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Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and spot applications of pre-emergence and post-emergence herbicides. Post-emergence herbicides were applied at 2–4-leaf and at 6–8-leaf stages of maize. Weed and crop density, weed control efficacy (WCE), crop losses, accuracy of weed classification in UAV images, herbicide savings and maize yield were measured and analyzed. On average, 94% of all weed plants were correctly identified in the UAV images with the automatic classifier. Spot-spraying achieved up to 86% WCE, which was equal to the broadcast herbicide treatment. Early spot spraying saved 47% of herbicides compared to the broadcast herbicide application. Maize yields in the spot-spraying plots were equal to the broadcast herbicide application plots. This study demonstrates that spot-spraying based on UAV weed maps is feasible and provides a significant reduction in herbicide use. [ABSTRACT FROM AUTHOR]

Vegetables are an essential part of people's daily diet, and weeds can cause serious losses in vegetable yield and quality. Intelligent weeding technology for vegetables will be one of the mainstream technologies in modern agricultural development. This article reviews the current research status of intelligent weeding technology for vegetables, including vegetable and weed detection technology, weeding actuators, and weeding robots. Firstly, the vegetable and weed detection technology was introduced in detail from three aspects: global weed detection, crop-rows detection, and vegetable/weed precise recognition technology. The research results of some researchers were summarised, and the vegetable/weed precise recognition technology, including machine learning and proximal sensor technology, was introduced. Secondly, the weeding actuators and robots were introduced, including intelligent chemical weeding, mechanical weeding, physical weeding, and integrated weed management methods. Some weeding actuators and robots developed by researchers and agricultural companies were showcased. Finally, the challenges and future development directions of intelligent weeding technology were discussed and analysed. Intelligent weeding technology for vegetables is still mainly limited by natural conditions and a lack of technology. In the future, it will be possible to develop in the direction of multi-algorithm and multi-sensor fusion technologies. It is necessary to improve the applicability of intelligent weeding equipment for various environments, crops, and weeds. This article can provide a reference for future research in the field of intelligent weeding for vegetables. [ABSTRACT FROM AUTHOR]

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields. [ABSTRACT FROM AUTHOR]

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image‐classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed‐detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Weed detection plays a critical role in smart and precise agriculture systems by enabling targeted weed management and reducing environmental impact. Unmanned aerial vehicles (UAVs) and their associated imagery have emerged as powerful tools for weed detection. Traditional and deep learning methods have been explored for weed detection, with deep learning methods being favored due to their ability to handle complex patterns. However, accuracy rate and computation cost challenges persist in deep learning-based weed detection methods. To address this, we propose a method on the basis of the YOLOv5 algorithm to deal with high accuracy demand and low computation cost requirements. The approach involves model generation using a custom dataset and training, validation, and testing sets. Experimental results and performance evaluation validate the proposed method that indicates the research contributes to advancing weed detection in smart and precise agriculture systems, leveraging deep learning techniques for enhanced accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating the transition from conventional to precision agriculture, particularly in the context of weed control. Precision agriculture, which previously relied on manual efforts, has now embraced the use of smart devices for more efficient weed detection. However, several challenges are associated with weed detection, including the visual similarity between weed and crop, occlusion and lighting effects, as well as the need for early-stage weed control. Therefore, this study aimed to provide a comprehensive review of the application of both traditional machine learning and deep learning, as well as the combination of the two methods, for weed detection across different crop fields. The results of this review show the advantages and disadvantages of using machine learning and deep learning. Generally, deep learning produced superior accuracy compared to machine learning under various conditions. Machine learning required the selection of the right combination of features to achieve high accuracy in classifying weed and crop, particularly under conditions consisting of lighting and early growth effects. Moreover, a precise segmentation stage would be required in cases of occlusion. Machine learning had the advantage of achieving real-time processing by producing smaller models than deep learning, thereby eliminating the need for additional GPUs. However, the development of GPU technology is currently rapid, so researchers are more often using deep learning for more accurate weed identification.

Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques. [ABSTRACT FROM AUTHOR]

BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time‐consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi‐supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub‐images containing weeds from those containing only turfgrass. RESULTS: Compared with ResNet50, the evaluated SSL methods, Π‐model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated. CONCLUSION: These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time‐ and labor‐efficient than the FSL method. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features. [ABSTRACT FROM AUTHOR]

Weed detection is closely related to agricultural production, but often faces the problems of leaf shading and limited computational resources. Therefore, this study proposes an improved weed detection algorithm based on YOLOv8. Firstly, the Dilated Feature Integration Block is designed to improve the feature extraction in the backbone network by introducing large kernel convolution and multi-scale dilation convolution, which utilizes information from different scales and levels. Secondly, to solve the problem of a large number of parameters in the feature fusion process of the Path Aggregation Feature Pyramid Network, a new feature fusion architecture multi-scale feature interaction network is designed, which achieves the high-level semantic information to guide the low-level semantic information through the attention mechanism. Finally, we propose a Dynamic Feature Aggregation Head to solve the problem that the YOLOv8 detection head cannot dynamically focus on important features. Comprehensive experiments on two publicly accessible datasets show that the proposed model outperforms the benchmark model, with mAP50 and mAP75 improving by 4.7% and 5.0%, and 5.3% and 3.3%, respectively, whereas the number of model parameters is only 6.62 M. This study illustrates the utility potential of the algorithm for weed detection in cotton fields, marking a significant advancement of artificial intelligence in agriculture. [ABSTRACT FROM AUTHOR]

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop's growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms. [ABSTRACT FROM AUTHOR]

In response to the issues of missed detection, false positives, and low recognition rates for specific weed species during weed detection, a YOLOv8-based improved weed detection algorithm named EDS-YOLOv8 is proposed. Improvements were made in three main aspects. First, the YOLOv8 backbone network was enhanced with EfficientViT and RepViT architectures to improve the detection capability of dense-type weeds. Second, different attention mechanisms were added, such as SimAM and EMA, to learn 3D weights and achieve full fusion of features. BiFormer was introduced for dynamic sparse attention and resource allocation. Third, significant module improvement involved introducing dynamic snake convolution into the C2f module to further enhance detection capabilities for deformable objects, especially needle-shaped weeds. The improved model is validated on the established weed dataset. The results show that combining the original backbone network with dynamic snake convolutions yields the highest performance improvement. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 5.6%, 5.8%, 6.4%, and 1%, respectively, and ablation experiments on the effects of the three improvement methods on model performance show that using EfficientViT as the backbone network while simultaneously improving the crucial module and adding the SimAM attention mechanism effectively enhances the model's performance. Precision, recall, mAP (0.5), and mAP (0.5:0.95) are improved by 6%, 5.9%, 6.4%, and 0.7%, respectively. [ABSTRACT FROM AUTHOR]

Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology. [ABSTRACT FROM AUTHOR]

Weeds are among the major risks impacting agricultural production and quality, it is still difficult to create reliable weed identification and detection systems because of the unstructured field circumstances and substantial biological heterogeneity of weeds. The proposed work develops a weed detection model for achieving higher crop yield. Crop and weed images are taken as an input for the proposed method. The collection of data consists of raw-data that cannot produce high accuracy. So, a certain pre-processing technique is used in the proposed method for achieving high accuracy. Adaptive median filter, adaptive gamma correction and high boost filtering techniques are used as pre-processing techniques for noise removal, contrast enhancement and edge sharpening. Then the pre-processed image is segmented and detected according to the features and properties of the pixels in the image. Improved YOLOv3-technique is used in the proposed approach for segmentation and detection of weed species. RSA-optimization is used to select the hyperparameters of YOLOv3-optimally. The proposed method is tested with several metrics which attain better performance like 96% accuracy, 96% precision, 95% recall, 4% error and 95% specificity value. Thus the designed model detects weed-species in an effective manner and it is useful for achieving higher crop production. [ABSTRACT FROM AUTHOR]

Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability of the underlying backbone network. Secondly, the MDPM module is proposed to generate attention matrices by capturing the horizontal and vertical information of feature maps, reducing duplicate information in the feature maps. Finally, we will replace the upsampling module of YOLOv8 with the CARAFE module to provide better upsampling performance. Extensive experiments on two publicly available datasets showed that the F1, mAP50 and mAP75 metrics improved by 1.2%, 5.1%, 2.9% and 3.8%, 1.3%, 2.2%, respectively, compared to the baseline model. This study showcases the algorithm's potential for practical applications in weed detection within cotton fields, promoting the significant development of artificial intelligence in the field of agriculture. [ABSTRACT FROM AUTHOR]

