Livestock data acquisition essentially used in Smart Farming where a large amount of connected technologies produces a huge amount of data in order to maximize productions by reducing: human efforts, environment impact and wasting natural resources. It helps in improving livestock production, animals’ welfare, and farming processes, allowing to ease monitoring operations that can help farmers.
The document outlines the timeline of agricultural development from the Neolithic Revolution to the potential of farming on Mars. It describes how agriculture has evolved from early sedentary farming and domestication 10,000 years ago, to subsequent revolutions brought by improved crop rotations, mechanization, biotechnology, and now smart farming technologies using IoT. The rise of IoT solutions such as wireless sensors, base stations, and monitoring software is allowing real-time data collection and analysis to optimize farming processes, reduce costs, and save time previously spent on manual data collection.
From Neolithic revolution to farming on Mars, the agriculture has experienced multiple stages of technological development throughout the ages. Take a look and find out more about the upcoming smart farming and IoT technology impact on agriculture.
IOT has many applications in agriculture such as crop water management using soil moisture sensors, pest management using motion detecting PIR sensors, precision farming using sensors and drones, and livestock monitoring using sensors on wearables that track temperature, activity, and health indicators. IOT helps optimize resources like water, increase yields, and monitor livestock health remotely. However, challenges include infrastructure and connectivity issues, costs, and difficulties in implementation and data analysis for some farmers. Overall, IOT solutions have potential to increase agricultural sustainability and competitiveness.
This document discusses an Internet of Things (IoT) based smart agriculture monitoring system. It begins with an introduction to IoT and why it is being implemented in the agriculture sector. It then discusses several applications of IoT in agriculture including crop water management using soil moisture sensors, pest management using passive infrared sensors, precision agriculture, and ensuring food production and safety. The document outlines the implemented method using sensors connected to an Arduino board and Raspberry Pi to monitor data and send alerts. It discusses the advantages of optimizing water use and increasing productivity but notes the potential disadvantage of high initial costs.
This document discusses the implementation of IoT in agriculture in China. It notes that currently, greenhouse planting relies on farmers' experience rather than data. IoT is seen as a promising technology to reduce costs and improve efficiency in agriculture. The document outlines China's progression from manual to smart agriculture enabled by IoT. It describes the structure of an IoT agriculture system including sensor, transmission, processing and application layers. Benefits of IoT include water and resource savings, improved yields and productivity, and remote monitoring and control. The key advantages are seen as real-time data access, surveillance, analysis and intelligent decision making for farmers, consultants, finance and government.
Today, majority of the farmers are dependent on agriculture for their survival. But
majority of the agricultural tools and practices are outdated and it yields less crop
products, because everything is depends on environment and Government support. The
world population is becoming more comparatively cultivation land and crop yield. It is
essential for the world to increase the yielding of the crop by adopting information
technology and communication plays a vital role in smart farming. The objective of this
research paper to present tools and best practices for understanding the role of
information and communication technologies in agriculture sector, motivate and make
the illiterate farmers to understand the best insights given by the big data analytics using
machine learning
precision mean â€the quality of being clear or exactâ€. Farmer tries hard to get the result but we need the smart way and result oriented. The history of India's development has been inexorably linked to that of its farmers, and the nation's growth with that of its agronomics. Agronomics provides highest contribution to nation income. Agronomics needed top most priority because the Government and the nation would both fail to succeed if agronomics could not be successful. Today we are living in 21st century where automation is playing significant role in human life. Automation allows us to control appliances automatic control. Today industries are using automation and control machine which is high in cost and not suitable for using in a field. So as to help both government and our farmer, we can use intelligent irrigation techniques with the use of IoT internet of things and by building network of farmer and agriculturist to share their ideas and experience, as a full fledged force solution to the need .this can be easily done by organizing and analysing the live and collected over time data ,allowing farmers to take pre emptive action for healthy harvest of their crops collecting live data using sensors which are placed across the land further sent to the cloud further under taking predictive analytics to enhance crops nutrition thus using predictive analysis on data to find better solution. The IoT connected devices stream live data on the land allowing data informed decisions on planning the resources and harvesting of farm. Kartikeya Bhatia | Devendra Duda ""Precision Farming"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22793.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/22793/precision-farming/kartikeya-bhatia
This document describes a proposed smart poultry farm control system using Internet of Things (IoT) technologies. Sensors would monitor environmental parameters like temperature, humidity, and heat in the farm. An Arduino microcontroller would connect the sensors to a cloud database and control fans and lights automatically based on the sensor readings. Chicken details like weight and purchase date would also be stored in the database. The system aims to automate farm monitoring and control to improve productivity and quality while reducing costs and labor.
The document outlines the timeline of agricultural development from the Neolithic Revolution to the potential of farming on Mars. It describes how agriculture has evolved from early sedentary farming and domestication 10,000 years ago, to subsequent revolutions brought by improved crop rotations, mechanization, biotechnology, and now smart farming technologies using IoT. The rise of IoT solutions such as wireless sensors, base stations, and monitoring software is allowing real-time data collection and analysis to optimize farming processes, reduce costs, and save time previously spent on manual data collection.
From Neolithic revolution to farming on Mars, the agriculture has experienced multiple stages of technological development throughout the ages. Take a look and find out more about the upcoming smart farming and IoT technology impact on agriculture.
IOT has many applications in agriculture such as crop water management using soil moisture sensors, pest management using motion detecting PIR sensors, precision farming using sensors and drones, and livestock monitoring using sensors on wearables that track temperature, activity, and health indicators. IOT helps optimize resources like water, increase yields, and monitor livestock health remotely. However, challenges include infrastructure and connectivity issues, costs, and difficulties in implementation and data analysis for some farmers. Overall, IOT solutions have potential to increase agricultural sustainability and competitiveness.
This document discusses an Internet of Things (IoT) based smart agriculture monitoring system. It begins with an introduction to IoT and why it is being implemented in the agriculture sector. It then discusses several applications of IoT in agriculture including crop water management using soil moisture sensors, pest management using passive infrared sensors, precision agriculture, and ensuring food production and safety. The document outlines the implemented method using sensors connected to an Arduino board and Raspberry Pi to monitor data and send alerts. It discusses the advantages of optimizing water use and increasing productivity but notes the potential disadvantage of high initial costs.
This document discusses the implementation of IoT in agriculture in China. It notes that currently, greenhouse planting relies on farmers' experience rather than data. IoT is seen as a promising technology to reduce costs and improve efficiency in agriculture. The document outlines China's progression from manual to smart agriculture enabled by IoT. It describes the structure of an IoT agriculture system including sensor, transmission, processing and application layers. Benefits of IoT include water and resource savings, improved yields and productivity, and remote monitoring and control. The key advantages are seen as real-time data access, surveillance, analysis and intelligent decision making for farmers, consultants, finance and government.
