• Title/Summary/Keyword: Pig ICT

Search Result 4, Processing Time 0.017 seconds

Implementation of Feeding Management Service Model based on Pig Raising Data (양돈 데이터 기반의 급이 관리 서비스 모델 구현)

  • Kim, Bong-Hyun
    • Journal of Digital Convergence
    • /
    • v.19 no.10
    • /
    • pp.105-110
    • /
    • 2021
  • The pig ICT automatic feeder is capable of automatically feeding feed, etc. according to the set conditions. However, there is a disadvantage that the setting condition itself must depend on the user's experience. Therefore, trial and error is caused, and there is a problem that the efficiency is lowered. Therefore, it is necessary to develop a system and implement a service model that can improve pig productivity by suggesting optimal feeding setting conditions based on data. Therefore, in this paper, a pig feeding management service model was developed using the performance analysis program such as the existing feeding data, breeding management data, and pig production management system. Through this, we developed a consumer-oriented feed management service model that can be efficiently utilized by analyzing pig data. In addition, it is possible to provide a service that contributes to a decrease in the mortality rate and an increase in the MSY of the farms with the intelligent automatic feeding management service, thereby improving the productivity of the pig farms and thereby increasing the income of the pig farms.

A Case Study on Smart Livestock with Improved Productivity after Information and Communications Technologies Introduction

  • Kim, Gok Mi
    • International Journal of Advanced Culture Technology
    • /
    • v.9 no.1
    • /
    • pp.177-182
    • /
    • 2021
  • The fourth industrial revolution based on information and communication technology (ICT) becomes the center of society, and the overall industrial structure is also changing significantly. ICT refers to the hardware of information devices and the software technologies required for the operation and information management of these devices, and any means of collecting, producing, processing, preserving, communicating and utilizing them. ICT is integrated into industries and services or combined with new technologies in various fields such as robotics and nanotechnology to connect all products and services to the network. The development of ICT, which continuously creates new products and services, has spread to all sectors of the industry, affecting not only daily life but also the livestock sector recently. In agriculture, ICT technology can reduce production costs by efficiently managing labor and energy because it can improve quality and yield based on data on environmental and growth information such as temperature, humidity, light and soil. In particular, smart livestock is considered suitable for achieving livestock management goals because it can reduce labor force and improve productivity by remotely and automatically managing accurate information necessary for raising and breeding livestock with ICT devices. The purpose of this study is to propose the need for ICT technology by comparing farm productivity before and after ICT is introduced. The method of the study is to compare the productivity before and after the introduction of ICT in Korean beef farms, pig farms, and poultry farms. The effectiveness of the study proved the excellence of ICT technology through the production results before ICT introduction and the productivity improvement case of livestock farms that efficiently operated manpower management and reduced labor force after ICT introduction. The conclusion of this paper is to present the need for smart livestock through ICT adoption through case study results.

A Study on Analysis of Problems in Data Collection for Smart Farm Construction (스마트팜 구축을 위한 데이터수집의 문제점 분석 연구)

  • Kim Song Gang;Nam Ki Po
    • Convergence Security Journal
    • /
    • v.22 no.5
    • /
    • pp.69-80
    • /
    • 2022
  • Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

Deep-learning-based gestational sac detection in ultrasound images using modified YOLOv7-E6E model

  • Tae-kyeong Kim;Jin Soo Kim;Hyun-chong Cho
    • Journal of Animal Science and Technology
    • /
    • v.65 no.3
    • /
    • pp.627-637
    • /
    • 2023
  • As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.