Accurate weed detection in agricultural images is a crucial challenge for improving crop management practices and reducing chemical usage. In this study, we propose an innovative segmentation model called DWUNet, inspired by popular architectures and incorporating the latest advances in the state of the art. Our model delivers remarkable accuracy, with a Jaccard index reaching 0.825, while ensuring fast inference speed of only 8 ms per image, thus providing an optimal solution for real-time applications. By comparing DWUNet to several state-of-the-art models, we demonstrate its superiority in terms of accuracy and efficiency. Furthermore, a qualitative analysis of the visual results confirms DWUNet's ability to accurately detect weeds and generalize results beyond the training data. This study represents a significant advancement in the field of precision agriculture, providing a powerful tool for sustainable crop management and reducing environmental impact.

Agriculture has been completely transformed by Deep Learning (DL) techniques, which allow for quick object localization and detection. However, because weeds and crops are similar in color, form, and texture, weed detection and categorization can be difficult. Advantages in object detection, recognition, and image classification can be obtained with deep learning (DL), a vital aspect of machine learning (ML). Because crops and weeds are similar, ML techniques have difficulty extracting and choosing distinguishing traits. This literature review demonstrates the potential of various DL methods for crop weed identification, localization, and classification. This research work investigates the present status of Deep Learning based weed identification and categorization systems. The majority of research employs supervised learning strategies, polishing pre-trained models on sizable, labeled datasets to achieve high accuracy. Innovations are driven by the need for sustainable weed management methods, and deep learning is demonstrating encouraging outcomes in image-based weed detection systems. To solve issues like resource scarcity, population increase, and climate change, precision agriculture holds great promise for the integration of AI with IoT-enabled farm equipment.

Hyperspectral remote sensors are emerging as valuable technology for identifying weeds in field crops. These sensors can acquire high-resolution images, both spatially and spectrally, which are crucial for early weed identification. The objective of this paper is to provide a comprehensive review on studies that have utilized hyperspectral remote sensing for weed identification in field crops. The review further explored the weed identification accuracies of these remote sensors. To gather the relevant information, a literature search was conducted across three academic journals (Science Direct, Web of Science, and Google Scholar) using a search criterion. The review summarized key findings as follows; (a) remote sensors such as the Specim ImSpector V10E, ASD FieldSpec, and Resonon Pika II are the widely used remote sensors for both ground and aerial data acquisition, (b) majority of the research utilized ground-based mounted platform for data acquisition compared to unmanned aerial platforms, (c) preprocessing hyperspectral data is critical for weed identification. Techniques like image calibration, standard normal variate, multiplicative scatter correction, Savitsky-Golay smoothing, derivatives, and features selection are among the most used techniques, (d) traditional machine learning models namely support vector machines (SVM), partial least square discriminant analysis (PLS-DA), maximum likelihood classifiers (MLC), and random forest (RF) are the widely employed classifiers for weed identification, (e) the application of deep learning technique, namely convolutional neural networks (CNNs) are limited, but its application demonstrated superior performance accuracies compared to traditional machine learning models. In conclusion, this review seeks to provide valuable insights into the latest developments in hyperspectral remote sensing for weed identification.

