Predicting of mutton sheep stress coupled with multi-environment sensing and supervised learning network in the transportation process

Zhang, Mengjie; Feng, Huanhuan; Tomka, J.; Polovka, Martin; Ma, Ruiqin; Zhang, Xiaoshuan

doi:10.1016/j.compag.2021.106422

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…At this stage, data integration will be the next step for the use of the CURO MkII. Data need to be processed using a static algorithm, artificial intelligence, or machine learning [ 16 ]. This future application could predict a cow’s status based on her previous data recording and generate a forecast of the desired status for an animal at a given stage of the production cycle [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

A Non-Invasive Sound Technology to Monitor Rumen Contractions

Vargas‐Bello‐Pérez

Neves

Harrison

2022

Animals

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This technical report used a wireless device (CURO MkII) that recorded high-quality rumen sound waves from cows of different production statuses (dry cow vs. lactating cow) and physiological stages (pregnant vs. non-pregnant). Recordings from a dry Jersey heifer fed a diet based on haylage and straw showed a few high-amplitude spikes (3 at 6 dB) but mostly infrequent signals (9 at 12 dB and 22 at 18 dB), with pauses of approx. 2 min with no rumen sounds in between. Analysis of a few individual spikes in the 12 dB range showed that wave frequencies ranged from 230 to 250 Hz and lasted 4 s. Recordings of the high-yielding Red Danish cow fed a total mixed ration (TMR) showed an almost constant frequency of the rumen sounds with considerable amplitude of the waves. Rumen sounds from the Red Danish dry and pregnant cow fed on TMR were less frequent, with a lower amplitude than those from the high-yielding cow. These preliminary results demonstrate that wireless sound recording units are capable of measuring rumen sounds in a production setting and can discern between animals of different production and physiological stages, but more studies are needed to confirm our findings.

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Section: Discussionmentioning

confidence: 99%

A Non-Invasive Sound Technology to Monitor Rumen Contractions

Vargas‐Bello‐Pérez

Neves

Harrison

2022

Animals

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“…The reviewed literature highlights various opportunities for advancing agricultural practices. Authors [76,206,234,235] emphasize the potential for enhancing crop-specific interventions, such as implementing dynamic nutrient delivery, as suggested by [206], and incorporating additional parameters influencing crops, as proposed by [234]. Additionally, expanding IoT applications along the coffee value chain, as recommended by [76], introduces possibilities for traceability and disease control.…”

Section: Agriculture Opportunitiesmentioning

confidence: 99%

“…Additionally, expanding IoT applications along the coffee value chain, as recommended by [76], introduces possibilities for traceability and disease control. Moreover, the inclusion of biosensors [235] and 24 h tracking of animals [128] exemplifies the opportunities for livestock monitoring, emphasizing the potential for real-time insights and improved animal welfare.…”

Section: Agriculture Opportunitiesmentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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“…Bacteria breed and cause ectoparasitic and respiratory system diseases in sheep; if the temperature is too low, it is detrimental to a lamb’s health and survival. The proportion of feed consumed to maintain body temperature increases, negatively affecting sheep production and reproductive performance [ 4 ]. However, the temperature in the sheep house is time-varying, lags, exhibits significant interference, and exhibits multiple coupling [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Temperature Prediction in a Sheep House

Feng

Hassan

et al. 2022

Animals

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Too high or too low temperature in the sheep house will directly threaten the healthy growth of sheep. Prediction and early warning of temperature changes is an important measure to ensure the healthy growth of sheep. Aiming at the randomness and empirical problem of parameter selection of the traditional single extreme Gradient boosting (XGBoost) model, this paper proposes an optimization method based on Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO). Then, using the proposed PCA-PSO-XGBoost to predict the temperature in the sheep house. First, PCA is used to screen the key influencing factors of the sheep house temperature. The dimension of the input vector of the model is reduced; PSO-XGBoost is used to build a temperature prediction model, and the PSO optimization algorithm selects the main hyperparameters of XGBoost. We carried out a global search and determined the optimal hyperparameters of the XGBoost model through iterative calculation. Using the data of the Xinjiang Manas intensive sheep breeding base to conduct a simulation experiment, the results show that it is different from the existing ones. Compared with the temperature prediction model, the evaluation indicators of the PCA-PSO-XGBoost model proposed in this paper are root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), mean absolute error (MAE) are 0.0433, 0.0019, 0.9995, 0.0065, respectively. RMSE, MSE, and MAE are improved by 68, 90, and 94% compared with the traditional XGBoost model. The experimental results show that the model established in this paper has higher accuracy and better stability, can effectively provide guiding suggestions for monitoring and regulating temperature changes in intensive housing and can be extended to the prediction research of other environmental parameters of other animal houses such as pig houses and cow houses in the future.

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Predicting of mutton sheep stress coupled with multi-environment sensing and supervised learning network in the transportation process

Cited by 8 publications

References 40 publications

A Non-Invasive Sound Technology to Monitor Rumen Contractions

A Non-Invasive Sound Technology to Monitor Rumen Contractions

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

A Hybrid Model for Temperature Prediction in a Sheep House

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