Weed Identification Research Articles

Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review

The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm

Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN algorithm for the soybean seedling. Three types of weed datasets, including soybean, with a total of 9816 photos were constructed, and cell phone photo data were used for training and recognition. Firstly, by comparing the classification effects of ResNet50, VGG16, and VGG19, VGG19 was identified as the best backbone feature extraction network for model training. Secondly, an attention mechanism was embedded after the pooling layer in the second half of VGG19 to form the VGG19-CBAM structure, which solved the problem of low attention to the attention target during model training using the trained Faster R-CNN algorithm to identify soybean and weeds in the field under the natural environment and compared with two classical target detection algorithms, SSD and Yolov4. The experimental results show that the Faster R-CNN algorithm using VGG19-CBAM as the backbone feature extraction network can effectively identify soybeans and weeds in complex backgrounds. The average recognition speed for a single image is 336 ms, and the average recognition accuracy is 99.16%, which is 5.61% higher than before optimization, 2.24% higher than the SSD algorithm, and 1.24% higher than the Yolov4 algorithm. Therefore, this paper’s optimized target detection model is advantageous and can provide a scientific method for accurate identification and monitoring of grass damage.

Tropical Field Observation of Weed Permanent Shading on Solar PV Surface

Solar Photovoltaic (PV) largely depends on the sun irradiation or insolation level (in W/m2) on its surface for electricity conversion process. Cloud movement especially thick cloud near to earth surface creates a non-permanent shading on solar PV farms top surface which significantly reduce the electricity yield. Improper weed management in large scale solarfarms would create a permanent shading to the PV surface especially with creeping plants. Thus, this work implies freely available application namely Pl@ntNet and Canopeo to analyse the impacts of weed surface cover in Solarfarms. Images of specified weed growing above the solar PV surface are captured and identified using Pl@ntNet application to determine the type of weed. Weed identification is the first stage of efficient weed management to aid in a fundamental understanding of the life cycle and biology of the weeds for proper control measures. The same images are used in Canopeo application to determine the surface coverage by means of Quadrat sampling. This information will be invaluable to solarfarm operators showing the significant energy reduction when the solar PV surface are covered by weeds.

A peanut and weed detection model used in fields based on BEM-YOLOv7-tiny.

Due to the different weed characteristics in peanut fields at different weeding periods, there is an urgent need to study a general model of peanut and weed detection and identification applicable to different weeding periods in order to adapt to the development of mechanical intelligent weeding in fields. To this end, we propose a BEM-YOLOv7-tiny target detection model for peanuts and weeds identification and localization at different weeding periods to achieve mechanical intelligent weeding in peanut fields at different weeding periods. The ECA and MHSA modules were used to enhance the extraction of target features and the focus on predicted targets, respectively, the BiFPN module was used to enhance the feature transfer between network layers, and the SIoU loss function was used to increase the convergence speed and efficiency of model training and to improve the detection performance of the model in the field. The experimental results showed that the precision, recall, mAP and F1 values of the BEM-YOLOv7-tiny model were improved by 1.6%, 4.9%, 4.4% and 3.2% for weed targets and 1.0%, 2.4%, 2.2% and 1.7% for all targets compared with the original YOLOv7-tiny. The experimental results of positioning error show that the peanut positioning offset error detected by BEM-YOLOv7-tiny is less than 16 pixels, and the detection speed is 33.8 f/s, which meets the requirements of real-time seedling grass detection and positioning in the field. It provides preliminary technical support for intelligent mechanical weeding in peanut fields at different stages.

Accurate Detection and Precision Spraying of Corn and Weeds Using the Improved YOLOv5 Model

The precise identification of corn and weeds plays an important role in precise spraying. This paper proposed a lightweight model based on the improved yolov5s and built a precision spraying robot. Firstly, we used a data augmentation method based on category balance and agronomic characteristics to solve the data imbalance problem. Then, compared with yolov5s, yolov5l, yolov5m, and yolov5x, we found that yolov5s has both real-time and accuracy and is easier to deploy the model on edge devices. Through the feature map visualization experiment, we found that the feature extraction network can’t pay close attention to the important feature of the target and suppress the feature of the noise. Therefore, we added the attention mechanism. In order to improve the real-time performance of the model, we designed the C3-Ghost-bottleneck module. Finally, we built a precision spraying robot. Compared with the original model, the value of map@0.5 is increased by 3.2%, the model file is reduced by 3.6 MB, the AP value for corn is increased from 93.2% to 96.3%, and the AP value for weeds is increased from 85.6% to 88.9%. Finally, the precision spraying experiment of weeds was carried out. The recognition accuracy of weeds is 83%, the probability of the spraying robot correctly identifying weeds and accurately spraying is 81%, and the detection speed is 30ms/f. The experimental results verify the feasibility of precision spraying weeding and the effectiveness of the improved model, which can provide a reference for the engineering application of precision weeding.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.