The implementation of a machine-vision system for real-time precision weed management is a crucial step towards the development of smart spraying robotic vehicles. The intelligent machine-vision system, constructed using deep learning object detection models, has the potential to accurately detect weeds and crops from images. Both data-centric and model-centric approaches of deep learning model development offer advantages depending on environment and non-environment factors. To assess the performance of weed detection in real-field conditions for eight crop species, the Yolov8, Yolov9, and customized Yolov9 deep learning models were trained and evaluated using RGB images from four locations (Casselton, Fargo, and Carrington) over a two-year period in the Great Plains region of the U.S. The experiment encompassed eight crop species—dry bean, canola, corn, field pea, flax, lentil, soybean, and sugar beet—and five weed species—horseweed, kochia, redroot pigweed, common ragweed, and water hemp. Six Yolov8 and eight Yolov9 model variants were trained using annotated weed and crop images gathered from four different sites including combined dataset from four sites. The Yolov8 and Yolov9 models’ performance in weed detection were assessed based on mean average precision (mAP50) metrics for five datasets, eight crop species, and five weed species. The results of the weed and crop detection evaluation showed high mAP50 values of 86.2 %. The mAP50 values for individual weed and crop species detection ranged from 80.8 % to 98 %. The results demonstrated that the performance of the model varied by model type (model-centric), location due to environment (data-centric), data size (data-centric), data quality (data-centric), and object size in the image (data-centric). Nevertheless, the Yolov9 customized lightweight model has the potential to play a significant role in building a real time machine-vision-based precision weed management system.

The main objective of this study is to develop a method for the automated detection and classification of weeds and sugar beets. Precision agriculture is an essential area of research that aims to optimize farming practices and reduce the use of harmful chemicals. For this purpose, the Faster RCNN and Federating Learning (FL)-based ensemble models were utilized to classify a specific dataset. In the first stage of the study, feature extraction is performed from the images in the dataset and classified by machine learning algorithms. Then, classification is carried out with the help of FL based deep learning ensemble models. Within the scope of the study, grid search is used for hyperparameter optimization and the results are obtained by a tenfold cross-validation method. Among all tested algorithms, the FL-based ensemble model constructed using the ResNet50 model exhibited the highest accuracy rate of 99%. This system has the potential to significantly reduce the use of herbicides and other chemicals in agricultural practices, promoting a more sustainable form of agriculture. [ABSTRACT FROM AUTHOR]

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated. [ABSTRACT FROM AUTHOR]

With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management. [ABSTRACT FROM AUTHOR]

Agriculture is heavily affected by weeds due to their random appearances in fields, competition for water, nutrients, and sunlight, and, if not controlled effectively, negative impact on crop yields. In general, there are many prevention strategies, but they are expensive and time consuming; moreover, labor costs have increased substantially. To overcome these challenges, a novel AGS-MNFELM model has been proposed for weed detection in agricultural fields. Initially, the gathered images are pre-processed using bilateral filter for noise removal and CLAHE for enhancing the image quality. The pre-processed images are taken as an input for automatic graph cut segmentation (AGS) model for segmenting regions with bounding box using the RCNN rather than manual initialization, hence eliminating the need for manual interpretation. The Mobile Net model is used to acquired rich feature representations for a variety of images, and the retrieved features FELM (Fuzzy Extreme Learning Machine Model) classifier is used to classify four weed types of maize and soyabean: cocklebur, redroot pigweed, foxtail, and giant ragweed. The proposed AGS-MNFELM model has been evaluated in terms of its sensitivity, accuracy, specificity, and F1 score. The experimental result reveals that the proposed AGS-MNFELM model attains the overall accuracy of 98.63%. The proposed deep learning-based MobileNet improves the overall accuracy range of 7.91%, 4.15%, 3.44% and 5.88% better than traditional LeNet, AlexNet, DenseNet and ResNet, respectively. [ABSTRACT FROM AUTHOR]

For weed detection in wheat field, it is difficult to identify weeds against the complex field background using single-modal red–green–blue (RGB) images due to similar appearance of grass weeds and wheat. To overcome limitations of single-modal information in grass weed detection, a dual-path weed detection network (WeedsNet) based on multi-modal information is proposed. At first, the single-channel depth image is encoded as a new three-channel image having similar structures with RGB color space, so that they are suitable for feature extraction using a convolutional neural network (CNN). Then, WeedsNet comprising a dual-path feature extraction network is constructed to extract features of weeds from RGB and depth images simultaneously. Finally, weights are assigned to features in different modalities in tandem with the idea of multi-scale object detection and the attention mechanism, thus effectively fusing multi-modal information. The results of the interpretability analysis of the model demonstrate that depth information is beneficial to achieve the detection of grass weeds, and effectively improves the weed detection accuracy in wheat field by complementing with RGB image features. WeedsNet has better detection accuracy than both traditional machine learning methods and integrated learning-based weight assignment methods. The weed detection accuracy is 62.3% based on WeedsNet in natural wheat field and the detection speed of single image was about 0.5s. Weed detection software is designed and developed based on WeedsNet to achieve real-time output of weeds distribution information in natural wheat field. [ABSTRACT FROM AUTHOR]

