YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

IntroductionThe competition between intra-row weeds and cultivated vegetables for nutrients is a major contributor for crop yield reduction. Compared with manual weeding, intelligent robots can improve the efficiency of weeding operations.MethodsThis study proposed a novel mechanical-laser collaborative intra-row weeding device structure. A slider-crank mechanism size optimization algorithm was proposed, and the correctness of the algorithm was verified by ADMAS software. Finally, the crank and link lengths were determined to be 87 mm and 135 mm, respectively. The resistance of triangular weeding knives with different penetration angles and edge angles in the soil was simulated and analyzed using EDEM software. The simulation results show that the triangular weeding knife with a soil penetration angle of 0 ° and an edge angle of 30 ° encountered the least resistance. In addition, weed control experiments with different powers and lasers were conducted using 200 W NIR and 200 W blue lasers. The experimental results show that the time it took for a 50 W blue laser and a 100 W NIR laser to remove small weeds was approximately between 0.3 and 0.4 s, and the time it took for a 50 W blue laser to remove larger weeds was approximately between 0.5 and 0.6 s. The time it took for 75 W and 50 W NIR lasers to remove weeds was more than 1 s.ResultsBased on the above research results, a prototype of a mechanical-laser collaborative intra-row weeding device was successfully built.DiscussionThis study provides a new idea for the field of intelligent weeding. The simulation and experimental results can provide a reference for the research and development of mechanical weeding and laser weeding equipment.

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Algorithm for Locating Apical Meristematic Tissue of Weeds Based on YOLO Instance Segmentation

Zhang,

Lu,

Guo

et al. 2024

Agronomy

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Laser technology can be used to control weeds by irradiating the apical meristematic tissue (AMT) of weeds when they are still seedlings. Two factors are necessary for the successful large-scale implementation of this technique: the ability to accurately identify the apical meristematic tissue and the effectiveness of the localization algorithm used in the process. Based on this, this study proposes a lightweight weed AMT localization algorithm based on YOLO (look only once) instance segmentation. The YOLOv8n-seg network undergoes a lightweight design enhancement by integrating the FasterNet lightweight network as its backbone, resulting in the F-YOLOv8n-seg model. This modification effectively reduces the number of parameters and computational demands during the convolution process, thereby achieving a more efficient model. Subsequently, F-YOLOv8n-seg is combined with the connected domain analysis algorithm (CDA), yielding the F-YOLOv8n-seg-CDA model. This integration enables the precise localization of the AMT of weeds by calculating the center-of-mass coordinates of the connected domains. The experimental results indicate that the optimized model significantly outperforms the original model; the optimized model reduces floating-point computations by 26.7% and the model size by 38.2%. In particular, the floating-point calculation is decreased to 8.9 GFLOPs, and the model size is lowered to 4.2 MB. Comparing this improved model against YOLOv5s-seg and YOLOv10n-seg, it is lighter. Furthermore, it exhibits exceptional segmentation accuracy, with a 97.2% accuracy rate. Experimental tests conducted on five different weed species demonstrated that F-YOLOv8n-seg-CDA exhibits strong generalization capabilities. The combined accuracy of the algorithm for detecting these weeds was 81%. Notably, dicotyledonous weeds were detected with up to 94%. Additionally, the algorithm achieved an average inference speed of 82.9 frames per second. These results indicate that the algorithm is suitable for the real-time detection of apical meristematic tissues across multiple weed species. Furthermore, the experimental results demonstrated the impact of distinctive variations in weed morphology on identifying the location of the AMT of weeds. It was discovered that dicotyledonous and monocotyledonous weeds differed significantly in terms of the detection effect, with dicotyledonous weeds having significantly higher detection accuracy than monocotyledonous weeds. This discovery can offer novel insights and avenues for future investigation into the identification and location of the AMT of weeds.

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YOLOv7-Based Intelligent Weed Detection and Laser Weeding System Research: Targeting Veronica didyma in Winter Rapeseed Fields

Cited by 3 publications

References 50 publications

Implementation of a Weed Detection System Based on YOLOv8-n

Implementation of a Weed Detection System Based on YOLOv8-n

A novel mechanical-laser collaborative intra-row weeding prototype: structural design and optimization, weeding knife simulation and laser weeding experiment

Algorithm for Locating Apical Meristematic Tissue of Weeds Based on YOLO Instance Segmentation

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