Today, majority of the farmers are dependent on agriculture for their survival. But
majority of the agricultural tools and practices are outdated and it yields less crop
products, because everything is depends on environment and Government support. The
world population is becoming more comparatively cultivation land and crop yield. It is
essential for the world to increase the yielding of the crop by adopting information
technology and communication plays a vital role in smart farming. The objective of this
research paper to present tools and best practices for understanding the role of
information and communication technologies in agriculture sector, motivate and make
the illiterate farmers to understand the best insights given by the big data analytics using
machine learning
precision mean â€the quality of being clear or exactâ€. Farmer tries hard to get the result but we need the smart way and result oriented. The history of India's development has been inexorably linked to that of its farmers, and the nation's growth with that of its agronomics. Agronomics provides highest contribution to nation income. Agronomics needed top most priority because the Government and the nation would both fail to succeed if agronomics could not be successful. Today we are living in 21st century where automation is playing significant role in human life. Automation allows us to control appliances automatic control. Today industries are using automation and control machine which is high in cost and not suitable for using in a field. So as to help both government and our farmer, we can use intelligent irrigation techniques with the use of IoT internet of things and by building network of farmer and agriculturist to share their ideas and experience, as a full fledged force solution to the need .this can be easily done by organizing and analysing the live and collected over time data ,allowing farmers to take pre emptive action for healthy harvest of their crops collecting live data using sensors which are placed across the land further sent to the cloud further under taking predictive analytics to enhance crops nutrition thus using predictive analysis on data to find better solution. The IoT connected devices stream live data on the land allowing data informed decisions on planning the resources and harvesting of farm. Kartikeya Bhatia | Devendra Duda ""Precision Farming"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22793.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/22793/precision-farming/kartikeya-bhatia
This document describes a proposed smart poultry farm control system using Internet of Things (IoT) technologies. Sensors would monitor environmental parameters like temperature, humidity, and heat in the farm. An Arduino microcontroller would connect the sensors to a cloud database and control fans and lights automatically based on the sensor readings. Chicken details like weight and purchase date would also be stored in the database. The system aims to automate farm monitoring and control to improve productivity and quality while reducing costs and labor.
The document discusses the Internet of Things (IoT) and how it connects physical objects through sensors, connectivity and integration with other systems and people. It defines the IoT as being driven by sensors and actuators that collect data about the physical world, connectivity that allows these devices to communicate over networks, and people and processes that analyze and make use of the collected data. It provides examples of current IoT applications and envisions how these applications could become more integrated and networked to create new services and value across different industries. It also discusses the rapid growth of the IoT market in terms of connected devices, installed base and revenue opportunities projected to reach over $1 trillion by 2020.
Smart system monitoring on soil using internet of things (IOT)IRJET Journal
This document describes a smart system for monitoring soil conditions using sensors and the Internet of Things (IoT). Sensors placed in agricultural land would measure the soil's pH rate, temperature, and moisture levels. This data would be sent to the cloud and then relayed to registered farmers via their mobile devices. The system aims to reduce farmers' workload by automatically monitoring soil and alerting them to abnormalities. It could also recommend pesticides to improve cultivation based on pH readings. The system is designed to help farmers better manage their land even from a distance through IoT-connected soil monitoring and analytics.
IRJET - An Effective Automated Monitoring and Controlling of Poultry Farm usi...IRJET Journal
This document describes a proposed system to automate and monitor a poultry farm using IoT technology. The system would automatically control and monitor key parameters like temperature, food feeding, movement of chickens, weight of chickens, and disposal of waste. Sensors would monitor these parameters and a microcontroller would control devices like fans, food dispensers, and motors to automate tasks. Data would be sent to the cloud and alerts could be sent to farmers via a mobile app. This would help reduce labor needs, improve health and growth of chickens, and make farm management easier.
The document discusses the use of Internet of Things (IoT) in agriculture. It begins by defining IoT and describing some common applications including environmental monitoring, industrial uses, agriculture, home automation and transportation. It then focuses specifically on how IoT allows remote monitoring of farm conditions in real-time through sensors that can measure soil moisture, chemical applications and livestock health. The document outlines five ways IoT can improve agriculture such as increased data collection, better production control, cost management, process automation and enhanced product quality. Finally, it discusses some advantages and disadvantages of using IoT in agriculture.
The document discusses how smart farming uses embedded systems to enhance agricultural efficiency and sustainability. Key components include sensors to measure farm conditions, drones to monitor fields, and IoT devices to collect and analyze data. Sensors monitor soil, weather, and crop health, while actuators control irrigation and machinery. Embedded systems enable remote monitoring, data-driven decisions, and optimizing resource use. Challenges include costs and connectivity in rural areas, but solutions involve government support and more energy efficient designs.
This document discusses Internet of Things (IoT) applications in agriculture. It begins by defining IoT and explaining its growing importance. It then discusses using IoT in agriculture to help farmers overcome challenges by remotely monitoring crops. Key applications mentioned include precision farming, agricultural drones, livestock monitoring, smart greenhouses, and crop management. The document also discusses agricultural sensors, sensor outputs, tools used, pros and cons of IoT in agriculture, and concludes that IoT can help increase yields, conserve water, reduce losses, and increase profits for farmers.
Presentation for a group of employees of Centric, a large software consultancy company. It provides an illustration of how IoT is currently being developed in farming, agri-logistics and food consumption. It also addresses the technical and organizational challenges that have to be overcome to make IoT application in agri-food a success. Open platforms and software development and above all appropriate business models are key issues that have to be addressed. The new EU-project "Internet of Food and Farm 2020" will address these issues by fostering a collaborative IoT ecosystem to upscale the use of IoT in agri-food.
ROLE OF IOT ,SENSORS AND NANOBIOSENSORS IN AGRICULTURE.pptxarchana reddy
Role of Internet of things, sensors, biosensors in agriculture,types of sensors,Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.
Machine learning is a branch of (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Deep learning is a subset of ML, which is essentially a neural network with three or more layers, which attempt to simulate the behavior of the human brain—though far from matching its ability—allowing it to “learn” from large amounts of data.
Pradhan Mantri Fasal Bima Yojana (PMFBY)
This is a government-sponsored crop insurance scheme that integrates multiple stakeholders on a single platform. To improve the crop sector, the government will now envisage the use of innovative technologies like AI, remote sensing imageries, and modelling tools to reduce the time lag for settling of claims of the farmers. By analysing the data collected, the scheme aims at increasing the crop insurance penetration in India by increasing farmer awareness and reducing farmer premium rates.
