Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

Hu, Jennifer Shueh-Inn; Jagtap, Rushikesh; Ravichandran, Rishikumar; Moorthy, Chitra Priyaa Sathya; Sobol, Nataliya; Wu, Jane Y.; Gao, Jerry

doi:10.3390/atmos14050771

Cited by 6 publications

(1 citation statement)

References 58 publications

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“…The findings indicated that YOLO variants outperformed the other networks, with the one-stage detection network YOLOv4 achieving the best performance (mAP of 91.29%). Hu et al [21] conducted a study utilising Detectron2, RetinaNet, YOLOv4, and YOLOv5 models to determine the count of cattle in satellite images, with YOLOv5 achieving the best results, producing an average precision of 91.60% and a recall of 91.20%. Both studies by Roy et al [20] and Hu et al [21] demonstrate the effectiveness of the YOLO family in animal detection.…”

Section: Machine Learning and Deep Learningmentioning

confidence: 99%

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

Afonso,

Lopes,

Ribeiro

2024

Sensors

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The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal’s body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process. This project aims to automate this acquisition process through the automatic recognition of parts of a lizard’s body, reading the temperature in these parts based on a video taken with two cameras simultaneously: an RGB camera and a thermal camera. The first camera detects the location of the lizard’s various body parts using artificial intelligence techniques, and the second camera allows reading of the respective temperature of each part. Due to the lack of lizard datasets, either in the biology laboratory or online, a dataset had to be created from scratch, containing the identification of the lizard and six of its body parts. YOLOv5 was used to detect the lizard and its body parts in RGB images, achieving a precision of 90.00% and a recall of 98.80%. After initial calibration, the RGB and thermal camera images are properly localised, making it possible to know the lizard’s position, even when the lizard is at the same temperature as its surrounding environment, through a coordinate conversion from the RGB image to the thermal image. The thermal image has a colour temperature scale with the respective maximum and minimum temperature values, which is used to read each pixel of the thermal image, thus allowing the correct temperature to be read in each part of the lizard.

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Section: Machine Learning and Deep Learningmentioning

confidence: 99%

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

Afonso,

Lopes,

Ribeiro

2024

Sensors

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Photothermal-Induced Multimodal Antibacterial Dressing Comprising N-Halamine Hydrogel Loaded with Cow Dung Biochar for Infected Wound Healing

Chu,

Zhao,

Yuan

et al. 2024

ACS Appl. Mater. Interfaces

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Disposal of the cow dung pollutants arising from cattle farming threatens the environment and public safety in diverse ways. To date, researchers have worked on developing new pathways to control and manage cattle farming wastes, but most do not involve the reuse of these wastes. Herein, a cow dung biocharmodulated photothermal N-halamine hydrogel (i.e., PAN/CA/ CoBC/pMAG−Cl) was designed for converting cow dung into biochar (CoBC), which was then coupled with a 3D interpenetrating hydrogel network to treat infected wounds. The PAN/ CA/CoBC/pMAG−Cl hydrogel exhibited excellent synergistic antibacterial performance against 10 6 CFU•mL −1 Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) within 60 min. The bactericidal effect was multimodal, involving CoBC-based photothermal killing (i.e., temperature as high as 80.5 °C) after 808 nm near-infrared light irradiation for 10 min, contact killing through the strong oxidative characteristic of N-halamine (pMAG−Cl), and release killing via active halogens (i.e., Cl + ) reinforced by the photothermal action of CoBC. The S. aureus-infected wound model in vivo demonstrated that the PAN/CA/CoBC/pMAG−Cl hydrogel worked as an ideal wound dressing, capable of resisting bacterial infection, accelerating the healing process, and promoting epithelial regeneration. This proposed strategy could indicate a new way for the disposal of cow dung pollutant and its reuse in antibacterial-associated applications.

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CNN-LSTM framework to automatically detect anomalies in farmland using aerial images from UAVs

Dewangan,

Vij

2024

BIO Web Conf.

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Using aerial inspection techniques in farmlands can yield vital data instrumental in mitigating various impediments to optimizing farming practices. Farmland anomalies (standing water and clusters of weeds) can impede farming practices, leading to the improper utilization of farmland and the disruption of agricultural development. Utilizing Unmanned Aerial Vehicles (UAVs) for remote sensing is a highly effective method for obtaining extensive imagery of farmland. Visual data analytics in the context of automatic pattern recognition from collected data is valuable for advancing Deep Learning (DL) -assisted farming models. This approach shows significant potential in enhancing agricultural productivity by effectively capturing crop patterns and identifying anomalies in farmland. Furthermore, it offers prospective solutions to address the inherent barriers farmers encounter. This study introduces a novel framework, namely the hybrid Convolutional Neural Networks and Long Short-Term Memory (HCNN-LSTM), which aims to detect anomalies in farmland using images obtained from UAVs automatically. The system employs a Convolutional Neural Network (CNN) for deep feature extraction, while Long Short-Term Memory (LSTM) is utilized for the detection task, leveraging the extracted features. By integrating these two Deep Learning (DL) architectures, the system attains an extensive knowledge of farm conditions, facilitating the timely identification of irregularities such as the presence of water, clusters of weeds, nutrient deficit, and crop disease. The proposed methodology is trained and evaluated using the Agriculture-Vision challenge database. The results obtained from the experiment demonstrate that the proposed system has achieved a high level of accuracy, with a value of 99.7%, confirming the effectiveness of the proposed approach.

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Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

Cited by 6 publications

References 58 publications

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence

Photothermal-Induced Multimodal Antibacterial Dressing Comprising N-Halamine Hydrogel Loaded with Cow Dung Biochar for Infected Wound Healing

CNN-LSTM framework to automatically detect anomalies in farmland using aerial images from UAVs

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