The agricultural sector plays an important role in contributing significantly to the gross domestic product (GDP) growth in developing countries. On the other hand, agriculture is widely affected by major factors such as environmental changes, natural disasters, pesticide control, and soil and irrigation-related issues, which reduce crop yield. The convergence of Industry 4.0 and agriculture offers an opportunity to move into the next generation of Agriculture 4.0. The internet of things (IoT), remote sensing, machine learning, deep learning, big data, cloud computing, thermal imaging, end-user apps and unmanned aerial vehicles offer a full-stack solution. IoT provides the ubiquitous connectivity of smart devices to the internet to collect, process and analyse a large amount of agriculture field data more quickly and synthesize them to make smart decisions using various machine learning and deep learning algorithms. This study reviews the challenges and major issues in the IoT agriculture domain and explores its emergence with new technologies. It covers the existing literature and illustrates how IoT applicationbased precision agriculture solutions have contributed. A case study on weed detection for smart agriculture using the YOLOv5 model is presented, achieving high accuracy. Finally, various IoT agriculture use cases are discussed, along with current research issues and possible solutions for future IoT-based agriculture advancement. [ABSTRACT FROM AUTHOR]

With the increasing environmental awareness and the demand for sustainable agriculture, herbicide reduction has become an important goal. Accurate and efficient weed detection in soybean fields is the key to test the effectiveness of herbicide application, but current technologies and methods still have some problems in terms of accuracy and efficiency, such as relying on manual detection and poor adaptability to some complex environments. Therefore, in this study, weeding experiments in soybean fields with reduced herbicide application, including four levels, were carried out, and an unmanned aerial vehicle (UAV) was utilized to obtain field images. We proposed a weed detection model—YOLOv7-FWeed—based on improved YOLOv7, adopted F-ReLU as the activation function of the convolution module, and added the MaxPool multihead self-attention (M-MHSA) module to enhance the recognition accuracy of weeds. We continuously monitored changes in soybean leaf area and dry matter weight after herbicide reduction as a reflection of soybean growth at optimal herbicide application levels. The results showed that the herbicide application level of electrostatic spraying + 10% reduction could be used for weeding in soybean fields, and YOLOv7-FWeed was higher than YOLOv7 and YOLOv7-enhanced in all the evaluation indexes. The precision of the model was 0.9496, the recall was 0.9125, the F1 was 0.9307, and the mAP was 0.9662. The results of continuous monitoring of soybean leaf area and dry matter weight showed that herbicide reduction could effectively control weed growth and would not hinder soybean growth. This study can provide a more accurate, efficient, and intelligent solution for weed detection in soybean fields, thus promoting herbicide reduction and providing guidance for exploring efficient herbicide application techniques. [ABSTRACT FROM AUTHOR]

The knowledge that precision weed control in agricultural fields can reduce waste and increase productivity has led to research into autonomous machines capable of detecting and removing weeds in real time. One of the driving factors for weed detection is to develop alternatives to herbicides, which are becoming less effective as weed species develop resistance. Advances in deep learning technology have significantly improved the robustness of weed detection tasks. However, deep learning algorithms often require extensive computational resources, typically found in powerful computers that are not suitable for deployment in robotic platforms. Most ground rovers and UAVs utilize embedded computers that are portable but limited in performance. This necessitates research into deep learning models that are computationally lightweight enough to function in embedded computers for real-time applications while still maintaining a base level of detection accuracy. This paper evaluates the weed detection performance of three real-time-capable deep learning models, YOLOv4, EfficientDet, and CenterNet, when run on a deep-learning-enabled embedded computer, an Nvidia Jetson Xavier AGX. We tested the accuracy of the models in detecting 13 different species of weeds and assesses their real-time viability through their inference speeds on an embedded computer compared to a powerful deep learning PC. The results showed that YOLOv4 performed better than the other models, achieving an average inference speed of 80 ms per image and 14 frames per second on a video when run on an imbedded computer, while maintaining a mean average precision of 93.4% at a 50% IoU threshold. Furthermore, recognizing that some real-world applications may require even greater speed, and that the detection program would not be the only task running on the embedded computer, a lightweight version of the YOLOv4 model, YOLOv4-tiny, was tested for improved performance in an embedded computer. YOLOv4-tiny impressively achieved an average inference speed of 24.5 ms per image and 52 frames per second, albeit with a slightly reduced mean average precision of 89% at a 50% IoU threshold, making it an ideal choice for real-time weed detection. [ABSTRACT FROM AUTHOR]

