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Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture 被引量:1
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作者 R.Punithavathi A.Delphin Carolina Rani +4 位作者 K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2759-2774,共16页
Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital ... Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished 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 prop-erly 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 out-comes over its recent approaches interms of several measures. 展开更多
关键词 Precision agriculture smart farming weed detection computer vision deep learning
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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture
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作者 Saud Yonbawi Sultan Alahmari +4 位作者 T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1533-1547,共15页
Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current... Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%. 展开更多
关键词 weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning
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Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture
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作者 Jigna Patel Anand Ruparelia +5 位作者 Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu 《Computers, Materials & Continua》 SCIE EI 2023年第10期1281-1301,共21页
The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The... The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data. 展开更多
关键词 Precision Agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN
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Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network 被引量:8
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作者 Muhammad Hamza Asad Abdul Bais 《Information Processing in Agriculture》 EI 2020年第4期535-545,共11页
Herbicide use is rising globally to enhance food production,causing harm to environment and the ecosystem.Precision agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse ... Herbicide use is rising globally to enhance food production,causing harm to environment and the ecosystem.Precision agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse effects of herbicides.Accurate weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture data.Labelling large agriculture data at pixel level is a time-consuming and tedious job.In this paper,a methodology is developed to accelerate manual labelling of pixels using a two-step procedure.In the first step,the background and foreground are segmented using maximum likelihood classification,and in the second step,the weed pixels are manually labelled.Such labelled data is used to train semantic segmentation models,which classify crop and background pixels as one class,and all other vegetation as the second class.This paper evaluates the proposed methodology on high-resolution colour images of canola fields and makes performance comparison of deep learning meta-architectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50.ResNet-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869. 展开更多
关键词 weed detection Semantic segmentation Variable rate herbicide Maximum likelihood classification
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Weed Recognition Using Image-Processing Technique Based on Leaf Parameters 被引量:5
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作者 Kamal N. Agrawal Karan Singh +1 位作者 Ganesh C. Bora Dongqing Lin 《Journal of Agricultural Science and Technology(B)》 2012年第8期899-908,共10页
Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabil... Weeds normally grow in patches and spatially distributed in field. Patch spraying to control weeds has advantages of chemical saving, reduced cost and environmental pollution. Advent of electro-optical sensing capabilities has paved the way of using machine vision technologies for patch spraying. Machine vision system has to acquire and process digital images to make control decisions. Proper identification and classification of objects present in image holds the key to make control decisions and use of any spraying operation performed. Recognition of objects in digital image may be affected by background, intensity, image resolution, orientation of the object and geometrical characteristics. A set of 16, including 11 shape and 5 texture-based parameters coupled with predictive discriminating analysis has been used to identify the weed leaves. Geometrical features were indexed successfully to eliminate the effect of object orientation. Linear discriminating analysis was found to be more effective in correct classification of weed leaves. The classification accuracy of 69% to 80% was observed. These features can be utilized for development of image based variable rate sprayer. 展开更多
关键词 Machine vision weed detection IMAGE-PROCESSING leaf parameters.
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A comprehensive survey on weed and crop classification using machine learning and deep learning
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作者 Faisal Dharma Adhinata Wahyono Raden Sumiharto 《Artificial Intelligence in Agriculture》 2024年第3期45-63,共19页
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 trans... 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 classifyingweed 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. 展开更多
关键词 Deep learning Machine learning Precision agriculture weed control weed detection
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Review of deep learning-based weed identification in crop fields
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作者 Wenze Hu Samuel Oliver Wane +4 位作者 Junke Zhu Dongsheng Li Qing Zhang Xiaoting Bie Yubin Lan 《International Journal of Agricultural and Biological Engineering》 SCIE 2023年第4期1-10,共10页
Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provid... Automatic weed identification and detection are crucial for precision weeding operations.In recent years,deep learning(DL)has gained widespread attention for its potential in crop weed identification.This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL.Through an analysis of relevant literature from both within and outside of China,the author summarizes the development history,research progress,and identification and detection methods of DL-based weed identification technology.Emphasis is placed on data sources and DL models applied to different technical tasks.Additionally,the paper discusses the challenges of time-consuming and laborious dataset preparation,poor generality,unbalanced data categories,and low accuracy of field identification in DL for weed identification.Corresponding solutions are proposed to provide a reference for future research directions in weed identification. 展开更多
关键词 deep learning weed detection weed classification CATION image segmentation Convolutional Neural Network image processing
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Evaluation of model generalization for growing plants using conditional learning
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作者 Hafiz Sami Ullah Abdul Bais 《Artificial Intelligence in Agriculture》 2022年第1期189-198,共10页
This paper aims to solve the lack of generalization of existing semantic segmentation models in the crop and weed segmentation domain.We compare two training mechanisms,classical and adversarial,to understand which sc... This paper aims to solve the lack of generalization of existing semantic segmentation models in the crop and weed segmentation domain.We compare two training mechanisms,classical and adversarial,to understand which scheme works best for a particular encoder-decoder model.We use simple U-Net,SegNet,and DeepLabv3+with ResNet-50 backbone as segmentation networks.The models are trained with cross-entropy loss for classical and PatchGAN loss for adversarial training.By adopting the Conditional Generative Adversarial Network(CGAN)hierarchical settings,we penalize different Generators(G)using PatchGAN Discriminator(D)and L1 loss to generate segmentation output.The generalization is to exhibit fewer failures and perform comparably for growing plants with different data distributions.We utilize the images from four different stages of sugar beet.We divide the data so that the full-grown stage is used for training,whereas earlier stages are entirely dedicated to testing the model.We conclude that U-Net trained in adversarial settings is more robust to changes in the dataset.The adversarially trained U-Net reports 10%overall improvement in the results with mIOU scores of 0.34,0.55,0.75,and 0.85 for four different growth stages. 展开更多
关键词 weed detection Semantic segmentation Adversarial training Late germination Sugar beet Crop segmentation Growing plants Domain change
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