyi Liu
Journal:  Sensors (Basel)       Date:  2018-03-15       Impact factor: 3.576

  8 in total

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