Accurate determination of the weed ratio in artificial meadows is critical for efficient pasture renovation. Reed canary grass (Phalaris arundinacea L., RCG) is treated as a troublesome grass in the Hokkaido region of Japan because of its low feed quality and poor palatability in dairy farming. In the present study, we examined a method of identifying the dominant area of RCG in timothy (Phleum pretense L.) meadows by applying the Canny method to unmanned aerial vehicle (UAV)‐based digital surface model (DSM) images. Comparing the actual RCG patches observed in a field survey (50 m quadrats × 4 places) with the predicted patches, the pixel‐based recall and F value were 0.90 and 0.83, respectively. These results demonstrated that the area of RCG can be detected using a simple method without supervised data or deep learning. This study is expected to be utilized in a wide variety of applications using relative height differences. [ABSTRACT FROM AUTHOR]

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop’s growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms.

Vegetables are an essential part of people’s daily diet, and weeds can cause serious losses in vegetable yield and quality. Intelligent weeding technology for vegetables will be one of the mainstream technologies in modern agricultural development. This article reviews the current research status of intelligent weeding technology for vegetables, including vegetable and weed detection technology, weeding actuators, and weeding robots. Firstly, the vegetable and weed detection technology was introduced in detail from three aspects: global weed detection, crop-rows detection, and vegetable/weed precise recognition technology. The research results of some researchers were summarised, and the vegetable/weed precise recognition technology, including machine learning and proximal sensor technology, was introduced. Secondly, the weeding actuators and robots were introduced, including intelligent chemical weeding, mechanical weeding, physical weeding, and integrated weed management methods. Some weeding actuators and robots developed by researchers and agricultural companies were showcased. Finally, the challenges and future development directions of intelligent weeding technology were discussed and analysed. Intelligent weeding technology for vegetables is still mainly limited by natural conditions and a lack of technology. In the future, it will be possible to develop in the direction of multi-algorithm and multi-sensor fusion technologies. It is necessary to improve the applicability of intelligent weeding equipment for various environments, crops, and weeds. This article can provide a reference for future research in the field of intelligent weeding for vegetables.

Spot spraying can significantly reduce herbicide use while maintaining equal weed control efficacy as a broadcast application of herbicides. Several online spot-spraying systems have been developed, with sensors mounted on the sprayer or by recording the RTK-GNSS position of each crop seed. In this study, spot spraying was realized offline based on georeferenced unmanned aerial vehicle (UAV) images with high spatial resolution. Studies were conducted in four maize fields in Southwestern Germany in 2023. A randomized complete block design was used with seven treatments containing broadcast and spot applications of pre-emergence and post-emergence herbicides. Post-emergence herbicides were applied at 2–4-leaf and at 6–8-leaf stages of maize. Weed and crop density, weed control efficacy (WCE), crop losses, accuracy of weed classification in UAV images, herbicide savings and maize yield were measured and analyzed. On average, 94% of all weed plants were correctly identified in the UAV images with the automatic classifier. Spot-spraying achieved up to 86% WCE, which was equal to the broadcast herbicide treatment. Early spot spraying saved 47% of herbicides compared to the broadcast herbicide application. Maize yields in the spot-spraying plots were equal to the broadcast herbicide application plots. This study demonstrates that spot-spraying based on UAV weed maps is feasible and provides a significant reduction in herbicide use.

