Wavelet Frequency Transformation for Specific Weeds Recognition

Habib, Shaban; Khan, Ishrat; Islam, Muhammad; Albattah, Waleed; Alyahya, Saleh; Khan, Sheroz; Hassan, Mohammad Kamrul

doi:10.1109/caida51941.2021.9425249

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…However, their system is based on voice and movement analysis, which has a higher false-positive rate. Habib et al’s [ 21 ] model for identifying and categorizing brown- and yellow-rusted illnesses in wheat crops uses classical machine learning. To help coffee growers, Esgario et al [ 22 ] created a mobile app and a CNN model specifically designed to identify biotic stresses in coffee leaves.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Khan,

Malebary,

Dang

et al. 2024

Plants

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Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem.

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Section: Literature Reviewmentioning

confidence: 99%

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Khan,

Malebary,

Dang

et al. 2024

Plants

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“…(1) binary labels and ( 2) importance scores at the frame level. The outputs in binary labels are either keyframes [19,22,63,64] or keyshots [65][66][67][68]. Keyframes constitute a selection of non-consecutive frames chosen for summarization, while keyshots correspond to time intervals within a video, with each interval encompassing a continuous sequence of frames.…”

Section: Problem Formulationmentioning

confidence: 99%

Effective Video Summarization Using Channel Attention-Assisted Encoder–Decoder Framework

Alharbi,

Habib,

Albattah

et al. 2024

Symmetry

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A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework's exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets.

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“…AlexNet has outperformed traditional handcrafted techniques in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [23]. AlexNet performs exceptionally well in the image classification task; researchers from the computer vision community are exploring and using CNN in several problems: segmentation [24], object tracking [25], plant disease recognition [26], chest disease detection [27], activity identification [28], and other similar areas. The main advantage of CNN architecture is the local connection and weight sharing that helps in processing high-dimensional data and extracting meaningful discriminative features.…”

Section: Spatial Features Extractionmentioning

confidence: 99%

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network

Habib

Hussain

Albattah

et al. 2021

Sensors

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Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently. Problem: As a result, these cameras generate an enormous amount of visual data through manual or offline monitoring, requiring numerous human resources for efficient tracking. Therefore, there is an urgent need to develop an intelligent and automatic system in order to efficiently monitor crowds and identify abnormal activity. Method: The existing method is incapable of extracting discriminative features from surveillance videos as pre-trained weights of different architectures were used. This paper develops a lightweight approach for accurately identifying violent activity in surveillance environments. As the first step of the proposed framework, a lightweight CNN model is trained on our own pilgrim’s dataset to detect pilgrims from the surveillance cameras. These preprocessed salient frames are passed to a lightweight CNN model for spatial features extraction in the second step. In the third step, a Long Short Term Memory network (LSTM) is developed to extract temporal features. Finally, in the last step, in the case of violent activity or accidents, the proposed system will generate an alarm in real time to inform law enforcement agencies to take appropriate action, thus helping to avoid accidents and stampedes. Results: We have conducted multiple experiments on two publicly available violent activity datasets, such as Surveillance Fight and Hockey Fight datasets; our proposed model achieved accuracies of 81.05 and 98.00, respectively.

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Wavelet Frequency Transformation for Specific Weeds Recognition

Cited by 5 publications

References 6 publications

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Effective Video Summarization Using Channel Attention-Assisted Encoder–Decoder Framework

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network

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