Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques
Abstract
:1. Introduction
- (1)
- A content analysis method is provided to organize the literature and present the research process.
- (2)
- The key technology of intelligent laser robot in weed-control process is provided.
- (3)
- Challenges and open problems are analyzed from all angles. New trends in this research field and future directions are accurately identified, so as to share the grand vision of research in the field of intelligent laser robot weeding and greatly expand our cognitive horizons.
2. Content Analysis Method and Research Process Design
2.1. Sample Extraction
2.2. Content Analysis Coding
2.3. Research Steps
3. Weed-Control System
3.1. Perception Layer
3.1.1. USB Camera
3.1.2. Binocular Camera
3.2. Decision-Making Layer
3.3. Execution Layer
3.3.1. Laser Weeding Device—Working Principle
3.3.2. Laser Control Mechanism
3.3.3. Weeding Performance of Existing Laser Weeding Robots
3.3.4. Robot Body Structure
3.4. Weeding Process
3.4.1. Weed-Control Standards
3.4.2. Weed Target Detection
4. Deep Learning Algorithms
4.1. Introduction to Deep Learning Detection Algorithms
4.1.1. Convolutional Neural Network
4.1.2. Transformer-Based Convolutional Neural Network
4.1.3. YOLO Target-Detection Algorithm
4.2. Other Object-Detection Algorithms Related to Deep Learning
4.3. Object-Detection Algorithm Evaluation Method
4.3.1. Comparative Analysis of Popular Deep Learning Algorithms
4.3.2. Evaluation Metrics for Deep Learning Algorithm
Evaluation Metrics | Meaning | Application Scenarios |
---|---|---|
Accuracy | • Accuracy refers to the proportion of correct predictions made by the model among all samples, that is, the ratio of the number of correctly predicted samples to the total number of samples. | • Accuracy is a basic indicator for evaluating model performance, which measures the classification accuracy of the model on the overall data. |
Precision | • Precision refers to the proportion of samples that are actually positive among all samples predicted to be positive. | • Precision is mainly used to focus on the accuracy of the model in predicting the positive class. |
Recall | • Recall refers to the proportion of samples that are correctly predicted as positive by the model among all samples that are actually positive. | • Recall is used to measure the model’s ability to detect positive samples. |
mean Average Precision (mAP) | • mAP is a commonly used indicator in target-detection tasks. It is the average of the average precision (AP) of multiple categories. | • mAP is mainly used in target-detection tasks to evaluate the detection accuracy of the model for different target categories. |
Intersection over Union (IoU) | • IoU is a metric used to measure the degree of overlap between two bounding boxes. It is the ratio of the intersection to the union of the two bounding boxes. | • In object detection, IoU is usually used to determine the degree of match between the predicted bounding box and the true bounding box, thereby evaluating the positioning accuracy of the model. |
Frames Per Second (FPS) | • FPS means the number of frames processed per second | • Reflects the processing speed of the model. |
4.4. Key Issues to Be Addressed for Deep Learning Detection System
4.4.1. Small Object-Detection Problem
4.4.2. The Balance Between Speed and Accuracy
5. Applications of Deep Learning Algorithms in Weed Control
5.1. Application of YOLO-Based Target-Detection Algorithm in Weed Control
5.2. Application of Object-Detection Algorithm Based on Faster R-CNN in Weed Control
5.3. Application of Object-Detection Algorithm Based on Convolutional Neural Network in Weed Control
5.4. Application of Other Detection Algorithms in Weed Control
6. Future Trends
6.1. Weeding System Based on Multimodal Data Fusion
6.2. Intelligent Decision Making
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Step | Description | Methods |
---|---|---|
Perception Layer | Responsible for obtaining image data of fields and other areas to provide basic materials for subsequent weed identification. | Binocular Camera, USB camera, USB camera |
Decision Layer | By analyzing and processing the image data transmitted from the perception layer, the location and characteristics of weeds can be accurately identified. | BP, CNN, ANN, ResNet, GoogleNet, Transformer, YOLO |
Execution Layer | Based on the weed information identified by the decision-making layer, lasers are emitted in a targeted manner to achieve precise weeding operations. | Semiconductor laser, carbon dioxide laser |
Model | Deep Learning Technology | Method | Advantage |
---|---|---|---|
YOLO + ResNet [29] | • YOLO | Add two residual units to the second block; modify six DBL units before detection layer | Improve feature reuse, enhance small target understanding and accuracy |
YOLO + SPP5 [30] | • YOLO | Design SPP5 module with refined pooling kernels; create YOLOv4-SPP2-5 model | Replace first SPP of YOLOv4 with SPP5, add second SPP for multi-scale feature capture |
YOLO + DenseNet [31] | • YOLO | Merge channels across layers; add DenseNet layer | Reduce calculations, speed up training, optimize resource use |
YOLO Network Branch [32] | • YOLO | Add network branch, adjust anchor box, filter samples by mask | Balance positive and negative samples, improve sample learning |
Faster R-CNN [33] | • CNN | Migrate Faster RCNN to TensorFlow, simplify | Optimize training and testing speeds on COCO dataset |