PM-KISAN
By leveraging the benefits of AI, the government of India has rolled out a scheme — PM-KISAN, where every farmer is going to receive Rs. 6000 annually to support their farming abilities. The government is aimed to leverage the huge amount of collected data by several agri-schemes and use the same to better target the farmer who requires the benefit of PM-KISAN.
The data will be used in creating a proper framework for farmers, along with the right policy. It will also help in converging some government projects to achieve the targeted development of farmers and the overall sector.
In a bid to push innovative technologies in agriculture secure, the government of India has also launched another initiative — AGRI-UDAAN – Food & Agribusiness accelerator 2.0 to mentor 40 agricultural startups from cities like Chandigarh, Ahmedabad, Pune, Bengaluru, Kolkata and Hyderabad, and enable them to connect with potential investors. This initiative is a six-month-programme in which shortlisted Agri startups with innovative business models will be mentored and guided to improve their operations, enhance commercialisation, improve product validation and business plan preparation, risk analysis, customer engagement, finance management, and fundraising. These shortlisted startups will also stand a chance of receiving $40,000 as funding assistanceLaunched earlier this year, the project is based out of Maharashtra — seeks to use innovative technologies to address various risks related to cultivation such as poor rains, pest attacks, etc. and to accurately predict crop yielding. The project will also use this data to inform farmers about several policy requirements including pricing, warehousing and crop
IRJET- Advanced Guiding Tool for the Selection of CropsIRJET Journal
This document describes an advanced guiding tool to help farmers select appropriate crops. It uses sensors to monitor soil characteristics and environmental conditions. The sensor data is sent to a Node MCU microcontroller and stored in the cloud. Farmers can access this historical data using wireless devices to determine which crops are best suited for their land based on the soil and climate conditions. This system improves upon previous agriculture monitoring systems by not only monitoring crop growth but also collecting and storing sensor data in the cloud. This stored data can then be used to advise farmers on optimal crop selection.
Technologies for agricultural and food processing.pptx20EUMC090PRAVEEND
Smart farming uses technologies like IoT, robotics, drones and AI to increase agricultural productivity and quality. It involves monitoring farm conditions like soil moisture, temperature and humidity with sensors. This allows farmers to precisely control their farms remotely 24/7. Smart farming solutions are implemented using sensors, specialized software, connectivity networks, GPS, robotics and data analytics to optimize activities like irrigation, pest management and precision agriculture. IOT is also being applied in agriculture for applications such as crop water management, pest control and precision farming.
This document discusses strategies for developing smart agriculture in Thailand using new technologies. It outlines several key challenges facing Thai agriculture, including rising population and food demands, climate change impacts, and labor shortages. It then proposes using information and communication technologies (ICT) like sensors, drones, and mobile apps to address issues in crop production, quality assessment, risk reduction, knowledge empowerment, and more. Specific solutions outlined include precision farming systems, quality traceability tools, early warning systems, and advisory services. It emphasizes the need for collaboration between different groups and an approach called "Village that Learn" to facilitate local knowledge sharing and lifelong learning. The overall aim is to create smarter farmers and officers through appropriate technology integration.
IRJET- Smart Green House using IOT and Cloud ComputingIRJET Journal
1. The document describes a smart greenhouse system that uses IoT and cloud computing to automatically monitor and control the greenhouse's environment.
2. Sensors measure soil moisture, temperature, humidity, and sunlight levels, and send the data to a Raspberry Pi controller.
3. If sensor readings exceed predefined thresholds, the Raspberry Pi activates devices like water sprayers, fans, and lights to regulate the environment and optimize plant growth.
IRJET - Poultry Farm Controlling based on IoTIRJET Journal
This document describes a poultry farm monitoring system using IoT technology. The system uses sensors to monitor temperature, humidity, gas levels and food levels in the farm. An Arduino Mega controller collects data from the sensors and sends it to the cloud. Users can then view the sensor data through a mobile app, which will also notify them of any abnormal conditions. The system aims to automate 80% of the farm monitoring process and remotely manage conditions in the poultry house.
This document discusses how internet of things (IoT) and big data analytics can be applied to smart agriculture management. It begins with an overview and then discusses key topics like IoT, big data analysis methods, smart agriculture, research methodology, results analysis, future applications, and implementation challenges. The document provides details on how sensors can collect unstructured agricultural data which is then processed using big data analytics to provide insights and recommendations to farmers to help improve productivity, optimize resources, and support decision making. Challenges around economic efficiency and technical complexity of implementing such smart agriculture systems are also outlined.
The Internet of Things (IoT) in agriculture revolutionizes traditional farming practices by integrating smart technologies. Through sensor networks, data analytics, and connectivity, IoT empowers farmers with real-time insights into crop conditions, soil health, and equipment performance. This transformative approach enhances efficiency, resource utilization, and sustainability in agricultural processes, marking a significant leap toward precision farming.
Here we tried to focus briefly on IoT in agriculture topic. Hope it will help you.
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to double by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and factor monitoring. Examples provided include automated irrigation systems for water and cost savings, remote sensing for crop health monitoring, AI-assisted harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using techniques like neural networks and genetic algorithms can also assist with yield prediction. Additional applications discussed are driverless tractors, targeted weed removal robots, and AI complementing farmer decision making.
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to double by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and machine vision systems. Examples provided include automated irrigation systems to save water, remote sensing for crop health monitoring, AI-based harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using neural networks, genetic algorithms and other techniques can also help with yield prediction. Additional applications mentioned are driverless tractors, targeted weed removal robots, and AI-guided farming decisions. The document concludes that AI can optimize resource use and help solve labor
1. Food processing automation aims to improve food safety, quality, and efficiency through technology.
2. Current automation in food industry consists of isolated automated processes, but full integration is needed.
3. Challenges include food variation and unique properties, but automation reduces costs and improves consistency.
artificial intelligence of farming.pptx____kdemersal
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to reach 10 billion by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and machine vision systems. Examples provided include automated irrigation systems to save water and labor, remote sensing for crop health monitoring, AI-based harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using neural networks, genetic algorithms and other techniques can also help with yield prediction. Additional applications mentioned are driverless tractors, targeted weed removal robots, and making optimal decisions to feed the world's growing population.
This document discusses how IoT sensors can benefit agriculture. It provides examples of IoT devices like drones, grain monitors, and water level sensors that collect real-time crop data. This data allows farmers to make better decisions that optimize resources, improve product quality, and increase responsiveness to changes. While high costs and security pose challenges, trends show drones and sensors aiding tasks like field monitoring and precision agriculture. Overall, IoT is transforming farming by helping close yield gaps and reducing labor needs through remote monitoring and smart practices.
Record keeping in dairy farm management.pdfHari Om Pandey
Presentation tries to explain role of record keeping in sound planning of breeding, feeding and heeding and health management of dairy farm
in order to efficiently and effectively manage growth, production and reproduction. It assists in livestock management decisions and evaluates overall activities of the dairy farm.
