By leveraging advanced convolutional neural network (CNN) architecture and intelligent optimization algorithms, this model significantly surpasses conventional techniques, offering enhanced accuracy and reduced computational costs.
Rice lodging, the bending or falling of crops caused by environmental factors like wind or rain, poses a substantial threat to crop productivity. It hinders photosynthesis, complicates harvesting, and increases vulnerability to pests, making it crucial for farmers and researchers to monitor and predict lodging effectively. Traditional methods, including visual inspection, mathematical modeling, and satellite remote sensing, are often labor-intensive and imprecise, lacking the scalability and immediacy required for large-scale agricultural assessment.
A study (DOI: 10.34133/plantphenomics.0182) published in Plant Phenomics on 25 April 2024, can guide timely remedial actions, such as adjusting irrigation or pest control strategies, to mitigate potential yield losses.
The AAUConvNeXt model, developed through multi-objective optimization using the AFOA-APM algorithm, offers an enhanced version of the UConvNeXt CNN architecture for segmenting rice lodging. The research method involved optimizing the number of channels in the model’s convolutional layers to improve performance and efficiency. Unlike the conventional approach where channels increase or decrease in a fixed pattern, the AAUConvNeXt model strategically adjusts channels, increasing them in layers that require high feature learning while reducing them in less critical layers to balance complexity and resource use. The results from extensive experiments highlight the superiority of AAUConvNeXt over existing models. The optimized architecture achieved a Pixel Accuracy (PA) of 96.3%, Mean Pixel Accuracy (MPA) of 96.3%, and a mean Intersection over Union (mIoU) of 93.2%, outperforming other models like DeepLabV3+ and HRNet. Additionally, AAUConvNeXt reduced parameter count and computational complexity by 8.66%, making it more resource-efficient. The model’s advanced feature extraction capabilities contributed to high segmentation accuracy, especially in distinguishing challenging rice lodging categories, including full, partial, and non-lodged states.Ablation studies confirmed that combining AFOA with APOM significantly improved segmentation metrics, with AAUConvNeXt outperforming its predecessors. Furthermore, targeted channel adjustments optimized model complexity, allowing efficient learning of both early-stage and refined features.
According to the study’s lead researcher, Dr. Xiaobo Sun, “By integrating deep learning with intelligent optimization, our model provides a powerful tool for efficient crop lodging monitoring. This advancement holds immense potential to transform rice farming practices by offering timely, reliable, and cost-effective solutions.”
The AAUConvNeXt model represents a significant advancement in agricultural technology, combining deep learning with intelligent optimization for efficient rice lodging monitoring. Its integration into farming practices could revolutionize crop management, offering a promising pathway to improved productivity and sustainability.
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References
DOI
Original Source URL
https://spj.science.org/doi/10.34133/plantphenomics.0182
Funding information
This work was supported by the National Key R&D Program of China (2023YFD2301602-1), the Basic Research Support Program for Excellent Young Teachers in Provincial Undergraduate Universities in Heilongjiang Province, the Heilongjiang Provincial key research and development program (2022ZX05B05), and the Heilongjiang Provincial Postdoctoral Science Foundation (LBH-Z22090).
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