The Distributed Deep Learning Paradigms for Detection of Weeds from Crops in Indian Agricultural Farms

Weeds are a major threat to crops, making early detection critical for maintaining agricultural productivity. Weeds are generally toxic, equipped with thorns and burrs, and can disrupt crop management by contaminating harvests. This research aims to identify weeds in a field using image processing and deep learning techniques. Images were collected from an Indian farm and pre-processed using image processing techniques. The images were then analysed to extract features that distinguish between weed and crop properties. Traditional crop weed identification methods mainly focused on identifying weeds directly but weed species can vary significantly. This study proposes a method that combines deep learning and image processing technology. Identifying weeds in crops is a challenging task that has been addressed through image processing, feature extraction, and image labelling to train deep learning algorithms. The study examines the performance of various deep learning algorithms and convolution neural networks to detect weeds using images obtained from an Indian crop field. Once the input image is identified as a weed or not, the crop class prediction is made. These results could have significant implications for optimizing agricultural fertilizer usage, leading to increased crop yields and less environmental impact.

Rapid and Accurate Identification of Grass Seedlings in Agricultural Fields Based on Optimized YOLOX Model

Traditional agricultural cultivation is labor‐intensive and vulnerable to natural climate conditions, such as heavy rainfall and drought. Concerns over food safety have also brought attention to the growth of weeds and the misuse of agricultural chemicals, which can have a serious negative impact on crop growth and safety. We investigated the feasibility of the YoloX model in the field of agricultural weed identification to address the problem of weed handling in growing crops. In order to overcome the effects of climate and environment, we chose a purchased weed model for our study. We used a binocular vision camera for image acquisition and created a database containing 6,000 samples and enhanced the original database of 1,000 samples with data. In order to address the complex background of the weed images, the changing lighting environment of the binocular camera‐acquired images, and the noise interference, we performed histogram equalization, image denoising, and background processing on the dataset. These processing measures aim to improve the overall learning efficiency of the model in order to improve the accuracy of deep learning on weeds. Target detection platforms based on the TensorFlow and PyTorch frameworks were established, respectively, and the mainstream target detection models Faster R‐CNN and YoloX series target detection models were trained with the same dataset for comparative analysis using the longitudinal comparison method. The results show that the training under the PyTorch framework yields better models than the training under the TensorFlow framework. YoloX‐x has higher recognition accuracy, faster recognition speed, and more stable compared to the Faster R‐CNN model, with an average recognition rate of 97.07% and an average recognition time of 0.062 s. Moreover, the optimization of YoloX by incorporating an attention‐focusing mechanism resulted in an improved accuracy rate with a decrease in recognition time, with an average recognition rate of 97.70% and an average recognition time of 0.029 s.

Review of deep learning-based weed identification in crop fields

Review of deep learning-based weed identification in crop fields

A Novel Approach for Identification of Weeds in Paddy By using Deep Learning Techniques

Weed is an unwanted plant which is grown in agriculture land. The land which is not cultivated will be fully covered by Weeds. Management of weed is the major concern for farmer because the weed will reduce the crop production quantity. There are many methods to control the weeds, one of those methods is manual plucking which is expensive because it takes more time, consumes human work. Second is by applying any chemicals suggested by external experts. This may cause damage to the crop which is cultivated. Identifying weeds in early stage of crop growth and destroying them through proper method is most important for increasing the crop production. We proposed an efficient method for identifying and classifying weed in paddy field by using Deep learning-based computer vision techniques. We applied Semantic Segmentation model for classifying weeds in agriculture land. We trained our model with SegNet with different batch size of 16,32,64 and obtained a highest accuracy of 94.223 for dropout value 0.1 and batch size set to 32.

WeedGan: a novel generative adversarial network for cotton weed identification

WeedGan: a novel generative adversarial network for cotton weed identification

Weed25: A deep learning dataset for weed identification.

Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models-YOLOv3, YOLOv5, and Faster R-CNN-were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.

Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops

As the tomato (Solanum lycopersicum L.) is one of the most important crops worldwide, and the conventional approach for weed control compromises its potential productivity. Thus, the automatic detection of the most aggressive weed species is necessary to carry out selective control of them. Precision agriculture associated with computer vision is a powerful tool to deal with this issue. In recent years, advances in digital cameras and neural networks have led to novel approaches and technologies in PA. Convolutional neural networks (CNNs) have significantly improved the precision and accuracy of the process of weed detection. In order to apply on-the-spot herbicide spraying, robotic weeding, or precise mechanical weed control, it is necessary to identify crop plants and weeds. This work evaluates a novel method to automatically detect and classify, in one step, the most problematic weed species of tomato crops. The procedure is based on object detection neural networks called RetinaNet. Moreover, two current mainstream object detection models, namelyYOLOv7 and Faster-RCNN, as a one and two-step NN, respectively, were also assessed in comparison to RetinaNet. CNNs model were trained on RGB images monocotyledonous (Cyperus rotundus L., Echinochloa crus galli L., Setaria verticillata L.) and dicotyledonous (Portulaca oleracea L., Solanum nigrum L.) weeds. The prediction model was validated with images not used during the training under the mean average precision (mAP) metric. RetinaNet performed best with an AP ranging from 0.900 to 0.977, depending on the weed species. Faster-RCNN and YOLOv7 also achieved satisfactory results, in terms of mAP, particularly through data augmentation. In contrast to Faster CNN, YOLOv7 was less precise when discriminating monocot weed species. The results provide a better insight on how weed identification methods based on CNN can be made more broadly applicable for real-time applications.

Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform

Accurately identifying weeds in crop fields is key to achieving selective herbicide spraying. Weed identification is made difficult by the dense distribution of weeds and crops, which makes boundary segmentation at the overlap inaccurate, and thus pixels cannot be correctly classified. To solve this problem, this study proposes a soybean field weed recognition model based on an improved DeepLabv3+ model, which uses a Swin transformer as the feature extraction backbone to enhance the model’s utilization of global information relationships, fuses feature maps of different sizes in the decoding section to enhance the utilization of features of different dimensions, and adds a convolution block attention module (CBAM) after each feature fusion to enhance the model’s utilization of focused information in the feature maps, resulting in a new weed recognition model, Swin-DeepLab. Using this model to identify a dataset containing a large number of densely distributed weedy soybean seedlings, the average intersection ratio reached 91.53%, the accuracy improved by 2.94% compared with that before the improvement with only a 48 ms increase in recognition time, and the accuracy was superior to those of other classical semantic segmentation models. The results showed that the Swin-DeepLab network proposed in this paper can successfully solve the problems of incorrect boundary contour recognition when weeds are densely distributed with crops and incorrect classification when recognition targets overlap, providing a direction for the further application of transformers in weed recognition.

A Faster R-CNN-Based Model for the Identification of Weed Seedling

The accurate and rapid acquisition of crop and weed information is an important prerequisite for automated weeding operations. This paper proposes the application of a network model based on Faster R-CNN for weed identification in images of cropping areas. The feature pyramid network (FPN) algorithm is integrated into the Faster R-CNN network to improve recognition accuracy. The Faster R-CNN deep learning network model is used to share convolution features, and the ResNeXt network is fused with FPN for feature extractions. Tests using >3000 images for training and >1000 images for testing demonstrate a recognition accuracy of >95%. The proposed method can effectively detect weeds in images with complex backgrounds taken in the field, thereby facilitating accurate automated weed control systems.

Weeds Associated with Papaya Plants (Carica Papaya L.) in Bengkulu

Papaya crops associated with weeds are commonly found in Kepahiang and Rejang Lebong district, Bengkulu. The presence of weeds can affect the development and productivity of papaya. Until now, the species and diversity of weeds associated with papaya crops have not been reported. This study aimed to identify and determine weed density around papaya crops. Sampling was conducted to measure and calculate weed density using the quadratic method measuring 1 m x 1 m. The data collection was done by randomly taking the weeds on the plots at as many as 10 points. Based on the result of weed identification, there were 16 species of weeds spread over 9 families. The calculation of the highest relative density in Padang Lekat, Durian Depun Village, and Dua Jalur Road was Ageratum conyzoides (0,584%), Claytonia perfoliata (0,306%), and Eleusine indica (0,274%).

