Early detection of wilt in Cajanus cajan using satellite hyperspectral images: Development and validation of disease-specific spectral index with integrated methodology

• Published in: Computers and electronics in agriculture Vol. 219; p. 108784
• Main Authors: Dutta, Amitava, Tyagi, Rashi, Chattopadhyay, Anirudha, Chatterjee, Debtoru, Sarkar, Ankita, Lall, Brejesh, Sharma, Shilpi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: agriculture; Cajanus cajan; data collection; DESIS; disease detection; electronics; EnMAP; fungi; Fusarium udum; Fusarium wilt; health status; hyperspectral imagery; Hyperspectral remote sensing; India; mortality; nitrogen; pathogens; photochemistry; pigeon peas; PRISMA; rapid methods; reflectance; satellites; Spectral signature library; stakeholders; vascular wilt; vegetation index; Vegetation indices; India; DESIS; Fusarium wilt; Vegetation indices; PRISMA; Cajanus cajan; Spectral signature library; EnMAP; Hyperspectral remote sensing
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.108784

•Designed hyperspectral data-based novel wilt detection methodology for pigeonpea.•Developed Fusarium udum infected plant-specific spectral index for detection.•Regional scale, non-destructive, economic, precise & rapid plant disease detection.•Crop high-throughput phenotyping using hyperspectral imagery and machine learning. Pigeonpea (Cajanus cajan), a legume of nutritional significance, is highly prone to wilt disease caused by fungal pathogen, Fusarium udum, that leads to 15–30 % of crop mortality in India. While early detection of wilts in legume is crucial for remedial measures, it has been poorly addressed till date using traditional field based manual methods. The present study aimed to design an integrated two-step wilt detection methodology, and develop a disease-specific spectral index for Cajanus cajan exploiting spectral enrichment of ASI-PRISMA hyperspectral dataset. Initially, Modified Red Edge Normalized Difference Vegetation Index, Normalized Difference Nitrogen Index, and Photochemical Reflectance Index were combined for generation of relative agricultural stress map and in parallel, Minimum Noise Fraction transformation and Pixel Purity Index (PPI) based endmember maps/spectra were generated. Integration of high agricultural stress areas/pixels with PPI endmembers successfully established the desired spectrum for the diseased Cajanus cajan plants. Subsequently, the novel two-step methodology was validated through ground truthing. In addition, a plant (C. cajan)-specific normalised difference disease/stress index was developed for rapid assessment of C. cajan health status, after exhaustive search for band combinations and separability analysis. To assess the robustness of the proposed two-step methodology and spectral index for disease detection in Cajanus cajan, another site was investigated. A total of seven DLR DESIS and EnMAP, and ASI-PRISMA hyperspectral images were exploited using the proposed methodology for wilt detection in C. cajan. It was established from the field experiments that hyperspectral imaging could efficiently detect the wilted C. cajan plants in the area. In conclusion, using spaceborne hyperspectral images, developed disease spectral index values of ≤0.55 and agricultural stress values ≥ 3 could jointly detect the wilt at an early stage in C. cajan. When compared with commonly used multispectral satellite imageries, the developed methodology for hyperspectral imagery based signature analysis could efficiently detect the diseased Cajanus cajan plants at least 2–3 weeks in advance. This is the first report on employing satellite hyperspectral imagery for the detection of the wilt in C. cajan. The field deployment of hyperspectral imaging based precise foreknowledge regarding the wilt in legumes would help the stakeholders to make more informed decisions for quick mitigation.