Synthesizing Novel Biodegradable Hybrid Hydrogels for Sustainable Agriculture

Abstract

Water scarcity has become a critical global issue, affecting over 2 billion people worldwide, with significant repercussions for agriculture, especially in arid and semiarid regions. Agricultural productivity is heavily reliant on the availability of water, yet the escalating water shortages pose a major threat to food security. As the global population continues to grow, the pressure on freshwater resources intensifies, leading to inefficient water usage, diminished crop yields, and a rising need for innovative water management solutions. Existing hydrogels, while promising in terms of water retention, present several challenges that limit their effectiveness in large-scale agricultural applications. Many of these hydrogels are based on synthetic polymers, which can pose environmental risks due to their non-biodegradability. Moreover, these synthetic hydrogels are often costly to produce, making them inaccessible to small-scale farmers who are among the most affected by water shortages. The lack of biodegradability also raises concerns about soil contamination, as these materials may accumulate in the soil over time, potentially disrupting microbial ecosystems and soil health. Additionally, the development of hydrogels has often relied on trial-and-error approaches, which are resource-intensive and time-consuming, slowing down the process of producing more environmentally friendly and cost-effective solutions. My proposed intervention involves the development of a hybrid hydrogel formed from coffee husks, cotton seeds, and cashew nut seeds crosslinked with an organic acid. This biodegradable and costeffective hydrogel addresses the limitations of existing products by utilizing natural materials that are both abundant and renewable. To complement the experimental evaluations, machine learning (ML) models were developed to predict the water retention capacity of the hydrogels based on their chemical composition and environmental conditions. This data-driven approach aimed to expedite the optimization of hydrogel formulations by minimizing the need for extensive experimental testing.