Irrelevant Features Research Articles

Selectively maintaining an object's feature in visual working memory: A comparison between highly discriminable and fine-grained features.

Selectively maintaining information is an essential function of visual working memory (VWM). Recent VWM studies have mainly focused on selective maintenance of objects, leaving the mechanisms of selectively maintaining an object's feature in VWM unknown. Based on the interactive model of perception and VWM, we hypothesized that there are distinct selective maintenance mechanisms for objects containing fine-grained features versus objects containing highly discriminable features. To test this hypothesis, we first required participants to memorize a dual-feature object (colored simple shapes vs. colored polygons), and informed them about the target feature via a retro-cue. Then a visual search task was added to examine the fate of the irrelevant feature. The selective maintenance of an object's feature predicted that the irrelevant feature should be removed from the active state of VWM and should not capture attention when presented as a distractor in the visual search task. We found that irrelevant simple shapes impaired performance in the visual search task (Experiment 1). However, irrelevant polygons did not affect visual search performance (Experiment 2), and this could not be explained by decay of polygons (Experiment 3) or by polygons not capturing attention (Experiment 4). These findings suggest that VWM adopts dissociable mechanisms to selectively maintain an object's feature, depending on the feature's perceptual characteristics.

Breast Cancer Detection Cum Classification using Feature Optimization and Transfer Learning

Early detection of breast cancer can significantly reduce its high mortality rate. However, manual diagnosis from mammography images always requires a skilled professional. Various researchers have developed artificial intelligence-based methods to address this challenge, yet they encounter several issues such as overlapping cancerous and noncancerous regions, extraction of irrelevant features, and inadequate training models. In this paper, we propose a novel computationally automated biological mechanism for categorizing breast cancer. This approach leverages a new optimization technique based on the Moth Flame Optimization (MFO) algorithm, which enhances the classification of breast cancer cases. The proposed framework consists of two main stages: Utilizing Deep Neural Networks (DNN) based on transfer learning. Implementing a Convolutional Neural Network (DCNN) optimized with MFO. By integrating transfer learning with an optimized DCNN for classification, our method significantly improves accuracy compared to recent approaches. The proposed framework was evaluated using publicly available datasets, achieving an average classification accuracy of 97.95%. To ensure the statistical significance and robustness of our results, we conducted tests that demonstrated the effectiveness and superior performance of our methodology compared to current methods.

Label distribution feature selection based on neighborhood rough set

SummaryIn label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature. Motivated by neighborhood rough set (NRS), which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this article proposes a novel label distribution feature selection method. In this article, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new NRS model for LDL is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on NRS is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm.

Label distribution feature selection based on hierarchical structure and neighborhood granularity

Label Distribution Learning (LDL) addresses label ambiguity in datasets but struggles with high-dimensional data due to irrelevant features. Label Distribution Feature Selection (LDFS) methods can effectively unravel the issues, but they often overlook the advantages of utilizing hierarchical relationships among data, which can improve feature discriminability. Furthermore, these methods inadequately consider the granulation process, directly affecting the important features’ identification. To overcome these challenges, this study proposes a novel LDFS approach incorporating hierarchical structures and neighborhood granularity. Our algorithm proceeds in three stages: initially, it forms a multi-granular representation of data to reveal hierarchical relationships; subsequently, in the granulation process, it employs a variable precision rough set model, leveraging neighborhood granularity for a nuanced feature relevance assessment; and finally, it synthesizes these findings via a fusion strategy, culminating in a hierarchical feature ranking. Extensive experiments are conducted on thirteen benchmark datasets against five different algorithms in terms of six evaluation metrics. The results show that our method outperforms competitors in about 80% of the cases, demonstrating its effectiveness and generalization.

