Assessment of energy cane bagasse-derived cellulosic microfiber hydrogels on the growth of potted chili peppers.

Energy cane (Saccharum spp.) bagasse, a type of biomass waste, is often underutilized, burned, or left to dispose of itself. This research aimed to evaluate the potential of converting this bagasse into high-value cellulosic microfiber hydrogels (CMH) for water conservation and potted chili (Capsicum annuum) plant growth. CMH offers a biodegradable alternative to synthetic polyacrylamide (PA) hydrogels and provides the dual benefit of improved water use efficiency and reduced environmental impact due to their ability to naturally break down in the soil. In this study, CMH and PA hydrogels were compared for water retention value (WRV), and reswelling kinetics (RK), as well as their effects on plant height, leaf count, root-to-shoot ratios (R:S ratio), and soil moisture retention. Two versions of CMH, CMH65 and CMH60, were prepared with varying cellulose-chitosan ratios: 65:35 and 60:40, respectively. The hydrogels were tested at four concentrations (0, 0.5, 1.0, and 2.0% w/w) by being mixed in Promix® soil. Observations were recorded over a 16-day period without additional water. Also, the WRV of hydrogels at 240 min and RK (10-180 min) were compared over three swelling-deswelling cycles. The PA hydrogel exhibited higher WRV (exceeding 450%) compared to CMH (45%). However, PA led to reduced plant height, leaf count, and R:S ratio when compared to higher concentrations of CMH65 and CMH60. In general, CMH60 (0.5% and 2%) exhibited superior plant growth. All hydrogels exhibited a significant decrease (p < 0.05) in WRV across successive cycles. Notably, during cycle 2, both CMH65 and CMH60 peaked in WRV at 10 and 20 min, respectively, compared to cycle 1. This study demonstrates the potential of bagasse-derived hydrogels as a value-added product for water conservation and crop growth.

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models.

This study was conducted to determine if artificial neural networks (ANN) can be used to accurately predict in vitro organogenesis of Bacopa monnieri compared with statistical regression. Prediction models were developed for shoot and root organogenesis (outputs) on two culture media (Murashige and Skoog and Gamborg B5) affected by two explant types (leaf and node) and two cytokinins (6-Benzylaminopurine and Thidiazuron at 1.0, 5.0, and 10.0 µM levels) with and without the addition of auxin (1-Naphthaleneacetic acid 0.1 µM) (inputs). Categorical data were encoded in numeric form using one-hot encoding technique. Backpropagation (BP) and Kalman filter (KF) learning algorithms were used to develop nonparametric models between inputs and outputs. Correlations between predicted and observed outputs (validation dataset) were similar in both ANN-BP (R values = 0.77, 0.71, 0.68, and 0.48), and ANN-KF (R values = 0.79, 0.68, 0.75, and 0.49), and were higher than regression (R values = 0.13, 0.48, 0.39, and 0.37) models for shoots and roots from leaf and node explants, respectively. Because ANN models have the ability to interpolate from unseen data, they could be used as an effective tool in accurately predicting the in vitro growth kinetics of Bacopa cultures.