Gossypium Plant Health Detection from Hyperspectral Data using Various Disease Indices
Komal D. Patil1, K. V. Kale2
1Komal D. Patil*, M.Tech (CSE), Department of CS and IT, Aurangabad, India.
2K. V. Kale, Professor, Dept. CS and IT, Aurangabad, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7129-7135 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1512109119/2019©BEIESP | DOI: 10.35940/ijeat.A1512.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cotton is the world’s most prevalent beneficial non-food crop, producing revenue for over 250 million people globally and employing nearly 7% of all workers in developing nations. About half of all fabrics are produced with cotton. In such a case if the cotton plant gets affected due to disease can lead to economic and personal loss. These diseases may be one of the reasons that could significantly reduce the supply of cotton to the market, which result in a low agricultural economy. Faster and more accurate prediction of leaf diseases in crops could help to develop an early treatment technique while significantly reducing economic losses. The traditional monitoring system is time-consuming and expensive. In this paper, we have discussed hyperspectral sensor ASD FieldSpec4 which are less time consuming and non-destructive. Spectral Vegetation Indices (SVI) is strongly linked to the chemical composition of the plant leaf such as chlorophyll, nitrogen, carotenoid, and anthocyanin. The linear regression models were developed for the calculation of correlations between spectral indices and plant composition using MATLAB 2018.
Keywords: ASD FieldSpec4, Linear regression, Plant disease, Vegetation Indices.