Hierarchical artificial immune system for crop stage classification

Senthilnath, J.; Omkar, S. N.; Mani, V.; Karnwal, Nitin

doi:10.1109/indcon.2011.6139339

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…1) Clonal Selection Theory: This theory explains how an immune response is mounted when a non-selfantigenic pattern is recognized by a B-cell [28] [18]. When an antigen is detected, those B-cells that best recognize, i.e.…”

Section: Artificial Immune Systemmentioning

confidence: 99%

“…Some of the widely used unsupervised techniques for crop type classification problem using satellite imagery are k-means [14], iterative self-organizing data analysis (ISODATA) [15], and self-organizing feature maps [16]. Further, many researchers have shown clustering problems can be analyzed efficiently using hierarchical methods [17] [18]. The hierarchical clustering for crop type classification using QuickBird image is the subject of this study.…”

Section: Introductionmentioning

confidence: 99%

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Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Senthilnath¹,

Karnwal²,

Teja³

2014

IJIGSP

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This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self-Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.

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Section: Artificial Immune Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Senthilnath¹,

Karnwal²,

Teja³

2014

IJIGSP

Self Cite

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“…Ben-Dor et al (2002) [2] and Tiwari et al (2015) [12] utilized hyperspectral images for mapping several soil properties, showcasing its effectiveness in soil characterization. Additionally, hyperspectral data has been instrumental in crop stage identification, as demonstrated by Senthilnath et al (2011) [10] , indicating its potential for crop monitoring and management.…”

Section: Introductionmentioning

confidence: 99%

Identification of optimal hyperspectral wavelength to discriminate maize and sorghum crops using AVIRIS-NG data in Guntur district, Andhra Pradesh, India

Tiwari,

Raghu

2024

Int. J. Res. Agron.

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In this study, we utilized AVIRIS-NG hyperspectral data captured during the Rabi season in February 2018 in a region of Guntur District, Andhra Pradesh, where Maize and Sorghum crops are extensively cultivated. Despite having parallel crop calendars and spectral similarities, distinguishing between Maize and Sorghum crops during similar vegetative growth stages poses a challenge. The high spectral resolution of AVIRIS-NG data allows for precise identification of subtle changes in the objects under study.The key finding of our research is the identification of sensitive spectral bands within specific wavelength regions that facilitate the differentiation of these two visually similar crops. Our results indicate that the spectral range from 1553 nm to 1749 nm of AVIRIS-NG data provides optimal discrimination between Sorghum and Maize crops, irrespective of their growth stages. Notably, the wavelengths at 1649 nm and 1654 nm emerge as particularly suitable for distinguishing between Maize and Sorghum crops, as indicated by statistical separability measures such as Wilks' Lambda and F-Value. At these wavelengths, we observed significant results with Wilks' Lambda values of 0.388 and 0.387, and F-Values of 39.29 and 39.46, respectively, further supporting their efficacy in crop discrimination.

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How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm

Vázquez

Sandoval

Ambrosio

2013

Cuckoo Search and Firefly Algorithm

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Hierarchical artificial immune system for crop stage classification

Cited by 8 publications

References 11 publications

Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Identification of optimal hyperspectral wavelength to discriminate maize and sorghum crops using AVIRIS-NG data in Guntur district, Andhra Pradesh, India

How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm

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