Soybean Disease Monitoring with Leaf Reflectance

Sreekala Bajwa,Johnny Mason,John Rupe

doi:10.3390/rs9020127

Abstract

Crop disease detection with remote sensing is a challenging area that can have significant economic and environmental impact on crop disease management. Spectroscopic remote sensing in the visible and near-infrared (NIR) region has the potential to detect crop changes due to diseases. Soybean cyst nematode (SCN) and sudden death syndrome (SDS) are two common soybean diseases that are extremely difficult to detect in the early stages under mild to moderate infestation levels. The objective of this research study was to relate leaf reflectance to disease conditions and to identify wavebands that best discriminated these crop diseases. A microplot experiment was conducted. Data collected included 800 leaf spectra, corresponding leaf chlorophyll content and disease rating of four soybean cultivars grown under different disease conditions. Disease conditions were created by introducing four disease treatments of control (no disease), SCN, SDS, and SCN+SDS. Crop data were collected on a weekly basis over a 10-week period, starting from 71 days after planting (DAP). The correlation between disease rating and selected vegetation indices (VI) were evaluated. Wavebands with the most disease discrimination capability were identified with stepwise linear discriminant analysis (LDA), logistic discriminant analysis (LgDA) and linear correlation analysis of pooled data. The identified band combinations were used to develop a classification function to identify plant disease condition. The best correlation (>0.8) between disease rating and VI occurred during 112 DAP. Both LDA and LgDA identified several bands in the NIR, red, green and blue regions as critical for disease discrimination. The discriminant models were able to detect over 80% of the healthy plants accurately under cross-validation but showed poor accuracy in discriminating individual diseases. A two-class discriminant model was able to identify 97% of the healthy plants and 58% of the infested plants as having some disease from the plant spectra.

Highlights

Plants react to biotic and abiotic stresses through biophysical and biochemical changes such as reduced biomass and chlorosis, which can be detected through remote sensing in the visible-NIR (VNIR) region [1,2,3]
Narrow band reflectance of soybean leaves with disease showed increased reflectance in the visible region and reduced reflectance in the near-infrared region compared to the leaves from healthy plants
Disease rating by visual observation indicated that most plants expressed some disease like symptoms by 126 days after planting (DAP) (Table 3)

Summary

Introduction

Plants react to biotic and abiotic stresses through biophysical and biochemical changes such as reduced biomass and chlorosis, which can be detected through remote sensing in the visible-NIR (VNIR) region [1,2,3]. Hyperspectral remote sensing can detect subtle changes in biophysical and biochemical characteristics of plant canopies caused by various stresses [4,5,6,7,8]. The SDS and SCN are two common diseases in soybeans that have shown some interaction Both of these diseases are caused by soil borne pathogens. SDS is difficult to detect in the early stages of infection because the disease starts from the lower leaves

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Results

Discussion

Conclusion