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Gómez-Astorga MJ, Villagra-Mendoza K, Masís-Meléndez F, Ruíz-Barquero A, Rimolo-Donadio R. Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels. SENSORS (BASEL, SWITZERLAND) 2024; 24:5958. [PMID: 39338703 PMCID: PMC11436195 DOI: 10.3390/s24185958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 09/08/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024]
Abstract
With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm-3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions.
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Affiliation(s)
- María José Gómez-Astorga
- Agricultural Engineering, CETIA Centro de Investigación y Extensión en Tecnología e Ingeniería Agrícola, Instituto Tecnológico de Costa Rica, Cartago P.O. Box 159-7050, Costa Rica
| | - Karolina Villagra-Mendoza
- Agricultural Engineering, CETIA Centro de Investigación y Extensión en Tecnología e Ingeniería Agrícola, Instituto Tecnológico de Costa Rica, Cartago P.O. Box 159-7050, Costa Rica
| | - Federico Masís-Meléndez
- Chemistry, CEQIATEC, Centro de Investigación y de Servicios Químicos y Microbiológicos, Instituto Tecnológico de Costa Rica, Cartago P.O. Box 159-7050, Costa Rica
| | - Aníbal Ruíz-Barquero
- Electronic Engineering, Instituto Tecnológico de Costa Rica, Cartago P.O. Box 159-7050, Costa Rica
| | - Renato Rimolo-Donadio
- Electronic Engineering, Instituto Tecnológico de Costa Rica, Cartago P.O. Box 159-7050, Costa Rica
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Rebez EB, Sejian V, Silpa MV, Kalaignazhal G, Thirunavukkarasu D, Devaraj C, Nikhil KT, Ninan J, Sahoo A, Lacetera N, Dunshea FR. Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock. SENSORS (BASEL, SWITZERLAND) 2024; 24:5890. [PMID: 39338635 PMCID: PMC11435989 DOI: 10.3390/s24185890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/24/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024]
Abstract
Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.
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Affiliation(s)
- Ebenezer Binuni Rebez
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Veerasamy Sejian
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | | | - Gajendirane Kalaignazhal
- Department of Animal Breeding and Genetics, College of Veterinary Science and Animal Husbandry, Odisha University of Agriculture and Technology, Bhubaneshwar 751003, India
| | - Duraisamy Thirunavukkarasu
- Department of Veterinary and Animal Husbandry Extension Education, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University, Namakkal 637002, India
| | - Chinnasamy Devaraj
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Kumar Tej Nikhil
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
| | - Jacob Ninan
- Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India
| | - Artabandhu Sahoo
- ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India
| | - Nicola Lacetera
- Department of Agriculture and Forest Sciences, University of Tuscia, 01100 Viterbo, Italy
| | - Frank Rowland Dunshea
- School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, Melbourne, VIC 3010, Australia
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Imada J, Arango-Sabogal JC, Bauman C, Roche S, Kelton D. Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests. Animals (Basel) 2024; 14:1113. [PMID: 38612352 PMCID: PMC11011002 DOI: 10.3390/ani14071113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs.
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Affiliation(s)
- Jamie Imada
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
| | - Juan Carlos Arango-Sabogal
- Département de Pathologie et Microbiologie, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, QC J2S 2M2, Canada;
| | - Cathy Bauman
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
| | - Steven Roche
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
- ACER Consulting, 100 Stone Rd West #101, Guelph, ON N1G 5L3, Canada
| | - David Kelton
- Department of Population Medicine, University of Guelph, Guelph, ON N1G 2W1, Canada; (J.I.); (C.B.); (S.R.)
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Siachos N, Neary JM, Smith RF, Oikonomou G. Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities. Vet J 2024; 304:106091. [PMID: 38431128 DOI: 10.1016/j.tvjl.2024.106091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.
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Affiliation(s)
- Nektarios Siachos
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Chester High Road, CH64 7TE, UK.
| | - Joseph M Neary
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Chester High Road, CH64 7TE, UK
| | - Robert F Smith
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Chester High Road, CH64 7TE, UK
| | - Georgios Oikonomou
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Chester High Road, CH64 7TE, UK
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Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H. Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5732. [PMID: 37420896 DOI: 10.3390/s23125732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.
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