Soybean Disease Monitoring with Leaf Reflectance

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.

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

“…Disease distribution within a field is limited by the spatial distribution of the pathogen at the beginning of the growing season.Scouting for SDS foliar symptoms in the field is made difficult by the relatively late onset of visible foliar symptom expression, which often occurs after the soybean canopy has closed, and by the patchy distribution of SDS in soybean fields. Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be difficult when foliar symptoms are mild [16], because multiple factors can contribute to the biophysical and chemical changes that are associated with plant disease [17].…”

“…Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be difficult when foliar symptoms are mild [16], because multiple factors can contribute to the biophysical and chemical changes that are associated with plant disease [17]. Plants with severe foliar symptoms differ significantly in canopy color from healthy or slightly infected plants [18].…”

In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

“…In contrast, this SVI was previously suggested as a potential surrogate for crop disease under field conditions (Bajwa et al, 2017;Yu et al, 2018). This Index was developed at the leaf scale to maximize sensitivity to the ratio between carotenoid and chlorophyll a concentrations (Car/Chl a ratio), while minimizing the effect of leaf surface and mesophyll structure (Penuelas et al, 1995).…”

“…Our results prominently illustrate context-specificity of the relationship between spectral reflectance and disease. Firstly, variation on a specific date in potentially disease-sensitive spectral features, such as the SIPI (see Bajwa et al, 2017;Yu et al, 2018), is quickly overridden by variation caused by advancing phenology ( Figure 4C), illustrating the difficulty in defining thresholds or calibration curves. Secondly, unstable VIP values of single wavelengths in PLSDA models, systematic shifts in VIP patterns ( Figure 6) and limited model applicability over time, even for the plots contained in the training dataset (Figure 7), illustrate marked shortterm changes in the relationship between STB and spectral reflectance.…”

Linear discriminant analysis of structure within African eggplant ‘Shum’

“…In plants, LDA has been applied in various studies such as taxonomic and germplasm characterisation (Herklotz et al, 2017), phenotypic changes evaluation in plant species over time (Alberti et al, 2017) and crop diseases detection on remote sensing generated data (Bajwa et al, 2017).…”