Neuro-endocrine basis of Behavioural Disorders in Farm AnimalsHari Om Pandey
1. The document discusses the neuroendocrine basis of behavioural disorders in farm animals. It explores how behaviours are influenced by both innate and acquired factors, as well as hormones and stress.
2. Specific behavioural disorders that are mentioned include stereotypic disorders, aggression, anxiety disorders, and eating disorders. Stereotypies can involve repetitive motions with no function.
3. Factors like genetics, environment, hormones, and stress are discussed in influencing the development of behavioural disorders. The hypothalamic-pituitary-adrenal axis is particularly important in the stress response.
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The document discusses the Internet of Things (IoT) and how it connects physical objects through sensors, connectivity and integration with other systems and people. It defines the IoT as being driven by sensors and actuators that collect data about the physical world, connectivity that allows these devices to communicate over networks, and people and processes that analyze and make use of the collected data. It provides examples of current IoT applications and envisions how these applications could become more integrated and networked to create new services and value across different industries. It also discusses the rapid growth of the IoT market in terms of connected devices, installed base and revenue opportunities projected to reach over $1 trillion by 2020.
Smart system monitoring on soil using internet of things (IOT)IRJET Journal
This document describes a smart system for monitoring soil conditions using sensors and the Internet of Things (IoT). Sensors placed in agricultural land would measure the soil's pH rate, temperature, and moisture levels. This data would be sent to the cloud and then relayed to registered farmers via their mobile devices. The system aims to reduce farmers' workload by automatically monitoring soil and alerting them to abnormalities. It could also recommend pesticides to improve cultivation based on pH readings. The system is designed to help farmers better manage their land even from a distance through IoT-connected soil monitoring and analytics.
IRJET - An Effective Automated Monitoring and Controlling of Poultry Farm usi...IRJET Journal
This document describes a proposed system to automate and monitor a poultry farm using IoT technology. The system would automatically control and monitor key parameters like temperature, food feeding, movement of chickens, weight of chickens, and disposal of waste. Sensors would monitor these parameters and a microcontroller would control devices like fans, food dispensers, and motors to automate tasks. Data would be sent to the cloud and alerts could be sent to farmers via a mobile app. This would help reduce labor needs, improve health and growth of chickens, and make farm management easier.
The document discusses the use of Internet of Things (IoT) in agriculture. It begins by defining IoT and describing some common applications including environmental monitoring, industrial uses, agriculture, home automation and transportation. It then focuses specifically on how IoT allows remote monitoring of farm conditions in real-time through sensors that can measure soil moisture, chemical applications and livestock health. The document outlines five ways IoT can improve agriculture such as increased data collection, better production control, cost management, process automation and enhanced product quality. Finally, it discusses some advantages and disadvantages of using IoT in agriculture.
The document discusses how smart farming uses embedded systems to enhance agricultural efficiency and sustainability. Key components include sensors to measure farm conditions, drones to monitor fields, and IoT devices to collect and analyze data. Sensors monitor soil, weather, and crop health, while actuators control irrigation and machinery. Embedded systems enable remote monitoring, data-driven decisions, and optimizing resource use. Challenges include costs and connectivity in rural areas, but solutions involve government support and more energy efficient designs.
This document discusses Internet of Things (IoT) applications in agriculture. It begins by defining IoT and explaining its growing importance. It then discusses using IoT in agriculture to help farmers overcome challenges by remotely monitoring crops. Key applications mentioned include precision farming, agricultural drones, livestock monitoring, smart greenhouses, and crop management. The document also discusses agricultural sensors, sensor outputs, tools used, pros and cons of IoT in agriculture, and concludes that IoT can help increase yields, conserve water, reduce losses, and increase profits for farmers.
Presentation for a group of employees of Centric, a large software consultancy company. It provides an illustration of how IoT is currently being developed in farming, agri-logistics and food consumption. It also addresses the technical and organizational challenges that have to be overcome to make IoT application in agri-food a success. Open platforms and software development and above all appropriate business models are key issues that have to be addressed. The new EU-project "Internet of Food and Farm 2020" will address these issues by fostering a collaborative IoT ecosystem to upscale the use of IoT in agri-food.
ROLE OF IOT ,SENSORS AND NANOBIOSENSORS IN AGRICULTURE.pptxarchana reddy
Role of Internet of things, sensors, biosensors in agriculture,types of sensors,Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.
Machine learning is a branch of (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Deep learning is a subset of ML, which is essentially a neural network with three or more layers, which attempt to simulate the behavior of the human brain—though far from matching its ability—allowing it to “learn” from large amounts of data.
Pradhan Mantri Fasal Bima Yojana (PMFBY)
This is a government-sponsored crop insurance scheme that integrates multiple stakeholders on a single platform. To improve the crop sector, the government will now envisage the use of innovative technologies like AI, remote sensing imageries, and modelling tools to reduce the time lag for settling of claims of the farmers. By analysing the data collected, the scheme aims at increasing the crop insurance penetration in India by increasing farmer awareness and reducing farmer premium rates.
PM-KISAN
By leveraging the benefits of AI, the government of India has rolled out a scheme — PM-KISAN, where every farmer is going to receive Rs. 6000 annually to support their farming abilities. The government is aimed to leverage the huge amount of collected data by several agri-schemes and use the same to better target the farmer who requires the benefit of PM-KISAN.
The data will be used in creating a proper framework for farmers, along with the right policy. It will also help in converging some government projects to achieve the targeted development of farmers and the overall sector.
In a bid to push innovative technologies in agriculture secure, the government of India has also launched another initiative — AGRI-UDAAN – Food & Agribusiness accelerator 2.0 to mentor 40 agricultural startups from cities like Chandigarh, Ahmedabad, Pune, Bengaluru, Kolkata and Hyderabad, and enable them to connect with potential investors. This initiative is a six-month-programme in which shortlisted Agri startups with innovative business models will be mentored and guided to improve their operations, enhance commercialisation, improve product validation and business plan preparation, risk analysis, customer engagement, finance management, and fundraising. These shortlisted startups will also stand a chance of receiving $40,000 as funding assistanceLaunched earlier this year, the project is based out of Maharashtra — seeks to use innovative technologies to address various risks related to cultivation such as poor rains, pest attacks, etc. and to accurately predict crop yielding. The project will also use this data to inform farmers about several policy requirements including pricing, warehousing and crop
IRJET- Advanced Guiding Tool for the Selection of CropsIRJET Journal
This document describes an advanced guiding tool to help farmers select appropriate crops. It uses sensors to monitor soil characteristics and environmental conditions. The sensor data is sent to a Node MCU microcontroller and stored in the cloud. Farmers can access this historical data using wireless devices to determine which crops are best suited for their land based on the soil and climate conditions. This system improves upon previous agriculture monitoring systems by not only monitoring crop growth but also collecting and storing sensor data in the cloud. This stored data can then be used to advise farmers on optimal crop selection.
