Detection of Rice Leaf SPAD and Blast Disease Using Integrated Aerial and Ground Multiscale Canopy Reflectance Spectroscopy

Aichen Wang, Zishan Song, Yuwen Xie, Jin Hu, Liyuan Zhang and Qingzhen Zhu ()
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Aichen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zishan Song: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yuwen Xie: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Jin Hu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Liyuan Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Qingzhen Zhu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2024, vol. 14, issue 9, 1-20

Abstract: Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient ( R c v 2 ) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields.

Keywords: multispectral imaging; canopy reflectance; blast disease; SPAD value; data fusion (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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