Robust weighted fuzzy margin-based feature selection with three-way decision

Feature selection has shown noticeable benefits to the tasks of machine learning and data mining, and an extensive variety of feature selection methods has been proposed to remove redundant and irrelevant features. However, most of the existing methods aim to find a feature subset to perfectly fit data with the minimum empirical risk, thus causing the problems of overfitting and noise sensitivity. In this study, a robust weighted fuzzy margin-based feature selection is proposed for uncertain data with noise. Concretely, a robust weighted fuzzy margin based on fuzzy rough sets is first introduced to evaluate the significance of different features. Then, a gradient ascent algorithm based on the noise filtering strategy and three-way decision is developed to optimize the sample and feature weights to further enlarge the fuzzy margin. Finally, an adaptive feature selection algorithm based on the robust weighted fuzzy margin is presented to generate an optimal feature subset with a large margin. Extensive experiments on the UCI benchmark datasets show that the proposed method could obtain high-quality feature subsets and outperform other representative methods under different noise rates.

Explainable ResNet50 learning model based on copula entropy for cotton plant disease prediction

This paper presents a novel Deep Learning (DL) framework for cotton plant disease prediction based on Explainable Artificial Intelligence (XAI) and Copula entropy based-Grey Wolf Optimization (GWO) algorithm. The suggested framework uses a cotton plant image as input and pre-processes it to produce an image with enhanced contrast. Features are extracted from the leaf images with the ResNet50 CNN model. A crucial pre-processing model known as feature selection helps to improve the effectiveness of image classification by deleting extraneous or irrelevant features. Therefore, the Gray Wolf optimization (GWO) algorithm which is a global search method with potential use in feature selection is employed in this paper. The proposed framework introduces Copula entropy (CE) as an indicator of association to create the GWO’s initial population and enhance the GWO feature engineering process. For the GWO initialization procedure, CE has been used to choose the most significant features which substantially improved the quality of the GWO starting population and as a result improved the performance of the proposed CE-based GWO algorithm by 78.57 % faster than the traditional GWO as stated by the time complexity analysis. In addition, Feature importance explanation is determined using XAI layer. The final classification is achieved using Random Forests (RF) classifier which is an ensemble learning approach. According to the experimental results, the suggested model has a classification accuracy of 99 % and a mean squared error of 0.0383. Furthermore, the proposed model has been compared to state-of-the-art algorithms and the results showed that the proposed model has the superiority performance. The proposed model can therefore be used to track a variety of cotton areas to enable faster analysis and response, resulting in higher productivity.

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount and dimensionality caused challenges in many disciplines, with the primary issues being high computing costs, memory costs, and low accuracy performance. These issues will arise since Machine Learning (ML) classifiers are mostly used in these fields. However, noisy and irrelevant features have an impact on ML accuracy. Therefore, to choose the best subset of features and decrease the dimensionality of the data, Metaheuristics (MHs) optimization algorithms are applied to Feature Selection (FS) using various modalities of medical imaging or disease datasets with different dimensions. The review starts by giving a general overview of the many approaches to AI algorithms, followed by a general overview of the various MH algorithms for healthcare applications, an analysis of MHs boosted AI for healthcare applications, and using a wide range of research databases as a data source for access to numerous field publications. The final section of this review discusses the problems and challenges facing healthcare application development.

A Novel Hybrid SDR Model for Dengue Prediction using Opt_Recurr Feature Selection Algorithm

Dengue is a vector borne disease , which can be fatal at times . Early detection dengue of dengue is vital as no vaccines have been developed for dengue yet..The process of eliminating irrelevant and redundant features from the data set facilitates the optimal features selection . This study is proposed to select the Optimal features by using Opt_Recur algorithm from the Dengue dataset , a hybrid SDR model which makes prediction with better accuracy when compared to the conventional classifiers like the Support Vector Machine (SVM), Decision Tree(DT) and the Random Forest (RF) classifiers.

Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification

Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-based clinical assessment methods are highly subjective and dependent on the neurologist's expertise. Alternate automated methods for arising-from-chair assessment can be established based on quantitative susceptibility mapping (QSM) images with multiple-instance learning. However, performance stability for such methods can be typically undermined by the presence of irrelevant or spuriously-relevant features that mask the intrinsic causal features. Therefore, we propose a QSM-based arising-from-chair assessment method using a causal graph-neural-network framework, where counterfactual and debiasing strategies are developed and integrated into this framework for capturing causal features. Specifically, the counterfactual strategy is proposed to suppress irrelevant features caused by background noise, by producing incorrect predictions when dropping causal parts. The debiasing strategy is proposed to suppress spuriously relevant features caused by the sampling bias and it comprises a resampling guidance scheme for selecting stable instances and a causal invariance constraint for improving stability under various interferences. The results of extensive experiments demonstrated the superiority of the proposed method in detecting arising-from-chair abnormalities. Its clinical feasibility was further confirmed by the coincidence between the selected causal features and those reported in earlier medical studies. Additionally, the proposed method was extensible for another motion task of leg agility. Overall, this study provides a potential tool for automated arising-from-chair assessment in PD patients, and also introduces causal counterfactual thinking in medical image analysis. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CFGNN-PDarising.

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty

BackgroundIn total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty. MethodsWe conducted this prospective observational study using convenience sampling and collected data from 327 total joint arthroplasty patients in a tertiary hospital from March 4, 2023, to September 11, 2023. Of those, 229 patients were assigned to the training and 98 to the testing sets. The Chi-square, Mann–Whitney U, and t-tests were used for baseline analyses. The feature variables selection used the RF algorithms, and the decision tree model was trained on 299 examples and validated on 98. The sensitivity, specificity, recall, F1 score, and area under the curve were used to test the model’s performance. ResultsThe RF algorithms identified the preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation as risk factors for IOH. The decision tree was grown to 5 levels with 9 terminal nodes. The overall incidence of IOH was 42.13%. The sensitivity, specificity, recall, F1 score, and area under the curve were 0.651, 0.907, 0.916, 0.761, and 0.810, respectively. The model indicated strong internal consistency and predictive ability. ConclusionsThe preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation could accurately predict IOH in total joint arthroplasty patients. By monitoring these factors, the clinical staff could achieve early detection and intervention of IOH in total joint arthroplasty patients.

Explicit unsupervised feature selection based on structured graph and locally linear embedding

Numerous redundant and irrelevant features are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Unsupervised feature selection (UFS) as a trending topic in a great deal of domains has been greatly concerned, which can effectively filter out redundant and irrelevant features in the unlabeled data. The majority of existing UFS methods mainly focus on building the parameterized model in the original feature space or some certain low-dimensional feature subspace, which cannot fully capture the information of the target space. In this work, we propose a novel UFS method that unifies the explicit feature selective matrix and structured graph into a learning framework. An explicit selection matrix, whose scale, bound of the elements and structure are considered, is tailored for UFS. Furthermore, we take into account the local consistency so that the low dimensional manifold structures can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both the explicit selection matrix and intrinsic structure are learned in the target feature subspace, and thus the proposed methodology is more straightforward and effective. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis. The experimental results show that our proposed approach has improved by over 5.28% and 5.79% on average in terms of clustering accuracy (ACC) and Normalized Mutual Information (NMI) compared with comparison methods, which demonstrates its superiority.

A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability.

A many objective based feature selection model for software defect prediction

SummaryGiven the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity.

Infrared and visible image fusion and detection based on interactive training strategy and feature filter extraction module

The infrared and visible image fusion-detection technique aims to expand the observation wavelength range to overcome harsh environmental conditions such as low light, smoke, heavy fog, etc., and improve the reliability and robustness of target detection. However, the current fusion-detection method suffers from problems of the joint optimization of the fusion-detection model and irrelevant feature interference. To address these problems, we propose an infrared and visible image fusion-detection network based on an interactive training strategy (ITS) and a feature filter extraction module (FFEM). The ITS constructs the closed-loop optimization for the fusion-detection method, which can mutually enhance the performance of the fusion and detection model through mutual supervision. The FFEM is designed to acquire detection-conducive features from visible and infrared images. Specifically, we define filtering weights based on the covariance values of features and use these weights to enhance relevant features and soft redundant and irrelevant features (e.g., noise). Quantitative and qualitative experimental results demonstrate that the proposed method achieves competitive performance in target detection accuracy compared with other state-of-the-art fusion-detection methods.

