A wireless intelligent thermal control and management system for piglet in large-scale pig farms

Zheng, Ping; Zhang, Jicheng; Liu, Honggui; Bao, Jun; Xie, Qing; Xiao-hua, Teng

doi:10.1016/j.inpa.2020.09.001

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…Embedded automation control system is a controller with industrial control machine as the core with the help of ARM embedded technology, realizing various program control instruments and equipment, bus interface for information exchange and transmission, and test software that accepts and executes test tasks. ere are five parts of various devices that need to be tested [3]. e automated test systems cooperate with each other in the process of testing and play a role together to complete the high-efficiency test [4].…”

Section: Related Workmentioning

confidence: 99%

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Gong

2022

Journal of Electrical and Computer Engineering

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In order to improve the automatic test effect of the electronic controller, this paper combines the intelligent control algorithm to construct the automatic test system of the electronic controller. Moreover, the paper makes a detailed theoretical analysis of several basic problems relating to signal integrity and gives a general method to solve the main SI problems. In addition, this paper analyzes the principle of the microstrip transmission line parameter model and its relationship with the signal integrity and constructs an automated test system for an intelligent electronic controller. The simulation test results show that the electronic controller automated test system based on the intelligent control algorithm proposed in this paper can play an important role in the electronic controller automated test.

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Section: Related Workmentioning

confidence: 99%

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Gong

2022

Journal of Electrical and Computer Engineering

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“…It is a type of automatic control that can independently drive intelligent machines to achieve their goals without human intervention. In addition, its creation and development require a high degree of integration and utilization of contemporary multiple frontier disciplines, multiple advanced technologies, and multiple scientific methods [3].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Discrepancies in scope (2) Discrepancies in the description of the research reported (3) Discrepancies between the availability of data and the research described (4) Inappropriate citations (5) Incoherent, meaningless and/or irrelevant content included in the article (6) Manipulated or compromised peer review Te presence of these indicators undermines our confdence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable.…”

mentioning

confidence: 99%

Automatic Intelligent Control System Based on Intelligent Control Algorithm

Huang

2022

Journal of Electrical and Computer Engineering

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In order to improve the effect of automatic intelligent control, this paper improves the intelligent vector control algorithm, analyzes the performance of the algorithm with common power pulses as an example, and obtains an improved intelligent vector control algorithm suitable for modern intelligent systems. By making two mutually adjacent vectors nonidentical combination in time, the two vectors have the difference between zero and nonzero, and the vector can be rotated into a certain area. In addition, with the support of improved algorithms, this paper gets an automated intelligent control system based on intelligent control algorithms. Finally, this paper verifies the effect of the intelligent control system through experimental research. The analysis of the test results shows that the automated intelligent control system proposed in this paper has a better effect in intelligent control.

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“…Although the above method can complete the automatic identification and extraction of the pig temperature, there are problems. For example, the detection method is too complicated, incomplete in one step, and easily affected by the pig breeding environment regarding the part identification effect [16,17] .…”

Section: Challengesmentioning

confidence: 99%

Pig-ear detection from the thermal infrared image based on improved YOLOv8n

Han,

Xue,

et al. 2024

Intelligence & Robotics

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In the current pig scale breeding process, considering the low accuracy and speed of the infrared thermal camera automatic measurement concerning the pig body surface temperature, this paper proposes an improved algorithm for target detection of the pig ear thermal infrared image based on the YOLOv8n model. The algorithm firstly replaces the standard convolution in the CSPDarknet-53 and neck network with Deformable Convolution v2, so that the convolution kernel can adjust its shape according to the actual situation, thus enhancing the extraction of input features; secondly, the Multi-Head Self-Attention module is integrated into the backbone network, which extends the sensory horizons of the backbone network; finally, the Focal-Efficient Intersection Over Union loss function was introduced into the loss of bounding box regression, which increases the Intersection Over Union loss and gradient of the target and, in turn, improves the accuracy of the bounding box regression. Apart from that, a pig training set, including 3,000 infrared images from 50 different individual pigs, was constructed, trained, and tested. The performance of the proposed algorithm was evaluated by comparing it with the current mainstream target detection algorithms, such as Faster-RCNN, SSD, and YOLO families. The experimental results showed that the improved model achieves 97.0%, 98.1% and 98.5% in terms of Precision, Recall and mean Average Precision, which are 3.3, 0.7 and 4.7 percentage points higher compared to the baseline model. At the same time, the detection speed can reach 131 frames per second, which meets the requirement of real-time detection. The research results show that the improved pig ear detection method based on YOLOv8n proposed in this paper can accurately locate the pig ear in thermal infrared images and provide a reference and basis for the subsequent pig body temperature detection.

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A wireless intelligent thermal control and management system for piglet in large-scale pig farms

Cited by 9 publications

References 26 publications

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Electronic Controller Automatic Test System Based on Intelligent Control Algorithm

Automatic Intelligent Control System Based on Intelligent Control Algorithm

Pig-ear detection from the thermal infrared image based on improved YOLOv8n

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