Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion

Chen, Jiqing; Wang, Huabin; Zhang, Hongdu; Luo, Tiejian; Depeng, Wei; Li, Teng; Wang, Zhikui

doi:10.1016/j.compag.2022.107412

Cited by 44 publications

(18 citation statements)

References 17 publications

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“…After the video of the drone is captured by the camera, the target detector is used to determine whether it is the drone target. Then the target tracker is used to predict the trajectory of the drone target, track and warn it, and complete the monitoring of the drone in a specific area [10]. This scheme greatly improves the monitoring accuracy and avoids the waste of unnecessary computing power in tracking other targets.…”

Section: System Modelmentioning

confidence: 99%

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

et al. 2022

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UAV technology is a basic technology aiming to help realize smart living and the construction of smart cities. Its vigorous development in recent years has also increased the presence of unmanned aerial vehicles (UAVs) in people’s lives, and it has been increasingly used in logistics, transportation, photography and other fields. However, the rise in the number of drones has also put pressure on city regulation. Using traditional methods to monitor small objects flying slowly at low altitudes would be costly and ineffective. This study proposed a real-time UAV tracking scheme that uses the 5G network to transmit UAV monitoring images to the cloud and adopted a machine learning algorithm to detect and track multiple targets. Aiming at the difficulties in UAV detection and tracking, we optimized the network structure of the target detector yolo4 (You Only Look Once V4) and improved the target tracker DeepSORT, adopting the detection-tracking mode. In order to verify the reliability of the algorithm, we built a data set containing 3200 pictures of four UAVs in different environments, conducted training and testing on the model, and achieved 94.35% tracking accuracy and 69FPS detection speed under the GPU environment. The model was then deployed on ZCU104 to prove the feasibility of the scheme.

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Section: System Modelmentioning

confidence: 99%

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

et al. 2022

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“…Te YOLO [20] and SSD series are frst-stage target detection algorithms. SSD network can efectively learn the RPN idea of Fast RCNN.…”

Section: Introductionmentioning

confidence: 99%

Research on a Target Detection Method for Alternanthera Philoxeroides in the Rice Seedling Stage Based on Single-Shot Multibox Detector

Deng

Liu

Gong

et al. 2023

Scientific Programming

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Alternanthera philoxeroides, an invasive alien malignant weed, competes with rice for water, fertilizer, light, and growth space before seedling closure stages, which commonly stresses the growth of rice. Chemical herbicides are mainly used to control weeds. However, excessive use of chemical herbicides could lead to serious environmental pollution. With the rapid development of artificial intelligence and deep-learning techniques, it is possible to reduce the use of chemical herbicides by spraying herbicides on only precise regions of weeds in rice fields. To improve the accuracy of the model in identifying weed regions, in this study the performance among the you only look once (YOLO) series and single-shot multibox detector (SSD) one-stage target detection models, i.e., YOLOv3, YOLOv4-tiny, YOLOv5-s, and SSD target detection networks, is discussed. The experimental results showed that the SSD-based target detection model for Alternanthera philoxeroides was better than the YOLO series due to its higher recall, mAP (mean average precision), and F1 values, which reached 0.874, 0.942, and 0.881, respectively. Meanwhile, the target detection model based on SSD performed better than the YOLO series when dealing with mutual occlusion images between seedlings and Alternanthera philoxeroides. In conclusion, in this study a high-accuracy method is provided for detecting precise regions of Alternanthera philoxeroides by constructing a model based on SSD, contributing to the reduction of environmental pollution.

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“…YOLO ("You Only Look Once") algorithm, known for its real-time object detection capabilities, has been widely used in weed recognition tasks, showing success across various applications and different weed species. [7][8][9][10][11][12] Multispectral imaging consists of capturing images of the same scene using different wavelengths. It has been widely used for remote sensing for productivity areas mapping, 13 weed mapping 14 plant density 15 and plant disease detection and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Weed detection among soybean plants in artificial lighting environment using multispectral images and computer vision

Sarreta Oda,

Orlandi de Oliveira,

De Paula

et al. 2023

SPIE-CLP Conference on Advanced Photonics 2023

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Precision Agriculture stands out as one of the most promising areas for the development of new technologies around the world. Some advances from this area include the mapping of productivity areas and the development of sensors for climate and soil analysis, improving the smart use of resources during crop management and helping farmers during the decision-making stages. Among the problems of modern agriculture, the intensive and non-localized use of herbicides causes environmental issues, contributes to elevated costs in farmers' budgets and results in applications of chemical substances in non-target organisms. Although there are many selective herbicide spraying systems available for use, the majority working principle is based upon chlorophyll detectors, thus not being able to distinguish crop plants from weeds with high accuracy in crop's post-emergence herbicide applications ("green-on-green" application). The main objective of this study is to develop a multispectral camera system for in-crop weed recognition using Computer Vision techniques. The system was built with four monochromatic CMOS sensor cameras with monochromatic wavelength bandpass filters (green, red, near infrared and infrared) and a RGB camera. Soybean and weed plants images were captured in a controlled environment using an automated v-slot rail system to simulate the movement of a spray tractor in the field. Infrared images presented higher precision (90.5%) and recall (89.3%) values compared to the other monochromatic bands, followed by RGB (87.0% and 86.1%, respectively) and near infrared images (83.6% and 87.9%), suggesting that infrared wavelengths plays an important role in plant detection and classification. Our results state that the combination of Computer Vision and multispectral images of plants is a more efficient approach for targeting weeds among crop plants for post-emergence herbicide applications.

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Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion

Cited by 44 publications

References 17 publications

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

Research on a Target Detection Method for Alternanthera Philoxeroides in the Rice Seedling Stage Based on Single-Shot Multibox Detector

Weed detection among soybean plants in artificial lighting environment using multispectral images and computer vision

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