Semantic attention-based heterogeneous feature aggregation network for image fusion

Infrared and visible image fusion aims to generate a comprehensive image that retains both salient targets of the infrared image and texture details of the visible image. However, existing methods overlook the differences in attention to semantic information among different fused images. To address this issue, we propose a semantic attention-based heterogeneous feature aggregation network for image fusion. The key component of our network is the semantic attention-based fusion module, which leverages the weights derived from semantic feature maps to dynamically adjust the significance of various semantic objects within the fusion feature maps. By using semantic weights as guidance, our fusion process concentrates on regions with crucial semantics, resulting in a more focused fusion that preserves rich semantic information. Moreover, we propose an innovative component called the attentive dense block. This block effectively filters out irrelevant features during extraction, accentuates essential features to their maximum potential, and enhances the visual quality of the fused images. Importantly, our network demonstrates strong generalization capabilities. Extensive experiments validate the superiority of our proposed network over current state-of-the-art techniques in terms of both visual appeal and semantics-driven evaluation.

Enhanced Hybrid Vision Transformer with Multi-Scale Feature Integration and Patch Dropping for Facial Expression Recognition.

Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in real-world environments remains highly challenging. At the same time, methods solely based on CNN heavily rely on local spatial features, lack global information, and struggle to balance the relationship between computational complexity and recognition accuracy. Consequently, the CNN-based models still fall short in their ability to address FER adequately. To address these issues, we propose a lightweight facial expression recognition method based on a hybrid vision transformer. This method captures multi-scale facial features through an improved attention module, achieving richer feature integration, enhancing the network's perception of key facial expression regions, and improving feature extraction capabilities. Additionally, to further enhance the model's performance, we have designed the patch dropping (PD) module. This module aims to emulate the attention allocation mechanism of the human visual system for local features, guiding the network to focus on the most discriminative features, reducing the influence of irrelevant features, and intuitively lowering computational costs. Extensive experiments demonstrate that our approach significantly outperforms other methods, achieving an accuracy of 86.51% on RAF-DB and nearly 70% on FER2013, with a model size of only 3.64 MB. These results demonstrate that our method provides a new perspective for the field of facial expression recognition.

Feature selection for label distribution learning based on the statistical distribution of data and fuzzy mutual information

Label distribution learning (LDL) is an emerging framework in machine learning. Fuzzy mutual information is mutual information under a fuzzy environment and plays an important role in handling uncertainty. This paper explores feature selection for LDL data based on the statistical distribution of data and fuzzy mutual information. The similarity between the feature values in the feature space is first defined by means of the statistical distribution of the data, and a threshold is introduced to control the similarity. Then, the fuzzy similarity relation for each feature subset is established via the similarity. This method utilizes adjustable fuzzy similarity radii to establish a fuzzy similarity relation and improve the classification ability of the data. The decision relation in the label space is then presented, and the decision class of each sample is constructed. Subsequently, two feature selection algorithms based on fuzzy mutual information are designed to remove the irrelevant features by employing a strategy that considers the correlation between the features and labels as well as the redundancy between the features in the LDL data. Finally, the experimental results show that the designed algorithms can effectively measure the uncertainty of LDL data and outperform four state-of-the-art feature selection algorithms. Specifically, our algorithms, LDFM and LDFMR, demonstrate their superiority by achieving overall average ranking improvements of 63.64% and 58.52%, respectively, across six evaluation metrics compared to the other four algorithms.

Transfer of visual perceptual learning over a task-irrelevant feature through feature-invariant representations: Behavioral experiments and model simulations.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.

Weed detection and recognition in complex wheat fields based on an improved YOLOv7.

The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields. First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd's purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCW-YOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed. The ablation experiment results showed that the CSCW-YOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications.