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Deep Learning-Based Forecasting of Invasive Plant Spread Across Regions

Deep Learning-Based Forecasting of Invasive Plant Spread Across Regions
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Author(s): Usharani Bhimavarapu (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India)
Copyright: 2026
Pages: 30
Source title: Impact of Invasive Grasses and Plants on Biodiversity
Source Author(s)/Editor(s): Sana Basharat (Hainan University, China)
DOI: 10.4018/979-8-3693-8724-5.ch011

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Abstract

This study explores the Geographic Variation in Growth Patterns of Invasive Plants by integrating ecological field data, advanced feature selection techniques, and deep learning methods. Invasive plant species pose serious threats to biodiversity and agriculture, especially when their growth patterns vary across regions due to environmental and human-influenced factors. Focusing on major spice-growing districts such as Guntur and Krishna, the dataset includes records of species like cinnamon, coriander, cumin, black pepper, turmeric, and chili, along with environmental attributes like soil composition, temperature, and rainfall. After rigorous preprocessing, Particle Swarm Optimization (PSO) was employed to extract the most relevant features influencing invasive species richness. A Gated Recurrent Unit (GRU) model was developed to predict the temporal and spatial growth patterns, capturing complex dependencies across multiple time steps.

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