Technologies for agricultural and food processing.pptx20EUMC090PRAVEEND
Smart farming uses technologies like IoT, robotics, drones and AI to increase agricultural productivity and quality. It involves monitoring farm conditions like soil moisture, temperature and humidity with sensors. This allows farmers to precisely control their farms remotely 24/7. Smart farming solutions are implemented using sensors, specialized software, connectivity networks, GPS, robotics and data analytics to optimize activities like irrigation, pest management and precision agriculture. IOT is also being applied in agriculture for applications such as crop water management, pest control and precision farming.
This document discusses strategies for developing smart agriculture in Thailand using new technologies. It outlines several key challenges facing Thai agriculture, including rising population and food demands, climate change impacts, and labor shortages. It then proposes using information and communication technologies (ICT) like sensors, drones, and mobile apps to address issues in crop production, quality assessment, risk reduction, knowledge empowerment, and more. Specific solutions outlined include precision farming systems, quality traceability tools, early warning systems, and advisory services. It emphasizes the need for collaboration between different groups and an approach called "Village that Learn" to facilitate local knowledge sharing and lifelong learning. The overall aim is to create smarter farmers and officers through appropriate technology integration.
IRJET- Smart Green House using IOT and Cloud ComputingIRJET Journal
1. The document describes a smart greenhouse system that uses IoT and cloud computing to automatically monitor and control the greenhouse's environment.
2. Sensors measure soil moisture, temperature, humidity, and sunlight levels, and send the data to a Raspberry Pi controller.
3. If sensor readings exceed predefined thresholds, the Raspberry Pi activates devices like water sprayers, fans, and lights to regulate the environment and optimize plant growth.
IRJET - Poultry Farm Controlling based on IoTIRJET Journal
This document describes a poultry farm monitoring system using IoT technology. The system uses sensors to monitor temperature, humidity, gas levels and food levels in the farm. An Arduino Mega controller collects data from the sensors and sends it to the cloud. Users can then view the sensor data through a mobile app, which will also notify them of any abnormal conditions. The system aims to automate 80% of the farm monitoring process and remotely manage conditions in the poultry house.
This document discusses how internet of things (IoT) and big data analytics can be applied to smart agriculture management. It begins with an overview and then discusses key topics like IoT, big data analysis methods, smart agriculture, research methodology, results analysis, future applications, and implementation challenges. The document provides details on how sensors can collect unstructured agricultural data which is then processed using big data analytics to provide insights and recommendations to farmers to help improve productivity, optimize resources, and support decision making. Challenges around economic efficiency and technical complexity of implementing such smart agriculture systems are also outlined.
The Internet of Things (IoT) in agriculture revolutionizes traditional farming practices by integrating smart technologies. Through sensor networks, data analytics, and connectivity, IoT empowers farmers with real-time insights into crop conditions, soil health, and equipment performance. This transformative approach enhances efficiency, resource utilization, and sustainability in agricultural processes, marking a significant leap toward precision farming.
Here we tried to focus briefly on IoT in agriculture topic. Hope it will help you.
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to double by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and factor monitoring. Examples provided include automated irrigation systems for water and cost savings, remote sensing for crop health monitoring, AI-assisted harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using techniques like neural networks and genetic algorithms can also assist with yield prediction. Additional applications discussed are driverless tractors, targeted weed removal robots, and AI complementing farmer decision making.
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to double by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and machine vision systems. Examples provided include automated irrigation systems to save water, remote sensing for crop health monitoring, AI-based harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using neural networks, genetic algorithms and other techniques can also help with yield prediction. Additional applications mentioned are driverless tractors, targeted weed removal robots, and AI-guided farming decisions. The document concludes that AI can optimize resource use and help solve labor
1. Food processing automation aims to improve food safety, quality, and efficiency through technology.
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artificial intelligence of farming.pptx____kdemersal
This document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to reach 10 billion by 2050, requiring a 70% increase in food production. AI can help address this challenge through automated farming activities, pest and disease monitoring, crop quality management, and machine vision systems. Examples provided include automated irrigation systems to save water and labor, remote sensing for crop health monitoring, AI-based harvesting of vine crops, and early warning systems for pest outbreaks. Decision support systems using neural networks, genetic algorithms and other techniques can also help with yield prediction. Additional applications mentioned are driverless tractors, targeted weed removal robots, and making optimal decisions to feed the world's growing population.
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Livestock farm Data Acquisition-Processing and management ..pdf
1. LIVESTOCK FARM DATA ACQUISITION-PROCESSING
AND MANAGEMENT
Hari Om Pandey
Livestock Production and Management
ICAR-IVRI, Izatnagar, Bareilly-243122, (UP), INDIA
hariomvet@gmail.com
2. q By 2050, the projected global human population will be
over 9 billion
q Land and water will become increasingly competitive
resources.
q Livestock producers will need to maximize production
sustainably
q This fuels incentives for responsible research and
innovation to solving pressing problems in livestock sector
q Hence need for mechanisation and digitalisation of
farming arises
Introduction
3. ØImprovement in livestock production, animals’ welfare, and
farming processes, allowing to ease monitoring operations that can
help farmers.
ØEra of advanced technologies where tremendous
amount of data is produced by multiple sources such
as sensors, devices, etc.
ØSmart Farming uses a large amount of connected
technologies producing also a huge amount of data in
order to maximize productions by reducing: human
efforts, environment impact and wasting natural
resources
Livestock data in smart farming
4. § Facts and statistics collected together for reference or analysis.
§ The quantities, characters, or symbols on which operations are performed by a
computer, which may be stored and transmitted in the form of electrical signals
and recorded on magnetic, optical, or mechanical recording media.
§ Things known or assumed as facts, making the basis of reasoning or calculation
§ Data requires interpretation to become information.
§ Data representing quantities, characters, or symbols on which
operations are performed by a computer are stored and recorded
on magnetic, optical, electronic, or mechanical recording media,
and transmitted in the form of digital electrical or optical signals.
§ Data structures can store data of many different types, including
numbers, strings and even other data structures.
Data ??
6. optimizing
production efficiency
Precise work
Safe operations/
minimizing risk
reducing cost /Tackling
cost of labour
(25-35%)
conservation of
resources
BENEFITS OF MECHANISATION
minimizing environmental impact
optimizing quality of the crop
increasing profit
Advantages of Livestock Farm Mechanization
Precision agriculture, site-specific farming, site specific
crop management, prescription farming, and satellite farming
,,,
,,,, and better management
decisions (Sørensen et al. 2010, 2011; Rains and Thomas 2009;
Zhang et al. 2002).
