An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments
- 1Research Scholar, School of Computer Science & Engineering, Vellore Institute of Technology (VIT) Chennai
- 2Faculty, School of Computer Science & Engineering, Vellore Institute of Technology (VIT) Chennai
Abstract
Introduction: The recognition of leaf segments and species in nature is a difficult process due to the changes in brightness, too much clutter in the background, and the overlapping of leaves due to which the accuracy falls down for 20-40% when using laboratory scans instead of field-ready applications.
Methods: This research presents a unified framework of deep learning that includes Mask R-CNN -based instance segmentation and deep feature fusion in order to overcome this problem. The process of extracting a number of leaves took place through Detectron2 and the excessive stream extractor that included ResNet-50 and VGG-16 used in conjunction with other descriptive methods allowed providing a joint representation of 6,148 dimensions. The classifier was trained according to three methods: Baseline, PSO (particle swarm optimization), and Grid search.
Results: The masked fusion system cut off the noise in the background while retaining the important botanical features with the help of achieving the mean Intersection-over-Union equal to 0.752, detection precision of 98.39%, and [email protected] of 0.8291. The ResNet-50 model enhanced with PSO techniques has the best performance among other technologies based on ResNet-50 (90% accuracy, F1 score 0.9002), while the technology of Grid search revealed a little higher result for the special techniques such as ResNet-50 plus traditional features (F1 score = 0.9135) which means that the efficiency of the methodology is contingent to the type of the features not the fact if the technology is better than the other.
Discussion: Comparative and ablation data analysis proved the efficiency of the instance segmentation used in combination with multi-mode fusion especially when used in occlusion-prone conditions. The system offers a scalable application for smart agriculture and biodiversity monitoring while being a good example of bridging the gap in automated environmental intelligence.
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BibTeX
@article{arunkumar2026integrated,
title = {{An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments}},
author = {K. Arunkumar and S. Leninisha},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1893779},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1893779/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An integrated instance segmentation and deep feature fusion model for robust leaf species classification in natural environments
AU - K. Arunkumar
AU - S. Leninisha
PY - 2026
DA - 2026/10/15
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1893779
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1893779/full
AB - Introduction: The recognition of leaf segments and species in nature is a difficult process due to the changes in brightness, too much clutter in the background, and the overlapping of leaves due to which the accuracy falls down for 20-40% when using laboratory scans instead of field-ready applications. Methods: This research presents a unified framework of deep learning that includes Mask R-CNN -based instance segmentation and deep feature fusion in order to overcome this problem. The process of extracting a number of leaves took place through Detectron2 and the excessive stream extractor that included ResNet-50 and VGG-16 used in conjunction with other descriptive methods allowed providing a joint representation of 6,148 dimensions. The classifier was trained according to three methods: Baseline, PSO (particle swarm optimization), and Grid search. Results: The masked fusion system cut off the noise in the background while retaining the important botanical features with the help of achieving the mean Intersection-over-Union equal to 0.752, detection precision of 98.39%, and [email protected] of 0.8291. The ResNet-50 model enhanced with PSO techniques has the best performance among other technologies based on ResNet-50 (90% accuracy, F1 score 0.9002), while the technology of Grid search revealed a little higher result for the special techniques such as ResNet-50 plus traditional features (F1 score = 0.9135) which means that the efficiency of the methodology is contingent to the type of the features not the fact if the technology is better than the other. Discussion: Comparative and ablation data analysis proved the efficiency of the instance segmentation used in combination with multi-mode fusion especially when used in occlusion-prone conditions. The system offers a scalable application for smart agriculture and biodiversity monitoring while being a good example of bridging the gap in automated environmental intelligence.
SN - 1664-462X
ER -