Autonomic computing in a beef-production process for Precision Livestock Farming

García, Ricardo Hernández; Aguilar, José; Toro, Mauricio; Pérez, Nelson; Pinto, Angel; Rodríguez, Paul

doi:10.1016/j.jii.2022.100425

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Singh et al ( 2021) hicieron una revisión de literatura que les permitió demostrar que el uso de sensores mejorados y tecnologías de datos con soluciones basadas en expertos trae una mejora sustancial en las prácticas ganaderas lecheras existentes. b. Optimización de la producción animal: García et al (2023) desarrollaron una arquitectura con tres ciclos autónomos de tareas de análisis de datos que permiten a los productores de carne tener una adecuada coordinación, optimización y planificación del proceso productivo. Tedeschi (2022) concluye que, a pesar de que la IA puede ayudar a optimizar los procesos de producción animal, se necesitan sistemas de monitoreo robustos, análisis de big data y algoritmos de inteligencia artificial avanzados para el desarrollo futuro de sistemas basados en modelos dinámicos y autocalibrables.…”

Section: Posibilidades De Usos De Chatgpt En El Campo Pecuariounclassified

The language model based on sensitive artificial intelligence -ChatGPT: Bibliometric analysis and possible uses in agriculture and livestock

Siché

Siche²

2023

Sci. agropecu.

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ChatGPT adds to the list of artificial intelligence-based systems designed to perform specific tasks and answer questions by interacting with users (Apple's Siri, Amazon's Alexa, Google's Assistant and Bard, Microsoft's Cortana, IBM's Watson, Bixby from Samsung, among others). ChatGPT works using OpenAI's GPT (Generative Pretrained Transformer) language model and is capable of learning from users' preferences and behavior patterns to customize its response. ChatGPT has the potential to be applied in different fields, including education, journalism, scientific writing, communication, cell biology, and biotechnology, where there is already evidence. The aim of this work was to analyze the possible applications of ChatGPT in the agricultural and livestock industry. First, a scientometric analysis was performed with VosViewer and Bibliometrix (Bliblioshiny). 3 clusters were identified: (a) Main characteristics; (b) learning systems you use; and (c) applications. To the question: What are the main applications in which ChatGTP will revolutionize agriculture (or livestock) in the world? ChatGPT responded: (a) in the agricultural field: improvement of agricultural decision-making, optimization of agricultural production, detection and prevention of plant diseases, climate management, and supply chain management; and (b) in the livestock field: improvement of animal health and welfare, optimization of animal production, supply chain management, detection and prevention of zoonotic diseases, and climate management for animal production. ChatGPT does not scientifically support its answer, but from the analysis carried out, we find that there is enough scientific evidence to conclude, in this case, that its answers were correct. While ChatGPT does not necessarily scientifically substantiate its answers, users should. There is a lack of studies on the use of Artificial Intelligence and its relationship with ethics.

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Section: Posibilidades De Usos De Chatgpt En El Campo Pecuariounclassified

The language model based on sensitive artificial intelligence -ChatGPT: Bibliometric analysis and possible uses in agriculture and livestock

Siché

Siche²

2023

Sci. agropecu.

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“…Using real-time data from Artificial Neural Networks (ANN), Arshad et al [19] hopes to predict illnesses in cattle and present the anticipated findings to authorised staff via a web application. Since this system may be hacked and needs strong network security to keep its resources private, secure, and always available, it employs a number of widely used authentication methods.…”

Section: Related Workmentioning

confidence: 99%

Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT

Krishnan,

Siva,

Hemamalini

et al. 2023

IJRITCC

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Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods.

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“…They implement a prototype and evaluate it on synthetic treatment data against malignant neoplastic diseases. Finally, previous work has determined that machine learning is essential to provide self-management capability in a beef production farm [13].…”

Section: Related Workmentioning

confidence: 99%

Management System for the Fattening Process of Bovines in Rotational Grazing using Diagnosis and Recommendation Systems

Garcia,

Benitez,

Aguilar

2023

CLEIej

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Cattle breeding has been one of the most important industrial sectors in the world since it is related to food security and the survival of the human race. Management of the cattle fattening process is a fundamental procedure for cattle breeders because it allows them to make strategic decisions, such as timely treatment in case of any abnormality (e.g., weight gain in herds, in their paddocks). This article aims to present a management system for the cattle fattening process under a rotational grazing scheme, considering the health status of the animal and the pasture, which should diagnose weight loss or gain in bovines and recommend actions when is required. The diagnostic process is based on a fuzzy system that defines rules that characterize the diagnostic process to determine the current situation given an input. Furthermore, the fuzzy classifier optimizes its rules by means of genetic algorithms by modifying its membership functions, providing a more accurate system for diagnosis. On the other hand, the recommendation system is based on a classification model of pasture crops, in which the best pasture is recommended given the soil variables. We tested our proposal with experimental cases, with promising results. For the fuzzy classifier, the accuracy metrics are very good, with values of accuracy close to 100% and of Area Under the Curve close to 1. For the classification model were used several machine learning techniques, resulting in the best classifier the random forest technique, with an accuracy of 98.61%.

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Autonomic computing in a beef-production process for Precision Livestock Farming

Cited by 5 publications

References 24 publications

The language model based on sensitive artificial intelligence -ChatGPT: Bibliometric analysis and possible uses in agriculture and livestock

The language model based on sensitive artificial intelligence -ChatGPT: Bibliometric analysis and possible uses in agriculture and livestock

Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT

Management System for the Fattening Process of Bovines in Rotational Grazing using Diagnosis and Recommendation Systems

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