Bacterial Leaf Blight Detection in Rice Crops Using Ground-Based Spectroradiometer Data and Multi-temporal Satellites Images

This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively

[1]  Sanjeev J. Koppal,et al.  Lambertian Reflectance , 2020, Computer Vision, A Reference Guide.

[2]  T. Musa,et al.  Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery , 2019, CATENA.

[3]  C. Peng,et al.  Land use/cover change in the Three Gorges Reservoir area, China: Reconciling the land use conflicts between development and protection , 2019, CATENA.

[4]  Heigang Xiong,et al.  Study on the Effect of Fractional Derivative on the Hyperspectral Data of Soil Organic Matter Content in Arid Region , 2019, Journal of Spectroscopy.

[5]  Yong He,et al.  Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape , 2018, Remote. Sens..

[6]  Simon Plank,et al.  The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas , 2018, Remote. Sens..

[7]  S. Mansor,et al.  Detection of BPH (brown planthopper) sheath blight in rice farming using multispectral remote sensing , 2016 .

[8]  C. Hongo,et al.  Development of Damage Assessment Method of Rice Crop for Agricultural Insurance Using Satellite Data , 2015 .

[9]  Vinay Kumar Dadhwal,et al.  Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India , 2015 .

[10]  Anne-Katrin Mahlein,et al.  Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases , 2012, Plant Methods.

[11]  Jie Cao,et al.  A Decision Tree Model for Meteorological Disasters Grade Evaluation of Flood , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[12]  John H. Prueger,et al.  Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices , 2010, Remote. Sens..

[13]  Jingfeng Huang,et al.  Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification , 2010, Journal of Zhejiang University SCIENCE B.

[14]  Gunter Menz,et al.  Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.

[15]  Armando Apan,et al.  Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression , 2006 .

[16]  Zhihao Qin,et al.  Detection of rice sheath blight for in-season disease management using multispectral remote sensing , 2005 .

[17]  E. Ford,et al.  Vegetation's red edge: a possible spectroscopic biosignature of extraterrestrial plants. , 2005, Astrobiology.

[18]  John B. Solie,et al.  Evaluation of Green, Red, and Near Infrared Bands for Predicting Winter Wheat Biomass, Nitrogen Uptake, and Final Grain Yield , 2005 .

[19]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[20]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[21]  Changsheng Li,et al.  Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields , 2002 .

[22]  Minghua Zhang,et al.  Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses , 2002 .

[23]  A. K. Skidmore,et al.  Derivation of the red edge index using the MERIS standard band setting , 2002 .

[24]  T. Kobayashi,et al.  Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. , 2001, Phytopathology.

[25]  A. Gitelson,et al.  Non‐destructive optical detection of pigment changes during leaf senescence and fruit ripening , 1999 .

[26]  F. Kogan.  Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data , 1995 .

[27]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[28]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[29]  S. Ustin,et al.  Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages , 2014 .

[30]  Kuo-Wei Chang,et al.  A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy , 2008 .

[31]  K. P. Singh,et al.  A remote sensing assessment of pest infestation on sorghum , 2007 .

[32]  William D. Philpot,et al.  Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation , 1999 .

[33]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .