Abstract
In agriculture, plant phenotyping serves as a critical process for assessing a wide range of plant traits that are pivotal for crop management and improvement, such as plant height, leaf area, flowering time, and resistance to diseases. Traditional methods employed in plant phenotyping often encounter significant challenges, including time-consuming procedures, inefficiencies in data processing, and vulnerabilities to environmental variability. These limitations hinder the accuracy and scalability required for effective agricultural practices. To address these issues, this paper introduces the Hybrid U Mask Regional Convolutional Pelican Search (HUMRC-PS) method as an innovative approach in plant phenotyping. This method leverages advanced technologies to overcome the shortcomings of conventional techniques. Specifically, HUMRC-PS utilizes mask Region-based Convolutional Neural Networks (RCNN), a sophisticated deep learning architecture, to accurately segment plant areas within images. By precisely delineating plant features from complex backgrounds, RCNN enables a focused analysis of key attributes such as leaf morphology, color variations, and overall size. Furthermore, the integration of U-net, another deep learning framework, enhances the method’s capacity to capture both local and global features from plant images. This capability is crucial for comprehensive trait measurement and analysis, as it ensures that nuanced details and broader characteristics are accounted for in the phenotyping process. Moreover, HUMRC-PS incorporates Pelican optimization with a crossover strategy to fine-tune its internal parameters and optimize model performance. This approach not only enhances the accuracy and efficiency of plant trait identification but also contributes to the method’s adaptability across different agricultural scenarios and environmental conditions. Through rigorous experimentation on an image dataset tailored for plant phenotyping, the study validates the effectiveness of HUMRC-PS. It evaluates the method’s performance using a spectrum of evaluation metrics, demonstrating superior results with 98.76% accuracy when compared to existing methodologies. This validation underscores HUMRC-PS’s potential to significantly advance plant phenotyping practices by providing more precise, efficient, and scalable solutions. By surpassing the limitations of traditional approaches, HUMRC-PS offers promising opportunities for improving agricultural productivity, disease management, and breeding programs through enhanced understanding and characterization of plant traits.
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Kumar, P., Senthilselvi, A., Manju, I. et al. HUMRC-PS: Revolutionizing plant phenotyping through Regional Convolutional Neural Networks and Pelican Search Optimization. Evolving Systems 15, 2211–2230 (2024). https://doi.org/10.1007/s12530-024-09612-6
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DOI: https://doi.org/10.1007/s12530-024-09612-6