Weed Detection in Potato Fields Based on Improved YOLOv4: Optimal Speed and Accuracy of Weed Detection in Potato Fields

The key to precise weeding in the field lies in the efficient detection of weeds. There are no studies on weed detection in potato fields. In view of the difficulties brought by the cross-growth of potatoes and weeds to the detection of weeds, the existing detection methods cannot meet the requirements of detection speed and detection accuracy at the same time. This study proposes an improved YOLOv4 model for weed detection in potato fields. The proposed algorithm replaces the backbone network CSPDarknet53 in the YOLOv4 network structure with the lightweight MobileNetV3 network and introduces Depthwise separable convolutions instead of partial traditional convolutions in the Path Aggregation Network (PANet), which reduces the computational cost of the model and speeds up its detection. In order to improve the detection accuracy, the convolutional block attention module (CBAM) is fused into the PANet structure, and the CBAM will process the input feature map with a channel attention mechanism (CAM) and spatial attention mechanism (SAM), respectively, which can enhance the extraction of useful feature information. The K-means++ clustering algorithm is used instead of the K-means clustering algorithm to update the anchor box information of the model so that the anchor boxes are more suitable for the datasets in this study. Various image processing methods such as CLAHE, MSR, SSR, and gamma are used to increase the robustness of the model, which eliminates the problem of overfitting. CIoU is used as the loss function, and the cosine annealing decay method is used to adjust the learning rate to make the model converge faster. Based on the above-improved methods, we propose the MC-YOLOv4 model. The mAP value of the MC-YOLOv4 model in weed detection in the potato field was 98.52%, which was 3.2%, 4.48%, 2.32%, 0.06%, and 19.86% higher than YOLOv4, YOLOv4-tiny, Faster R-CNN, YOLOv5 l, and SSD(MobilenetV2), respectively, and the average detection time of a single image was 12.49ms. The results show that the optimized method proposed in this paper outperforms other commonly used target detection models in terms of model footprint, detection time consumption, and detection accuracy. This paper can provide a feasible real-time weed identification method for the system of precise weeding in potato fields with limited hardware resources. This model also provides a reference for the efficient detection of weeds in other crop fields and provides theoretical and technical support for the automatic control of weeds.

Research on weed identification method in rice fields based on UAV remote sensing.

Rice is the world's most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVI NIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVI NIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVI NIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVI NIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

YOLOX-based blue laser weeding robot in corn field.

A YOLOX convolutional neural network-based weeding robot was designed for weed removal in corn seedling fields, while verifying the feasibility of a blue light laser as a non-contact weeding tool. The robot includes a tracked mobile platform module, a weed identification module, and a robotic arm laser emitter module. Five-degree-of-freedom robotic arm designed according to the actual weeding operation requirements to achieve precise alignment of the laser. When the robot is in operation, it uses the texture and shape of the plants to differentiate between weeds and corn seedlings. The robot then uses monocular ranging to calculate the coordinates of the weeds using the triangle similarity principle, and it controls the end actuator of the robotic arm to emit the laser to kill the weeds. At a driving speed of 0.2 m·s-1 on flat ground, the weed robot's average detection rate for corn seedlings and weeds was 92.45% and 88.94%, respectively. The average weed dry weight prevention efficacy was 85%, and the average seedling injury rate was 4.68%. The results show that the robot can accurately detect weeds in corn fields, and the robotic arm can precisely align the weed position and the blue light laser is effective in removing weeds.

Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme

Site-specific weed detection and management is a crucial approach for crop production management and herbicide contamination mitigation in precision agriculture. With the advent of unmanned aerial vehicles (UAVs) and advances in deep learning techniques, it has become possible to identify and classify weeds from crops at desired spatial and temporal resolution. In this research, a faster region based convolutional neural network was implemented for the automatic weed identification and classification using a mixed crop farmland as a case study. A DJI phantom 4 UAV was used to simultaneously collect about 254 image pairs of the study site. The images were annotated before transferring them into google colaboratory where they were trained over five epochs; 10,000, 20,000, 100,000, 200,000, and 242,000 with the aim of detecting the point when the model flattens out in the process of automatically identifying and classifying the weeds. The neural network identified and classified five classes which are; sugarcane, spinach, banana, pepper, and weeds. Finally, the accuracy of the automatic weed classification was evaluated with the aid of the recorded loss function and confusion matrix, and the result shows that the implemented model gave a classification accuracy of 52.5%, weed precision of 50%, weed recall of 7.7% and F1 score of 71.6% at 10,000 epochs, classification accuracy of 67.8%, weed precision of 67%, weed recall of 52.4% and a F1 score of 85.9% at 20,000 epochs, classification accuracy of 97.2%, weed precision of 96.2%, weed recall of 97.5% and a F1 score of 99% at 100,000 epochs, classification accuracy of 98.3%, weed precision of 98.1%, weed recall of 99.1% and a F1 score of 99.4% at 200,000 epochs, and classification accuracy of 97%, weed precision of 95%, weed recall of 99% and a F1 score of 99% at 242,000 epochs. It was observed that the model's performance improves significantly with increase in the number of epochs but got saturated at 242,000 epochs. The findings showed that the faster RCNN is robust for automatic weed identification and classification in a mixed crop farm.