Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10⁻⁴ (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.

The contradiction between economic development and ecological protection has become a common challenge for territorial governance in developing countries around the world. However, extant studies have neglected the coupling and symbiotic relationship between humans and nature, resulting in significant functional conflicts, insufficient stability, and imbalances in ecological and economic benefits in the reconstruction of territorial spatial functional pattern (TSFP), making it difficult to achieve synergies between development and protection. The question that arises is how the TSFP can be reconstructed in order to achieve harmonious coexistence between humans and nature. This remains a challenging problem in the context of the synergizing development and protection of the TSFP. This study innovatively integrates set-theoretic principles and functional mappings with game-theoretic analysis to develop Territorial Spatial Functional Pattern Reconstruction (TSFPR) model designed to foster harmonious human–nature coexistence, and validates the model using geospatial data from Qionglai City, China. Empirical evidence demonstrates that, in comparison with conventional methods, TSFPR model significantly mitigates the territorial spatial functional conflicts (TSFCs), enhances stability and ecological and economic benefits, and achieves the expected harmonious coexistence between humans and nature. The analysis confirms that the territorial spatial functional conflict (TSFC) coordination index established in this study provides a reliable criterion for identifying superior territorial spatial functions (TSFs). The proposed TSFPR model is an expansion of the theory of spatial optimization modelling, and it provides a tool for reconstructing the TSFP for the harmonious coexistence between humans and nature. In summary, the utilization of the TSFPR model to reconstruct the TSFP for harmonious coexistence between humans and nature provides a novel solution for coordinating the development and protection of territorial space governance.

Plant organ segmentation supports image-based phenotyping, but occlusion, slender structures, and differences between image domains limit performance. This study proposes MVA-PlantNet, an encoder–decoder network incorporating a Multi-view Attention (MVA) block. MVA combines Dynamic Group Depth Attention (DDA), which models channel and within-batch relationships, with Global Spatial Attention (GSA), which uses feature gating. The network was pretrained on synthetic images from the Sweet Pepper Dataset and fine-tuned on real Sweet Pepper images and a Capsicum Dataset containing 30 images, of which six were used for fine-tuning and 24 for validation. Additional fine-tuning experiments used 100 images from the Plant Organ Dataset. MVA-PlantNet achieved a reported mean intersection over union (mIoU) of 71.45% on synthetic Sweet Pepper images, exceeding U-Net and SegFormer by 9.60 and 24.91 percentage points, respectively. The reported mIoUs after fine-tuning were 60.17% on real Sweet Pepper images, 74.16% on the Capsicum Dataset, and 55.52% on the Plant Organ Dataset. MVA-PlantNet had the highest reported mIoU among the evaluated models on these splits. However, stem IoU on the Plant Organ Dataset remained low at 1.32%. The experimental results demonstrate that MVA-PlantNet achieved the best overall segmentation performance among the evaluated models. This showed advantages in segmenting certain small plant organs and adapting to target domains through fine-tuning, providing a feasible approach to addressing segmentation challenges arising from morphological differences among plant organs.

The contradiction between economic growth and carbon emissions restricts the sustainable development of developing countries. Whether territorial spatial functional conflicts (TSFCs) are statistically associated with the intensification of this tension remains an important empirical question that has not been fully addressed. This study comprehensively employs multiple spatiotemporal analysis methods to analyze the spatiotemporal association between TSFC intensity and the synergy state of economic growth and carbon emissions in Sichuan Province, China, over the period 2010–2022. The results show that the territorial spatial functional conflict index (TSFCI) level has significant explanatory power for the spatial heterogeneity in both the CCD and the decoupling elasticity index (DEI) between economic growth and carbon emissions, with q-values of 0.081 and 0.023, respectively, both passing the significance test at p < 0.001. The time-stratified geographical detector results further reveal that the explanatory power of the TSFCI for the DEI reaches significant levels across all four periods (2010–2013, 2013–2016, 2016–2019, and 2019–2022), with q-values of 0.254, 0.065, 0.118, and 0.030, respectively, exhibiting pronounced phased fluctuations. These findings demonstrate, from both spatial and temporal dimensions, a substantial association between TSFCs and the synergy between economic growth and carbon emissions, with the association characterized by phased fluctuations and nonlinear features. These results provide spatiotemporal empirical evidence for the close linkage between TSFCs and economic–carbon contradictions, while the causal inference that TSFCs exacerbate the contradiction is supported only by theoretical mechanism deduction rather than direct empirical proof. Further analysis suggests that the territorial spatial governance approach of alleviating the economic–carbon contradiction by coordinating TSFCs and optimizing territorial spatial development and conservation patterns is theoretically plausible. This study provides empirical reference and theoretical implications for developing countries to mitigate the contradiction between economic growth and carbon emissions by optimizing territorial spatial development and conservation patterns.

The contradiction between economic growth and carbon emissions restricts the sustainable development of developing countries. Whether territorial spatial functional conflicts (TSFCs) are statistically associated with the intensification of this tension remains an important empirical question that has not been fully addressed. This study comprehensively employs multiple spatiotemporal analysis methods to analyze the spatiotemporal association between TSFC intensity and the synergy state of economic growth and carbon emission in Sichuan Province, China, over the period 2010–2022. The results show that the territorial spatial functional conflict index (TSFCI) level has significant explanatory power for the spatial heterogeneity of both the CCD and the decoupling elasticity index (DEI) between economic growth and carbon emission, with q-values of 0.081 and 0.023, respectively, both passing the significance test at p < 0.001. The time-stratified geographical detector results further reveal that the explanatory power of TSFCI for DEI reaches significant levels across all four periods (2010–2013, 2013–2016, 2016–2019, and 2019–2022), with q-values of 0.254, 0.065, 0.118, and 0.030, respectively, exhibiting pronounced phased fluctuations. These findings demonstrate, from both spatial and temporal dimensions, a substantial association between TSFCs and the synergy between economic growth and carbon emission, with the association characterized by phased fluctuations and nonlinear features. These results provide spatiotemporal empirical evidence for the close linkage between TSFCs and economic-carbon contradictions, while the causal inference that TSFCs exacerbate the contradiction is supported only by theoretical mechanism deduction rather than direct empirical proof. Further analysis suggests that the territorial spatial governance approach of alleviating the economic-carbon contradiction by coordinating TSFCs and optimizing territorial spatial development and conservation patterns is theoretically plausible. This study provides empirical reference and theoretical implications for developing countries to mitigate the contradiction between economic growth and carbon emission by optimizing territorial spatial development and conservation patterns.