MLP-YOLOv5: A Lightweight Multi-Scale Identification Model for Lotus Pods with Scale Variation

Lu, Ange; Liu, Jun; Cui, Hao; Ma, Lingzhi; Ma, Qiucheng

doi:10.3390/agriculture14010030

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The proposed model successfully detects and segments "ripe" and "overripe" lotus seedpods, whereas only overripe lotus seedpods were detected in reference [2]. Furthermore, references [28,29] realized the detection and segmentation of lotus seedpods with high mAP values, respectively. However, they did not distinguish the maturity stage of lotus seedpods.…”

Section: Discussionmentioning

confidence: 95%

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Ma,

Zhao,

Fan

et al. 2024

Agronomy

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Lotus seedpod maturity detection and segmentation in pond environments play a significant role in yield prediction and picking pose estimation for lotus seedpods. However, it is a great challenge to accurately detect and segment lotus seedpods due to insignificant phenotypic differences between the adjacent maturity, changing illumination, overlap, and occlusion of lotus seedpods. The existing research pays attention to lotus seedpod detection while ignoring maturity detection and segmentation problems. Therefore, a semantic segmentation dataset of lotus seedpods was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. Afterwards, an improved YOLOv8-seg model was proposed to detect and segment the maturity of lotus seedpods. In the model, the convolutional block attention module (CBAM) was embedded in the neck network to extract distinguished features of different maturity stages with negligible computation cost. Wise-Intersection over Union (WIoU) regression loss function was adopted to refine the regression inference bias and improve the bounding box prediction accuracy. The experimental results showed that the proposed YOLOv8-seg model provides an effective method for “ripe” and “overripe” lotus seedpod detection and instance segmentation, where the mean average precision of segmentation mask (mAPmask) reaches 97.4% and 98.6%, respectively. In addition, the improved YOLOv8-seg exhibits high robustness and adaptability to complex illumination in a challenging environment. Comparative experiments were conducted using the proposed YOLOv8-seg and other state-of-the-art instance segmentation methods. The results showed that the improved model is superior to the Mask R-CNN and YOLACT models, with recall, precision, mAPbox and mAPmask being 96.5%, 94.3%, 97.8%, and 98%, respectively. The average running time and weight size of the proposed model are 25.9 ms and 7.4 M, respectively. The proposed model obtained the highest mAP for lotus seedpod maturity detection and segmentation while maintaining an appropriate model size and speed. Furthermore, based on the obtained segmentation model, 3D visualization of the lotus pond scene is performed, and cloud point of lotus seedpods is generated, which provides a theoretical foundation for robot harvesting in the lotus pond.

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Section: Discussionmentioning

confidence: 95%

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Ma,

Zhao,

Fan

et al. 2024

Agronomy

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Design and Experiment of Cantilevered Lotus Seedpod Picking Device

Zeng,

Xue,

Tang

et al. 2024

Journal of Field Robotics

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Addressing the issue of the absence of relevant automated harvesting machinery for lotus seedpods, which currently relies solely on manual harvesting, this paper proposes a four‐degree‐of‐freedom cantilever‐type lotus seedpod harvesting device. This harvesting device comprises an electric mobile chassis, mobile mechanism, harvesting mechanism, transfer mechanism, image acquisition system, and control system, which make it suitable for harvesting lotus seedpod in standardized lotus fields. The cantilevered lotus seedpod picking device obtains the image of the lotus from the depth camera at an overlooking angle. The YOLOv5‐trained lotus‐bud recognition model is used to identify and locate the lotus seedpod. Through the calibration using active vision method, the conversion relationship between the ZED camera coordinates and the base coordinates of the picking device is calculated to realize lotus seedpod picking. The results of picking experiments conducted in different time periods show that the picking success rate is higher at night, and in the early morning and evening, with the picking success rates of 89.47%, 85.7%, and 85%, respectively, while the picking success rate at noon is only 42.86%. It can be seen that the ambient light has a great influence on the picking of the lotus seedpods, and it is reasonable to avoid the midday period with strong light for picking. The experimental results show that the clamping coupling method realizes the integration of automatic picking and conveying of lotus seedpods, improves the picking efficiency, and provides a new scheme for fruit picking.

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YOLOv5s-BiPCNeXt, a Lightweight Model for Detecting Disease in Eggplant Leaves

Xie,

Li,

Yang

et al. 2024

Plants

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Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient detection method suitable for disease detection in natural scenes. A lightweight detection model, YOLOv5s-BiPCNeXt, is proposed. This model utilizes the MobileNeXt backbone to reduce network parameters and computational complexity and includes a lightweight C3-BiPC neck module. Additionally, a multi-scale cross-spatial attention mechanism (EMA) is integrated into the neck network, and the nearest neighbor interpolation algorithm is replaced with the content-aware feature recombination operator (CARAFE), enhancing the model’s ability to perceive multidimensional information and extract multiscale disease features and improving the spatial resolution of the disease feature map. These improvements enhance the detection accuracy for eggplant leaves, effectively reducing missed and incorrect detections caused by complex backgrounds and improving the detection and localization of small lesions at the early stages of brown spot and powdery mildew diseases. Experimental results show that the YOLOv5s-BiPCNeXt model achieves an average precision (AP) of 94.9% for brown spot disease, 95.0% for powdery mildew, and 99.5% for healthy leaves. Deployed on a Jetson Orin Nano edge detection device, the model attains an average recognition speed of 26 FPS (Frame Per Second), meeting real-time requirements. Compared to other algorithms, YOLOv5s-BiPCNeXt demonstrates superior overall performance, accurately detecting plant diseases under natural conditions and offering valuable technical support for the prevention and treatment of eggplant leaf diseases.

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MLP-YOLOv5: A Lightweight Multi-Scale Identification Model for Lotus Pods with Scale Variation

Cited by 3 publications

References 38 publications

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Design and Experiment of Cantilevered Lotus Seedpod Picking Device

YOLOv5s-BiPCNeXt, a Lightweight Model for Detecting Disease in Eggplant Leaves

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