Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging

• Published in: Computers and electronics in agriculture 2024-12, Vol.227, p.109570, Article 109570
• Main Authors: Zhang, Panli, Zhang, Sheng, Wang, Jiquan, Sun, Xiaobo
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Rice lodging; Salp swarm algorithm; Semantic segmentation
• ISSN: 0168-1699
• DOI: 10.1016/j.compag.2024.109570

•Offered a cost-effective method for efficient extraction of crop phenotypic data.•Developed MI-UConvNext, optimized for detecting rice lodging across UAV datasets.•Introduced ISSA-LE to optimize convolutional channel numbers for efficient learning.•Demonstrated MI-UConvNext’s performance with data from two distinct UAV conditions. Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (PA) of 95.59%, mean Pixel Accuracy (mPA) of 95.62%, and mean Intersection over Union (mIoU) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.