7. Timeliness of operations
(15-20% less)
Precise
work/better
dicision
Minimizing
risk/Safe operations
Tackling cost of labour
(25-35%)
Input savings/
reducing cost
BENEFITS OF MECHANISATION
optimizing
production efficiency
/optimizing quality
Improved farmer dignity
More economic
returns/increasing profit
Advantages of Livestock Farm Mechanization
Precision agriculture, site-specific farming, site specific
crop management, prescription farming, and satellite farming
(Sørensen et al. 2010, 2011; Rains and Thomas
2009; Zhang et al. 2002).
8. Labour and time benefits
Source:FICCI,www.indiaagristat.com;
Financial evaluation of mechanization options,www.fao.org
• 15-20% saving of time
• More time for animal care
• Increased efficiency and per man productivity
• 25-35% labour saving
Use of power for agriculture (kW/ha)
Source of power and % shares
AgWorker Animal Tractor PowerTiller Diesel
Engine
Electric
Motor
Advantages of Livestock Farm Mechanization
9. Benefits of livestock farm mechanization
Production benefits: a case of milk
1960-61
20
million
tonnes
1980-81
31
million
tonnes
2000-01
80
million
tonnes
2018-19
187
million
tonnes
Early 1990s: Commercial
milking machines introduced
Milk
production
Source:Ministry of Agriculture and Farmer’sWelfare,GOI
10. Environmental benefits
1 tonne of paddy straw burning =
3 kg particulate matter, 60 kg CO, 1460 kg CO2, 199 kg ash and 2 kg SO2
• Decreased greenhouse gas emissions by feed waste management
• Green energy generation (dung + feed waste utilization)
1 tonne of paddy straw utilization = 3500 MJ clean energy
• Greenhouse gas emissions ~20% can be controlled through dung management
Source:Guidelines for crop residue
managementwww.agricoop.nic.in,2019
Source:https://www.thehindubusinessline.com/news/indias-methane-emissions-stabilising-conform-to-un-
report/article9912925.ece
Advantage of Livestock Farm Mechanization
11. Social benefits
§ Decrease in workload of hardworking farm women with advanced gender
sensitive machines
§ Use of advanced machinery like drones, AI and sensors attracts youth
§ Ergonomically safe farm machinery operation with lesser efforts – happy
farmers
§ Farmer has more time for a socio-economic life away from
laborious/drudgery prone work
Advantage of Livestock Farm Mechanization
13. Use of Artificial Intelligence (AI) in livestock farms
AI for health monitoring
• Motion detection sensors +AI system
• Movement behaviour of cattle noted byAI smart collars
• Data basedAI determinations: animal illness, ready to breed, or production status
• Heat stress, feeding efficiency and Estrus in cows also detected
• Robotic systems +AI + RFID tags aid in mechanized vaccination
14. Use of Drones, Robots and 3D printing
Drones
§ Locating and tracking cows
§ Monitoring of resting and grazing
§ Thermal imaging for health monitoring
Robots
§ Milking machines and automatic teat cleaners
§ Farm sanitization systems
3D printing
§ Printing of machine parts in dairy sector
§ Printing of animal products (even feed?)
15. Internet of Things (IoT) and blockchain in livestock farms
Internet of Things
§ Exchanging data between devices over the internet
§ Helps in smart and automated information gathering and merging.
§ Farm record keeping, management and biosecurity
§ Used in farm management information systems (FMISs) that
supports the automation of data acquisition and processing,
monitoring, planning, decision making, documenting, and
managing the farm operations.
§ Cloud based system, enables feed, waste management
Blockchain
§ Connecting supply chain: industry to consumers
§ Food security and traceability enhanced
§ Competitive edge to manufacturers
§ Can be easily applied for milk and milk products
16. Use of sensors and big data in livestock farms
Lying
Rumination Drinking
Moving Feeding
Heat Standing
• Real time sensors
• Wearable smart collars
• Data send through devices
• Data collection and evaluation centre
• Analysis for big data for animal trends
• Animal interaction and behaviour
monitoring
17. Data acquisition(DAQ) is the process of automatically importing data from an instrument/circuit into
a computer.
Sensor(eg .temperature to
voltage)
Amplification,filtering
Communication bridge between sensor
and computer
Through USB
DAQ acts as an interface between the signal from outside and the computer.
What is Data Acquisition
Data acquisition (commonly abbreviated as DAQ or DAS) is the process of sampling signals that
measure real-world physical phenomena and converting them into a digital form that can be
manipulated by a computer and software
18. q Sensor
q Signal Conditioning
qAnalog-to-Digital Converter (ADC)
qComputer with DAQ software for signal logging and analysis
Components for data acquisition system
Data Acquisition Systems
19. § Transducer-used for data handling; converts one form of energy to another but sensor directly
measures electrical quantities
§ Signal conditioning- Weak signals from transducer are converted to strong signals
§ Data conversion- Analogue converted to digital signals
§ Multiplexing-Several analogue or digital input signals are forwarded as a single output line.
Steps Involved In Data Acquisition
20. SENSORS
A sensor can be defined as a device which
measures or detects changes in its
environment and collects the data for
interpretation by a human or a machine.
§ The measurement of a physical phenomenon, such as the temperature,
the level of a sound source, or the vibration occurring from constant
motion, begins with a sensor.
§ A sensor converts a physical phenomenon into a measurable electrical
signal.
21. Sensor technologies based on the animal farming market :-
1. Sensors for feeding systems and precision milking robots
2. Hardware sensors such as camera or vision sensors
3. Infrared thermal imaging sensors to monitor body temperatures
to detect disease and stress
4. Facial recognition vision sensors
5. Radio Frequency Identification (RFID): Tagging with this allows
users to automatically and uniquely identify and track.
6. Pedometers
SENSORS
22. vPiggery:
Ø includes 2D and 3D cameras,
Ø microphones,
Ø thermal imaging,
Ø accelerometers,
Ø radio frequency identification (RFID),
Ø facial recognition and
Ø acoustic analysis
§ Drawback-Due to the curious nature of the pigs they are likely to chew devices that are
placed almost anywhere on the body or in the pen, making ear-tag RFID technology the
most promising solution
vPoultry- Infrared thermometers have been used to monitor the body temperature of
broilers.Acoustic analysis used to monitor change in chicken vocalisations.
SENSORS IN DIFFERENT SPECIES
23. Signal conditioning is the technique of making a signal from a sensor or transducer suitable
for processing by data acquisition equipment.
It is the first step of computerised data acquisition.
Signal Conditioning
24. § The output of most physical measurement signal conditions is an analogue signal.
§ It is necessary to convert this signal to a series of high-speed digital values so that it can be
displayed and stored by the data acquisition system.