Mask R-CNN [34] | • CNN | Replace ROI-Pooling with ROIAlign, add FCN head | Create accurate masks, avoid feature misalignment |
SSD + Anti-residual Module [35] | • SSD | Use a series of convolutions and sum layer | Enhance image perception, improve detection accuracy |
SSD + Hierarchical feature fusion [36] | • SSD | Sum and concatenate hole convolution outputs | Utilize feature-scale differences, complement features |
Efnet-1 [37] | • EfficientNet | Comprise normal and MB convolution modules, connect to classifier | Extract multi-scale features, enhance feature representation |
Key Issues | Solutions | Reference |
---|---|---|
Small object-detection problem | (1) Introduce the Convolutional Block Attention Module (CBAM) into the backbone feature-extraction network of the YOLOv5 target-detection algorithm and add the Transformer module. | [69] |
(2) Combine the collaborative attention (CA) and the receptive field block (RFB) module to improve the backbone network, introduce the CA attention mechanism, use the CARAFE upsampling method, and adopt WIoU v3 to replace the CIoU loss function. | [70] | |
(3) The YOLOX algorithm is optimized by adding lightweight attention modules, adding deconvolution layers, using GIoU instead of IoU, etc., to improve detection accuracy, enhance the algorithm’s ability to extract small-size features, and improve the accuracy of the predicted box position. | [71] | |
The balance between speed and accuracy | (1) Optimize the Neck layer of YOLOv8 using GSConv and VoV-GSCSP to improve the accuracy and inference speed of the model | [72] |
(2) Reshape the subsequent layers so that the new output tensor corresponds to an pixel grid cell instead of 32 × 32 as in YOLOv2. Add two blocks consisting of a convolutional layer, a batch normalization layer, and a leaky ReLU activation layer after the reshape, and finally add an output convolutional layer | [73] | |
(3) A new feature-extraction module DenseRes Block is proposed to replace the CSP Block in the backbone network CSP DarkNet in YOLOv4. The DenseRes Block consists of several series residual structures and shortcut connections with the same topology, which can better extract features while reducing the amount of calculation and inference time. | [74] |
Applications | Goal | Method | Reference |
---|---|---|---|
Vegetable plot | • Avoid the intensive labor of manual weeding and reduce food production costs | (1) Intelligent weed detection and laser weeding system to achieve the accurate positioning and removal of weeds | [79] |
• Achieve the accurate detection of weeds in vegetable seedling fields, which has potential practical value in the research and development of smart agricultural equipment, etc. | (2) Use crop-marking technology and a machine-vision system | [80] | |
Laboratory environment | • Demonstrate the feasibility of laser weeding equipment | Designed and tested a laser-based weed control device that controls weeds by irradiating the weed stems with lasers | [81] |
• Provide technical support for the realization of automated agricultural machinery precision fertilization, pesticide application and weeding | |||
Orchard | • Improve weed-control efficiency and reduce environmental impact | (1) Developed a static, movable, liftable and adjustable enclosed fiber laser weeding equipment and system | [82] |
• Improve weed-control efficiency and accuracy and reduce costs | (2) Designed and produced a prototype of a laser weeding robot based on STM32, using color training and morphological feature recognition algorithms to improve weed recognition accuracy | [83] | |
Rice fields | • Increase rice yield and reduce labor input • Reduce herbicide use and reduce environmental impact | (1) Use machine vision systems to identify weeds and crops in rice fields and guide robots to perform precise weeding. | [84] |
• Improve the accuracy of crop and associated weed identification and detection under complex backgrounds | (2) Use low-energy laser processing to control the growth of weeds by irradiating specific parts of the weeds. | [85] | |
(3) Use image processing to identify weeds, determine the amount of laser required for weeding, and use a fixed step length to perform weeding operations. | [86] | ||
(4) Develop an unmanned weeding robot platform and achieve stable autonomous navigation and weeding operations through sensor fusion. | [87] | ||
(5) Develop a small two-wheeled autonomous weeding robot that uses GPS and directional sensors for navigation, taking into account the effects of soil and GPS errors to achieve precise weeding operations. | [88] | ||
Cornfield | • Increase corn yield, reduce the impact of weeds on corn, and reduce labor intensity | (1) Identify crops and weeds through the YOLOX network and calculate the coordinates of weeds using the triangular similarity principle. | [89] |
• Achieve the rapid identification and positioning of weed meristems based on laser weeding | (2) Upload images and send control signals through the WiFi module. | [90] | |
• Improve the accuracy and efficiency of weed identification and provide accurate targeting for laser weeding | (3) Optimize the YOLOX algorithm and use self-made data sets for training and testing. | [87] | |
(4) Test a new static weeding path-planning algorithm, using an image-processing algorithm based on the color and size differences of crops and weeds to separate crops and weeds from the field background, detect the type of foreground and output the location information of weeds. | [91] | ||
