LightTassel-YOLO: A Real-Time Detection Method for Maize Tassels Based on UAV Remote Sensing

doi:10.12133/j.smartag.SA202505021

Abstract

Abstract:

[Objective] The accurate identification of maize tassels is critical for the production of hybrid seed. Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity, insufficient feature extraction, high computational load, and low detection efficiency. To address these challenges, a real-time field maize tassel detection model, LightTassel-YOLO (You Only Look Once) based on an improved YOLOv11n is proposed. The model is designed to quickly and accurately identify maize tassels, enabling efficient operation of detasseling unmanned aerial vehicles (UAVs) and reducing the impact of manual intervention. [Methods] Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs, establishing a large-scale, high-quality maize tassel dataset that covered different maize tasseling stages, multiple varieties, varying altitudes, and diverse meteorological conditions. First, EfficientViT (Efficient vision transformer) was applied as the backbone network to enhance the ability to perceive information across multi-scale features. Second, the C2PSA-CPCA (Convolutional block with parallel spatial attention with channel prior convolutional attention) module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps, effectively enhancing the network's capability to extract target features while reducing computational complexity. Finally, the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction, thereby improving the model's detection accuracy. [Results and Discussions] The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection. The final model achieved an accuracy of 92.6%, a recall of 89.1%, and an AP@0.5 of 94.7%, representing improvements of 2.5, 3.8 and 4.0 percentage points over the baseline model YOLOv11n, respectively. The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs. In addition, LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN, SSD, and multiple versions of the YOLO series. The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios. [Conclusions] The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.

Key words: maize tassel detection, YOLOv11, EfficientViT, CPCA, SCConv, UAV

CLC Number:

S513

CAO Yuying, LIU Yinchuan, GAO Xinyue, JIA Yinjiang, DONG Shoutian. LightTassel-YOLO: A Real-Time Detection Method for Maize Tassels Based on UAV Remote Sensing[J]. Smart Agriculture, 2025, 7(6): 96-110.

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Year	Number of images
Year	Train	Validation	Total
2023	2 397	1 026	3 423

Parameters	Setup
Epochs	100
Batch size	16
Learning rate	0.01
Optimizer	AdamW
Number of workers	8
Image size	640 × 640

Model	P/%	R/%	AP@0.5/%	AP@0.5：0.95/%	Params/M	FPS	GFLOPs
YOLOv11n+ StarNet	91.1	86.8	93.5	54.3	2.64	179.5	5.2
YOLOv11n+ VanillaNet	90.9	85.7	93.1	53.3	3.69	101.2	6.2
YOLOv11n+MoblieNetV4	91.2	85.2	92.7	53.2	3.84	112.4	7.2
YOLOv11n+ ShuffleNetv2	91.3	85.8	93.4	54.6	2.48	123.3	5.9
YOLOv11n+ RepViT	91.1	87.6	93.9	55.5	6.16	131.2	17.7
YOLOv11n+ EfficientViT	91.5	87.9	93.8	56.2	3.56	163.9	6.9

Model	P/%	AP@0.5/%	AP@0.5：0.95/%	Params/M	FPS	GFLOPs
YOLOv11n+ EfficientViT_M0	91.5	93.8	56.2	3.56	163.9	6.9
YOLOv11n+ EfficientViT_M1	92.0	94.3	55.8	4.37	152.8	12.6
YOLOv11n+ EfficientViT_M2	91.8	94.1	55.5	5.58	143.2	14.7
YOLOv11n+ EfficientViT_M3	90.8	94.2	55.7	8.27	139.8	18.4
YOLOv11n+ EfficientViT_M4	92.3	94.2	56.0	10.17	137.6	20.5
YOLOv11n+ EfficientViT_M5	91.4	94.3	56.5	13.85	122.4	33.0

Baseline model	Models	EfficientViT	C2PSA-CPCA	C3k2-SCConv	P/%	R/%	AP@0.5/%	Params/M	GFLOPs	FPS
YOLOv11n	Model 1	×	×	×	90.1	85.3	90.7	2.43	6.3	124.9
	Model 2	√	×	×	91.5	87.9	93.8	3.56	6.9	163.9
	Model 3	×	√	×	90.8	86.7	93.3	2.43	6.3	239.2
	Model 4	×	×	√	90.7	86.8	93.2	2.11	5.4	226.3
	Model 5	√	√	×	92.2	87.9	94.1	3.56	6.9	238.5
	Model 6	√	×	√	92.1	88.4	94.4	3.15	6.7	190.8
	Model 7	×	√	√	91.8	87.6	93.9	2.26	5.4	285.6
	LightTassel-YOLO	√	√	√	92.6	89.1	94.7	3.23	6.7	226.9