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Image Based High throughput Phenotyping for Fusarium Wilt Resistance in Pigeon Pea (Cajanus cajan)

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Abstract

In pigeonpea, resistance against vascular wilt disease was assessed based on leaf images captured throughred-green–blue (RGB) and chlorophyll fluorescence imaging sensors. At leaf level, wilt response in RGB images was characterized by changes in pixel intensities in red, green, and blue channels leading to variation in texture. Texture analysis based on gray level co-occurrence matrix (GLCM) was able to explain variation pattern between resistance and susceptible genotypes. Extracted texture features particularly contrast and energy were significantly different between the two genotype groups. Training of a neural network model for contrast and energy feature enabled genotype prediction with 79–98% accuracy. Healthy leaf area estimated based on photosynthetic or quantum efficiency (Fv/Fm > 0.75 as healthy) in chlorophyll fluorescence images, indicated significant variation (p < 0.05) between genotype groups at 10–25 days after inoculation (dpi). In susceptible genotype, healthy area was observed to decrease in significant proportion over time as compared to resistant type. Resistant genotype was less sensitive to infection as healthy leaf area (Fv/Fm > 0.75) remained unaffected between 10-25dpi.At canopy level, although differences in pixel intensity (Fv/Fm > 0.75) were noted between inoculated and healthy (mock) particularly in susceptible types but differences between inoculated susceptible and resistant type were non-significant (p > 0.05). Although trained ML algorithms for leaf and canopy level images resulted low accuracy (41–54%) in genotype classification but with large number of images captured later than 15 dpi expected to increase in accuracy. A protocol to facilitate non-invasive imaging techniques in association with machine learning tools is proposed over the tedious, time consuming and error-prone conventional screening method.

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Acknowledgements

The authors gratefully acknowledge Head and Professor, Division of Plant Pathology, Joint Director and Director, ICAR-Indian Agricultural Research Institute New Delhi for overall support.

Funding

This work was funded by the ICAR-Indian Agricultural Research Institute New Delhi under the Post-Graduate Research activities and National Agricultural Science Fund (NASF) funded NanajiDeshmukh Plant Phenomics Center, ICAR-IARI, New Delhi.

RKB acknowledges the RGNFD fellowship received from UGC, Govt of India, New Delhi; For the pursuance of PhD degree at Indian Agricultural Research Institute, New-Delhi-110012.

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Authors and Affiliations

  1. Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi, India

    Rudrappa K. Bannihatti, Parimal Sinha & R. Aggarwal

  2. Nanaji Deshmukh Plant Phenomics Center, New Delhi, India

    Dhandapani Raju & Sudhir Kumar

  3. Department of Information Technology, Kalyani Government Engineering College, Nadia, Kalyani, West Bengal, India

    Shubhajyoti Das & S. N. Mandal

  4. Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India

    R. S. Raje

  5. Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi, India

    C. Viswanathan

  6. ICAR-National Institute Plant Biotechnology, New Delhi, India

    K. Gaikwad

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Contributions

Conceptualization: PS and RKB; investigation: RKB, DR, SK and PS; writing—original draft preparation: PS, RKB, RD, SD and SNM; reviewing and editing: RKB, PS, DR, KG and RSR; machine learning- SD and SNM; supervision: RA, CV and PS.

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Correspondence to Rudrappa K. Bannihatti.

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Research involving Human Participants / Animals

Research work has been carried out in different pigeonpeagenotypesfor resistance phenotyping. There is no involvement of Humans participation or Animals present experimentaltrial purpose.

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12600_2022_993_MOESM1_ESM.docx

Supplementary file1 (DOCX 26 KB) Supplementary Fig. 1 Wilt severity index 20 (WSI20) of reference pigeonpea genotypes (susceptible – ICP2376 and Gulyal local; resistant- Asha and Maruti) in 30 days old seedlings (soil inoculation with F udum spore suspension 3.4 x104 /mL) maintained in glasshouse (28°±1.5°C).

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Bannihatti, R.K., Sinha, P., Raju, D. et al. Image Based High throughput Phenotyping for Fusarium Wilt Resistance in Pigeon Pea (Cajanus cajan). Phytoparasitica 50, 1075–1090 (2022). https://doi.org/10.1007/s12600-022-00993-5

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