§ As such, anA/D card orA/D subsystem is used to convert this signal.
Analog-to-Digital Converters (ADCs or AD Converters
26. Three types of data acquisition categories are:-
1) Image acquisition
TYPES OF DATAACQUISITION SYSTEM
27. 3) Video acquisition:
Allows the digital video recording.
The biometric data acquisition can be made upon request or can be scheduled by the system manager,
according to the researcher needs for monitoring the experiment or activity
2) one-dimensional signal acquisition
TYPES OF DATAACQUISITION SYSTEM
30. 3/17/23
30
— Not suitable for all species
— Can be replicated/counterfeit
— Branding-Vogue method (Welfare issues)
— Tattooing-need close observation
— Traditional animal identification are inferior
— Larger herd – poorer management practices
— Individual animal management is not possible
— Skilled labour problem for record keeping in govt. farms
(Trevarthen, 2007)
(Schwartzkopf-Genswein, 1998)
Demerits of Conventional Methods of Identification
31. 3/17/23
31
— Uses radio waves to link database with identification chips/tags
(Wu Hong-da, 2012)
Radio Frequency Identification
(RFID)
33. 3/17/23
RFID-TRANSPONDERS
33
— Electronic collars - similar to neck chains
— Tag with an electronic number- readable
by a scanner.
— Electronic collars are easy to use
— Draw backs-
— Nuisance & choking
— Hooked on protrusions.
Electronic Collars
34. 3/17/23 RFID-TRANSPONDERS 34
Antenna
— Contain silicon chips and an
antenna
— Information stored is strictly
accordance with the ISO
standards
— Passive tags are lighter, less
expensive, and unlimited
life.
(Kampers et al. 1999)
e-ID Tags
36. 3/17/23
RFID-TRANSPONDERS
36
Balling gun.
§ Placed a balling gun
§ Irretrievable until the time of slaughter
§ Showed higher readability than visual tag (99.5 vs
89.8%). (Garin et al. 2005)
Electronic Bolus
37. 3/17/23
RFID-TRANSPONDERS
37
— Microchips -with antenna, under the skin
of animal
— Site- near the neck or near the base of the
ear
— Permanent and relatively painless to
implant.
The Kopordem farm atValpoi in SattariTaluk in North Goa has become the first farm in
India to use RFID microchips
Microchip implant
39. 3/17/23
RFID-READERS
39
q Fixed e.g. in milking parlour or abattoir
q Handheld, attached to a handheld computer (used in field)
(Blasi et al. 2000)
Reader
40. 3/17/23 RFID-READERS 40
— Greater range for reading tags (aprox100 cm)
— Direct network link with central database
— operations require individual identification
— Identifying cow entering milking parlor
— Automatic feeding
— Milk meters to record milk production
— Drafting gate operations
Fixed reader
41. 3/17/23
RFID-READERS
41
— Identification in the field
— Data from database can be copied to
this
— Read and display number on digital
screen
— Information viewed, recorded and
updated in field.
— Later uploaded to central database
Portable reader
42. 3/17/23
RFID-SOFTWARE
42
— Digital information storage
— Store individual data into a database
— Data entered manually or automatically
— Automating entry enables data to be stored reliably and
accurately
— Provides information required to make a decision or conduct an
action
Herdman was designed by Abdul Samad, Dean, Bombay vet college
(http://www.rfidjournal.com/article/view/7621)
Herd Management Software
43. 3/17/23 RFID-NETWORKS 43
Communication of devices b/w
one another, with RFID readers
and software.
— Wired
— Wireless
— Hybrid
(Trevarthen & Michael,2008)
Digital Network
44. 3/17/23
RFID-NETWORKS
44
— Wired:
— Cheaper than wireless network
— Reliability problems (rodents, general wear and tear)
— Wireless:
— Eliminates the need for cords/cables
— New devices are quite easy to introduce
— Hybrid:
— preferred where only some devices require portability
Cont…
46. 3/17/23
IMPLICATIONS
46
ØSimplified herd recording
ØAutomatic weighing
ØMilk production recording
ØFeeding Automation
ØReproduction
Management
ØHealth Monitoring
ØLivestock Insurance
ØComputerised storage of
breeding & veterinary record
ØAutomatic identification of
cows that need to be milked
separately.
ØBehaviour Study
ØTraceability (RFID)
ØAnimal movement tracking
ØHabitat Monitoring
RFID system is a key technology for the automation
Implication in Animal Husbandry
48. 3/17/23
IMPLICATIONS OF RFID
48
Sensor system for milking robot
Animal identification, Teat location, Monitoring
AMS function, Concentrate feeding, Milk quality & Composition
-Electrical Conductivity
-Temperature
-Chemical Composition &
-Color, SCC, Particle size
Recording of Milk
49. 3/17/23
IMPLICATIONS OF RFID
49
— Electronic Estrus Detection
— By body temperature variation
— Activity monitoring system –
Animal Activity appeared to rise by 30 to 200% (Smith and Saunders,
2005)
— Parturition Sensor
— Sense the body temperature and gives signal
Reproduction Management
50. 3/17/23
IMPLICATIONS OF RFID
50
— RFID helps:
— Monitor health of herd (e.g. physiological parameters, mastitis)
— Individual medication and vaccination records
— Tracking and segregating at early stages
— Leading to successful isolation and treatment
(Jansen & Eradus, 2009)
Health Monitoring
53. 3/17/23
53
§ Quick, Easy, and Accurate access of information of
individual animal
§ Difficult to replicate /counterfeit
§ Efficient management
§ Time & Labour saving
§ Dynamic data storage
§ Data can easily view, analyze, manipulate & sort
§ Aid in taking immediate decisions
Advantages of RFID
54. Accurate body measurement of live cattle using three depth cameras and non-
rigid 3-D shape recovery
Ruchay, 2020
§ Body condition scoring of livestock is widely used as a subjective method
for assessing energy reserves and making management decisions for
livestock.
§ Recent advances in three-dimensional sensor technology provide
innovative tools for the design of automated contactless systems for
assessing the animal body condition.
§ An automated computer vision system capable to generate an accurate
three-dimensional model of live cattle.
§ The system is based on a non-rigid 3-D shape reconstruction utilizing data
from depth cameras.
§ The design methodology includes three Microsoft Kinect v2 cameras,
computer vision, signal filtering of point clouds, pattern recognition using 3-
D feature extraction techniques, and statistical analysis using point and
interval estimations.
§ Approach can serve as a new accurate method for non-contact body
measurement of
55. Accurate body measurement of live cattle using three depth cameras and non-
rigid 3-D shape recovery
Ruchay, 2020
The estimated body measurements are defined as follows:•
WH (Withers height) : vertical distance from the highest point of the withers to the ground surface
at the level of the forelegs.
