Lightweight Deep Learning Model for Weed Detection for IoT Devices

Farooq, Umar; Khanam, Tayyibah; Amtullah, Afeefa; Bou-Rabee, Mohammed A.; Tariq, Mohd

doi:10.1109/icefeet51821.2022.9847812

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Finally, the total loss (P Loss ), comprising object classification loss (LOC), confidence loss (LOF), and object prediction position loss (LOCI), is defined by (25).…”

Section: Row Detectionmentioning

confidence: 99%

“…Chen et al employed the YoLo-sesame model to identify weeds and crops in sesame fields, achieving an average precision rate (mAP) of 96.1% at a frame rate of 36.8 frames per second (fps) [24]. Umar Farooq et al introduced a lightweight deep learning model, Tiny-YOLOv4, for weed detection, addressing the trade-off between algorithmic cost-effectiveness and performance [25].…”

Section: Introductionmentioning

confidence: 99%

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Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach

Chang,

Chen

2023

Preprint

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This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

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“…Finally, the total loss (P Loss ), comprising object classification loss (LOC), confidence loss (LOF), and object prediction position loss (LOCI), is defined by (25).…”

Section: Row Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach

Chang,

Chen

2023

Preprint

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“…YOLOv4 has made a lot of improvements based on the previous single-stage algorithm, absorbing many excellent ideas, such as data augmentation, CSP structure, etc. The related algorithms also have a large number of applications in the industry [19,20]. And then YOLOv4 introduced YOLOv4-tiny optimized for edge-side devices has greatly improved the real-time performance of the model, but there is still room for optimization.…”

Section: Introductionmentioning

confidence: 99%

PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

et al. 2022

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With the rapid development of Industrial Internet of Things (IIoT) technology, video surveillance devices and video data in IIoT environments are massively growing and increasingly important. Deploying rapidly evolving deep learning-based object detection algorithms in IIoT can improve the efficiency of video data utilization and increase the automation and intelligence of the IIoT. Facing the transmission latency problem of massive video data, the algorithms are better deployed in edge devices. However, due to the large size and high computing power requirements of existing object detection algorithms, it is difficult to deploy them on edge devices with limited performance. Therefore, to solve this problem, we propose a smaller and less computationally intensive object detection algorithm, PG-YOLO. We replaced all the convolutional modules of YOLOV5 with low computational ghost modules and streamlined the backbone network to improve the network performance. Then, we propose a better pruning algorithm to compress the network, and finally improve the accuracy by distillation to obtain PG-YOLO. PG-YOLO is smaller and faster and can be deployed in edge devices. In addition, we conducted experimental validation on the SHWD dataset. The experimental results show that PG-YOLO can compress the volume of the model by 9 times compared with YOLOv5s, and the compressed detection accuracy reaches 0.934, with only 0.1% loss of accuracy. Also, the time required for inference is reduced by 32.7%, and the inference speed is improved by 10 FPS. Compared with other object detection algorithms, PG-YOLO also has advantages.INDEX TERMS deep learning, object detection, edge device, lightweight model, PG-YOLO.

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Impact of Internet of Things in Smart Agriculture

Siva,

Ponnusamy

2024

Lecture Notes in Networks and Systems

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Lightweight Deep Learning Model for Weed Detection for IoT Devices

Cited by 8 publications

References 13 publications

Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach

Robust Guidance and Precise Spraying of a Four-wheeled Agricultural Robot based on Deep Learning Approach

PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

Impact of Internet of Things in Smart Agriculture

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