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

In field weed detection tasks, achieving accurate identification of crops and weeds is the primary target. However, since small target weeds among crops are not easily detected, this undoubtedly increases the difficulty of detection. In order to solve this problem, based on the YOLO-V4 network, this paper modifies the residual block of the backbone network into a Res2block residual block with a hierarchical residual mode, and constructs a new backbone network Csp2Darknet53 to enhance fine-grained feature detection; In addition, receptive field enlargement and multi-scale fusion are achieved by using the I-SPP structure with multi-branch structure and dilated convolution; Finally, a depthwise separable convolution block with residual mode (IDSC-X) is proposed to replace the original 5-time convolution block in the path aggregation network (PANet) to ensure that the original features are not completely lost and reduce the amount of parameters. Compared with FasterR-CNN, SSD, MaskR-CNN, YOLO-V3 and YOLO-V4, the improved network detection accuracy is significantly better than other networks. Compared with YOLO-V4, the AP value of small target weeds increased by 15.1%, the mAP value increased by 4.2%, and the model parameters and training weight file size decreased by 34%. The results show that the method is feasible to improve the accurate detection of small target weeds, and can be extended to weed detection tasks of different crops. [ABSTRACT FROM AUTHOR]

Weeds pose a persistent threat to farmers' yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying the entire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds. [ABSTRACT FROM AUTHOR]

Crop yields in paddy fields can be greatly lowered by weeds and crowded places. Conventional techniques for identifying thick areas and weeds are frequently labor-and time-intensive. While machine learning-based techniques present a viable substitute, creating a single model that is capable of properly and effectively completing both jobs is difficult. This study uses image and video processing to present a multi-task machine-learning model for weed detection and dense area identification in paddy fields. The YOLO V8 deep learning architecture, which is renowned for its great accuracy and speed, serves as the foundation for the model. We gathered a sizable dataset of labeled weeds and thick areas from paddy field photos and videos in order to train the algorithm. After that, the model was trained to simultaneously identify dense areas and detect weeds. The model was assessed using a different test dataset once it had been trained. The outcomes demonstrated that even when used with video streams, the model maintained good accuracy on both tasks. The suggested model can be utilized to create other paddy field management applications, including: • Automated weed detection: The suggested model has the potential to assist farmers in increasing yields and lessening their environmental effects by automating the processes of weed detection and dense area designation. [ABSTRACT FROM AUTHOR]

Agricultural image and vision computing are significantly different from other object classification-based methods because two base classes in agriculture, crops and weeds, have many common traits. Efficient crop, weeds, and soil classification are required to perform autonomous (spraying, harvesting, etc.) activities in agricultural fields. In a three-class (crop–weed–background) agricultural classification scenario, it is usually easier to accurately classify the background class than the crop and weed classes because the background class appears significantly different feature-wise than the crop and weed classes. However, robustly distinguishing between the crop and weed classes is challenging because their appearance features generally look very similar. To address this problem, we propose a framework based on a convolutional W-shaped network with two encoder–decoder structures of different sizes. The first encoder–decoder structure differentiates between background and vegetation (crop and weed), and the second encoder–decoder structure learns discriminating features to classify crop and weed classes efficiently. The proposed W network is generalizable for different crop types. The effectiveness of the proposed network is demonstrated on two crop datasets—a tobacco dataset and a sesame dataset, both collected in this study and made available publicly online for use by the community—by evaluating and comparing the performance with existing related methods. The proposed method consistently outperforms existing related methods on both datasets. [ABSTRACT FROM AUTHOR]

Weeds are one of the biggest barriers to crop productivity in recent years. Many efforts are being made to enhance the quality of agricultural crops which is a challenging task for farmers. Weeds are also affecting the quality of the crops along with the productivity of the crops. Weeds are the reason for the lack of water in the crops, because they absorb the moisture of the land, due to which the crops do not get proper water. Weeds also create hindrance during harvesting of crops. When farmers harvest crops, often toxic weeds also get mixed, due to which farmers do not get fair price for their crops. To prevent weeds, farmers spray herbicides uniformly throughout the field without knowing what type of weed it is. Due to which there is a lot of damage to the crops. Spraying herbicides also affects the environment, so it is essential to have a thorough knowledge of weeds so that specific weeds can be controlled. The main objective of this paper is to improve the accuracy of weed detection using machine learning techniques. [ABSTRACT FROM AUTHOR]