Application of Spectrometer in Potato Late Blight Research

01 Background

  

Potato, a dual-purpose crop of food and vegetable use, ranks as the fourth-largest staple crop globally after Wheat, rice, and corn. Potato late blight, caused by Phytophthora infestans, is a devastating disease affecting stems, leaves, tubers, and even flowers and berries. Prevalent in central and northern China, this disease can lead to 20-40% yield loss under favorable climatic conditions and even total crop failure in severe cases.

 

Fig. 1 Potato Infected with Late Blight

Late Blight can occur throughout the potato growth cycle, making early detection and prediction critical for effective disease management. In recent years, spectroscopic techniques have gained prominence in agriculture pest and disease monitoring due to their non-destructive, rapid, and environmentally friendly nature.

By integrating spectral analysis with image processing and chemometrics, early detection of crop diseases has become feasible.

 

 

02 Principles

Peroxidase (POD):

An enzyme indicative of plant stress resistance, POD activity fluctuates during disease progression and correlates directly with infection severity.

 

Diffuse Reflectance Spectroscopy (DRS):

A non-invasive diagnostic technique that captures spectral signatures reflecting the scattering and absorption properties of biological tissues. By spatially separating illumination and detection, DRS probes subsurface layers of samples rather than surface reflection. This capability enables applications such as:

• Chlorophyll content measurement in plant leaves.

• Monitoring fungal infections in crops (e.g., peach quality decline).

• Biomedical diagnostics (e.g., detection of colon polyps and skin cancers).

 

 

03 Experimental Design

Samples

• In vitro potato leaves: Inoculated with P. infestans and cultivated under varying temperature/humidity conditions for 5 days.

• Same-leaf monitoring: Sequential spectral measurements of a single leaf at different disease stages under controlled conditions.

Fig. 2 Samples

(a) Cultured under varying conditions;

(b) Sequential monitoring of the same leaf (Day 1-5).

 

Setup

A spectrometer-based system (e.g., Optosky ATP2000 series, 300-1000 nm range) was employed. The probe comprised six illumination fibers surrounding a central collection fiber, minimizing surface reflections and enhancing subsurface signal acquisition.

 

Key Components:

• Broadband light source.

• Fiber optic probe pressed against the leaf surface.

• Spectrometer capturing light interactions (absorption and scattering) with the leaf.

 

Since the measured diffuse reflection spectrum is affected by the internal structure of the leaf and changes with the variation of POD enzyme activity, the severity of late blight in potatoes can be observed in this way.

 

 

Fig. 3 Measurement setup

 

Multi-Channel Spectrophotometer:

A four-channel optical path design enabled simultaneous multi-sample analysis.

Fig. 5 Four-Channel Spectrophotometer Schematic

 

04 Spectral Analysis

Diffuse reflectance spectra were acquired from potato leaves at different disease stages:

• Healthy: Pre-inoculation (blue curve).

• Early-stage: 1-4 days post-inoculation (green curve).

• Mid-stage: 5-9 days post-inoculation (orange curve).

• Late-stage: ≥10 days post-inoculation (purple curve; vivsible necrosis).

 

Fig. 6 Spectral Analysis

(a) Raw averaged spectra; (b,c) Normalized spectra under different conditions.

 

Observations:

1. Healthy vs. Infected: Significant spectral differences emerged as early as 24 hours post-inoculation, preceding visible symptoms.

2. Intensity Trends:

• Early infection: Reduced diffuse reflectance intensity

• Mid-stage: Increased intensity.

• Late-stage: Decline in intensity due to tissue necrosis.

 

 

05 Conclusion

Spectrometers enable rapid, non-destructive assessment of POD activity in potato leaves under varying environmental conditions. By correlating spectral changes with disease progression, this method offers:

 

• Early detection: Identification of infection 24 hours post-inoculation.

• Qualitative prediction: POD activity-based severity grading.

• Field applicability: Foundation for portable devices to support on-site disease monitoring.

 

Further optimization through large-scale sampling and advanced modeling (e.g., machine learning algorithms) will enhance prediction accuracy, paving the way for precision agriculture in potato cultivation.

 

 

References:

1. Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease，Applied Spectroscopy，2023, Vol. 77(5) 491–499，DOI: 10.1177/00037028231165342.

2. Hou B., et al. "Prediction of Potato Late Blight Severity Based on Fiber Optic Spectral Data of Leaves," Spectroscopy and Spectral Analysis, 2022, 42(5): 1426–1432. DOI: 10.3964/j.issn.1000-0593(2022)05-1426-07.