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

Dutta, Amitava; Tyagi, Rashi; Chattopadhyay, Anirudha; Chatterjee, Debtoru; Sarkar, Ankita; Lall, Brejesh; Sharma, Shilpi

doi:10.1016/j.compag.2024.108784

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…When the sample size is sufficient and relatively balanced, theoretically, a more accurate identification of a greater range of colors can be achieved by selecting more calibrated colors. Hyperspectral analysis is a key technology and method for achieving high-throughput analysis of crops [1,58,[62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Zou,

Zhu,

Wang

et al. 2024

Agronomy

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Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.

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Section: Discussionmentioning

confidence: 99%

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Zou,

Zhu,

Wang

et al. 2024

Agronomy

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Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral responses

Liu,

Chen,

Xiang

et al. 2024

European Journal of Agronomy

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Hyperspectral Reflectance Assessment for Preliminary Identification of Degraded Soil Zones in Industrial Sites, India

Dutta,

Tyagi,

Sharma

et al. 2024

photogramm eng remote sensing

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The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface‐soil contamination and degradation by exploiting various spectral indices and signature‐matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India.

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

Cited by 3 publications

References 35 publications

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral responses

Hyperspectral Reflectance Assessment for Preliminary Identification of Degraded Soil Zones in Industrial Sites, India

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