Insight Analysis of Deep Learning and a Conventional Standardized Evaluation System for Assessing Rice Crop's Susceptibility to Salt Stress during the Seedling Stage

Author:

Shiragudikar Sharada K.,Bharamagoudar Geeta,Manohara K. K.,Malati S. Y.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference15 articles.

1. Mandal AK, Sharma RC, Singh G, Dagar JC 2006 Computerized Database On Salt Affected Soil In India. Technical Bulletin No. CSSRI/Karnal/2/2010.

2. BappaDas KK, Manohara GRM, Sahoo RN. Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice. Spectrochim Acta Part A: Mol Biomol Spectrosc. 2018;229(2020):117983.

3. Anami BS, Malvade NN, Palaiah S. Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artif Intell Agricul. 2020;4:12–20.

4. Fageria NK. Role of soil organic matter in maintaining sustainability of cropping systems. Commun Soil Sci Plant Anal. 2012;43:2063–113.

5. Ismail AM, Horie T. Genomics, physiology, and molecular breeding approaches for improving salt tolerance. Annu Rev Plant Biol. 2017;68:405–34.

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