Artificial neural network modelling for seedling regeneration in Gracilaria dura (Rhodophyta) under different physiochem
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ORIGINAL ARTICLE
Artificial neural network modelling for seedling regeneration in Gracilaria dura (Rhodophyta) under different physiochemical conditions M. Vignesh1 · Mudassar Anisoddin Kazi1 · Mangal S. Rathore1,2 · Monica Gajanan Kavale1,2 · Ramalingam Dineshkumar1,2 · Vaibhav A. Mantri1,2 Received: 29 June 2020 / Accepted: 1 October 2020 © Springer Nature B.V. 2020
Abstract Agarophytic seaweeds have assumed prominence recently due to the development of innovative products, different marketing strategies as well as attracting new entrepreneurs and investors. Several domestic species have emerged as key players aptly supporting regional agar trade. Gracilaria dura is one such example and its commercial farming has been adopted by local Indian fisherman for diversification of their livelihood. This necessitated the adequate and continuous supply of viable seeds for sustainability of farming and subsequent processing sector. We herein reported data centric approach by adopting combined artificial neural network (ANN) model, particle swarm optimization (PSO) as well as response surface methodology (RSM) to optimize salinity, temperature, media concentration and weight to volume ratio to derive an accurate regeneration strategy in clonal seedlings. ANN topology of 4-16-1 and the combination of tangent-sigmoidal transfer function for hidden layer and linear function for output layer was found to be optimal with maximum R-value of 0.991. On employing optimized ANN model as a fitness function with PSO tool, the optimal physiochemical factors were 27 ppt salinity, 25 °C, 2.19 g L−1 DAP and 303 ml media volume. Further, the results of ANN model were experimentally validated and 33.54 ± 6.36% regeneration was observed. The prediction error in optimum regeneration rate by the ANN-PSO and RSM were 1.25% and 13.75%, respectively. The study demonstrated the efficacy of combined ANN-PSO method in solving the nonlinearity of the system. Key message This manuscript for the first time used data centric approach by adopting artificial neuralnetworking (ANN) model and particle swarm optimization (PSO) algorithm in optimization of salinity, temperature, media concentration and weight to volume ratio to derive an accurate regeneration strategy for production of clonal seedlings in industrially important red alga Gracilaria dura. The out-come of the study would certainly help to augment and support the commercial seaweed farmers for making their livelihood lucrative and sustainable. Communicated by Ming-Tsair Chan. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11240-020-01943-x) contains supplementary material, which is available to authorized users. * Ramalingam Dineshkumar [email protected] * Vaibhav A. Mantri [email protected] 1
Division of Applied Phycology and Biotechnology, CSIRCentral Salt and Marine Chemicals Research Institute, Gijubhai Badheka Marg, Bhavnagar 364002, India
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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