(5) Design and trial-produce a laser weeding robot prototype, conduct field trials, and optimize the weed recognition algorithm. | [92] | ||
(6) Introduce a feature-extraction method based on wavelet transform to classify and identify weeds, and accurately control the sprayer to spray herbicides according to the weed location. | [86] | ||
Cotton Field | • Reduce the use of chemical herbicides | Use the YOLOX network to identify weeds, calculate the weed coordinates through monocular ranging, and control the end of the robotic arm to emit laser to weed. | [93] |
lawn | • Achieve efficient, accurate and environmentally friendly weed-control operations | (1) Designed and manufactured a laser weeding device based on a single-chip microcomputer, which senses and identifies weeds through sensors or cameras | [94] |
• Reduce dependence on chemical agents and reduce pollution to the environment | (2) Designed and trained a CNN model for weed detection and classification, combining a laser range finder (LRF) and an inertial measurement unit (IMU) to detect rice seedlings and obstacles, and achieve automatic weeding | [95] |
Problem | Solution | Reference |
---|---|---|
Traditional weed control methods are labor intensive | (1) Designed and manufactured a laser weeding robot based on a robotic arm, which achieves weeding through the cooperation of the robotic arm and laser. | [96] |
(2) Used the blue laser as the weeding actuator to design an intelligent laser weeding device. | [90] | |
(3) Designed and studied the actuator of the laser weeding robot, including laser control, weed recognition, robot field positioning and navigation, etc. | [97] | |
Mechanical weeding may damage crop roots, harm beneficial organisms, and affect soil structure. Chemical weeding is harmful to humans and the environment. | (1) Use low-energy laser treatment to control the growth of weeds by irradiating specific parts of the weeds. | [85] |
(2) Experimentally study the effects of the laser on the growth and development of weeds at different growth stages and determine the optimal timing and dosage for weed control. | [26] | |
(3) Study the effect of laser on weed control and create a weed damage model. | [84] | |
(4) Study the effect of laser on Elymus repens and experimentally determine the laser dose and number of irradiations required to kill this weed. | [98] | |
Traditional herbicides have resistance problems | (1) Use small autonomous laser weeding vehicles to reduce the use of chemical herbicides and reduce the impact on the environment and organisms. | [3] |
(2) Use CO2 laser cutting systems and laser-pointer triangulation systems to replace pesticides for weeding. | [22] | |
(3) Develop an automatic weeding robot that detects the location of weeds in real time and removes them through image recognition and processing technology, avoiding the use of harmful chemicals. | [99] | |
Traditional computer-vision methods have difficulty detecting weeds in natural scenes | (1) Develop a deep learning-based weed detection model | [100] |
(2) Design and manufacture Agri-Bot, which uses image processing and AI technology to identify and locate weeds | [101] | |
(3) Use advanced sensors and AI technology to accurately identify and remove weeds | [102] | |
Large weeding robots are not suitable for farmland environment in southwest China | (1) The design of a small blue laser weeding robot | [89] |
(2) The use of a small autonomous robot to automatically detect and remove weeds in farmland | [103] | |
Traditional detection algorithms have low recognition accuracy for small-sized weeds and obscured weeds | (1) Use laser reflection to identify changes in weeds, and use the Cascade RCNN deep learning method to detect and locate weeds after laser irradiation. | [104] |
(2) Use GSConv and VoV-GSCSP to optimize the Neck layer of YOLOv8 to improve the accuracy and inference speed of the model and achieve weed detection. | [72] | |
(3) Combine the collaborative attention CA and the receptive field block (RFB) module to improve the backbone network and introduce the CA attention mechanism. | [105] | |
Wheeled mobile robots are susceptible to uncertainty and interference during operation | (1) Control the robot’s movement and weeding operations through a smartphone to achieve automated weeding | [106] |
(2) Use a dual-servo system to adjust the laser emission angle to achieve precise weeding | [98] | |
(3) Propose an RNN-based tracking system to coordinate multiple controllers to achieve predetermined path tracking | [8] | |
(4) Improve the planning method and use a rolling-view observation model and biodiversity-aware weeding method | [1] |
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Wang, C.; Song, C.; Xu, T.; Jiang, R. Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques. Agriculture 2025, 15, 1213. https://doi.org/10.3390/agriculture15111213
Wang C, Song C, Xu T, Jiang R. Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques. Agriculture. 2025; 15(11):1213. https://doi.org/10.3390/agriculture15111213
Chicago/Turabian StyleWang, Chengming, Caixia Song, Tong Xu, and Runze Jiang. 2025. "Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques" Agriculture 15, no. 11: 1213. https://doi.org/10.3390/agriculture15111213
APA StyleWang, C., Song, C., Xu, T., & Jiang, R. (2025). Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques. Agriculture, 15(11), 1213. https://doi.org/10.3390/agriculture15111213