•HH (Hip height) : vertical distance from the highest point of the hip bones to the ground surface at
the level of the hind legs.
•CD (Chest depth) : vertical distance from the back to the floor of the chest at the shallowest part of
the chest.
•HG (Heart girth) : circumference of the body at a point immediately posterior to the front leg and
shoulder and perpendicular to the body axis.
•IW (Ilium width) : distance between the outermost points of the ilium bones perpendicular to the
back.
•HJW (Hip joint width) : distance between the hip joint points perpendicular to the back.
•OBL (Oblique body length) : distance from the anterior extremity of the humerus to the posterior
internal extremity of the ischium.
•HL (Hip length) : distance from the outer point of the ilium bone to the posterior internal extremity
of the ischium.
•CW (Chest width) : distance between the posterior corner points of the shoulder perpendicular to
the back.
56. Accurate body measurement of live cattle using three depth cameras and non-
rigid 3-D shape recovery
Ruchay, 2020
Synchronized data captured by three Kinect
cameras: (a) RGB image from C1, (b) depth map
from C1, (c) RGB image from C2, (d) depth map
from C2, (e) RGB image from C0, (f) depth map
from C0.
57. The wireless DataAcquisition Systems have enabled the application of this technology in
retrieving data from animals
Integrated with modern information technologies such as sensors, communications,
computer and networks, data acquisition system can be connected with most common
sensors for environmental factors of livestock, such as air temperature and humidity, CO2,
and some other harmful gases, production disease, mastitis etc
.
The monitoring activities of animals, still demand interest to develop equipment for
data collection
APPLICATIN IN LIVESTOCK
58. 1.Identifying,predicting & preventing diseases using sensors
.
Sensors constantly monitor key animal health parameters such as movement, airquality, and consumption of
feed and fluids.
Abnormalities or deviations detected are helpful in prevention.
2.Detection of calving time
with an accuracy up to 90%
Changes in the ingestive behavior of dairy cows due to the onset of calving is the key basis for
detection.
3.Environment monitoring of buildings
When livestock are kept in enclosed buildings, impact of air quality, temperature, humidity, etc.
needs to be considered. Monitoring of environmental factors reduce the detrimental effects on the
health.
Examples in livestock farms includes such applications as-
EXAMPLE OF DATA ACQUISITION SYSTEM
59. 5. Feed and water intake determination
4. Estrus detection
Use of pressure sensitive sensors ,pedometers, collars are used for detection of estrus
6. Rumen sensors
helps measure pH, temperature, motility, pressure, etc. to discover new insights into nutritional
research,animal health and behaviour,animal emissions activity
EXAMPLE OF DATA ACQUISITION SYSTEM
60. With the development of the Internet and the cow feeding of intensive open and
extensible dairy farm, remote data acquisition systems which has the functions of
network data sharing, data publishing and remote monitoring is the trend of
times(Xiong etal,2005)
The monitoring activities of animals, still demand interest to develop equipment for
data collection. The wireless sensor network research advances have enabled the
application of this technology in retrieving data from animals and their employment
was evaluated by Kettlewell et al. (1997) and Silva et al. (2005).
EXAMPLE OF DATA ACQUISITION SYSTEM
61. The physical variables obtained from cow house and individual cattle such as environmental
temperature, humidity, harmful gas concentration, cow body temperature, milk yield, activity, feed
intake and so on is converted to digital signals (Xiaxhing Huo,2013)
EXAMPLE OF DATA ACQUISITION SYSTEM
62. Source-Remote Data Acquisition System at the dairy farm based on Data socket technology(Xiaojing Huo etal,2013)
Remote data acquisition system at the dairy farm
63. Measurement of Real-time environmental data in Livestock house
including carbon dioxide , ammonia gas conc, etc
Source:-Design of LH Environmental Perception System based on wireless sensor network(Weizheng Shen etal,2016)
Change ofAmmonia Gas Content in 24h
64. Data acquisition for feed weight consumed
A. System structure, B. Software structure
The camera was placed at 140 cm above the head .(Computer vision system for measuring individual cow feed
intake using RGB-D camera and deep learning algorithms Ran Bezena etal,2020)
DATA ACQUISITION FOR FEED CONSUMED
Depth and colour images of feed
were acquired from an RGB-D
camera, which was placed above
the feed table
These images were used to build a
CNN regression model for
weight intake prediction
Computer vision system for
measuring individual cow feed
intake using RGB-D camera and
deep learning algorithms
65. DAQ system aims to provide:-
Ø Flexibility and user-friendly configuration for
Ø Capacity for acquiring large number of data with ease of dynamic real-time analogue and
digital configuration
Ø Convenience of table and graphical displays,
Ø Integration of stand-alone instruments into the system
DRAWBACKS OF DAQ
ØThe development of the measurement system, especially the software, is a very
time consuming and expensive process.
ØDue to lack of knowledge, the adoption of data acquisition system among the
farmers is very scarce.
66. § Presently in some animal farms
§ Expected to extend in more livestock sites by further development both in
hardware and software to meet smart farming
§ In near future could revolutionise global livestock production and management
in a positive way.
§ Leads to improvement of the livestock:
ü production,
ü animals’ welfare, and
ü farming processes that will allow to ease monitoring operations that can help
farmers.
CONCLUSION
67.
68. References:-
1. Remote Data Acquisition System at the dairy farm based on Data socket technology(XiaojingHuo,2013
2. Air Quality Monitoring and DataAcquisition for Livestock and Poultry Environment Studies Ji-Qin Ni Purdue UniversityAlbert J.
Heber Purdue University Matthew J. Darr Iowa State University, darr@iastate.eduTengT. Lim Purdue University ClaudeA. Diehl
Purdue University
3. Design and Implementation of Livestock House Environmental Perception System Based onWireless Sensor Networks
4. Weizheng Shen, Guanting Liu, Zhongbin Su, Rongyu Su andYu Zhang
5. Tech,A.R.B.,Arce,A.I.C.1A, Silva,A.C.S.1B and Costa, E.J.X. 2012.A wireless data acquisition system for cattle behavior
monitoring in zootechnics E-Science1ZAB-FZEA.Arch. Zootec. 61 (234): 175-185. 2012..
6. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms Ran Bezena,b,
Yael Edanb, Ilan Halachmia,b
7. IoT sensors for smart livestock management(Wataru I wasaki etal,2019)
8. Recent advances in wearable sensors for animal health management
9. IoTTechnologies for Livestock Management:A Review of Present Status, Opportunities, and FutureTrends
(Bernard Ijesunor Akhigbe etal 2021)
LPM 513:Lecture1-Mechanisation in livestock by Dr.AyonTarafdar,Scientist,LPM Division