This is a talk delivered at PyConNG conference 2017 and it describes explicitly some projects in Africa which are using data science to solve serious problems and drive economic growth.
IOT can be used for smart farming applications by connecting devices to monitor and automate agricultural tasks. Soil moisture sensors, temperature sensors, and PIR motion sensors connected to an Arduino board can help farmers precisely manage crop watering, detect predators for pest management, and monitor field conditions. This allows for optimized water usage, high crop yields, and reduced damage compared to traditional farming methods. While the upfront costs may be high, IOT for agriculture can increase profits for farmers through greater productivity and efficiency.
This document discusses smart agriculture and Internet of Things (IoT) sensors. It describes how modern agriculture uses data from various sources to manage farm activities, while smart agriculture focuses on soil conditions, weather, and crops. Smart systems differentiate themselves by recording and analyzing data to provide actionable insights. The document then lists several factors that can be measured by IoT sensors, including soil temperature, moisture, weather conditions, and provides details on how soil moisture and rain drop sensors work to measure these variables.
This document discusses how IoT can be applied to smart agriculture. It begins by defining IoT and explaining its applications, particularly in agriculture where it can help boost yields, monitor crops and livestock, and make farming more efficient. It then outlines several major IoT applications in agriculture like soil mapping, irrigation control, and precision fertilizer application. The document also discusses technologies used like sensors, automated equipment, and cloud computing. It ends by addressing current challenges in agriculture and future expectations around technologies like IoT, robots, and vertical farming.
This document summarizes Precision AI, a company that is developing artificial intelligence technology to help farmers apply agricultural chemicals like herbicides more precisely and efficiently. The company was founded in 2018 and has 24 employees. Precision AI uses drones equipped with cameras and AI models to identify weeds and generate precise maps of where herbicides need to be applied. This allows farmers to spray only the weeds and avoid spraying bare soil, resulting in potential savings of 50% of chemical costs. Precision AI aims to launch its first product, an AI-enabled ground spraying system, in 2022 and develop autonomous drone spraying capabilities going forward to further reduce farming costs and environmental impact.
We can predict soil moisture level and motion of predators.
Irrigation system can be monitored .
Damage caused by predators is reduced.
Increased productivity.
Water conservation.
Profit to farmers.
Early detection of diseases, precision agriculture through IoT sensors, and calculating crop yields using drone images and AI are three promising use cases for applying AI to agriculture. AI can help farmers detect plant diseases earlier through image analysis of crop fields, optimize water and pesticide use through real-time soil and environment monitoring, and estimate crop yields automatically. These applications of AI could significantly impact farmers and national economies by improving agricultural outcomes.
Both climate change and global food demand are expected to become more severe in the upcoming decades. In terms of consistently growing population, the agricultural industry will need to embrace better methods to feed our people with a sufficient and healthy supply of food. The Internet of Things technology (IoT) is a breakthrough technology system that evolved from the convergence of wireless technologies and the Internet. Machine-to-machine (M2M) communication systems will be embedded in an objects’ manufacture and will operate automatically without human-to-computer interaction. This will allow information to be transmitted among wireless devices amongst the machines themselves. With IoT innovation, farmers and growers will be able to boost productivity, strengthen pest control and reduce possible energy waste during cultivation.
This document discusses Internet of Things (IoT) applications in agriculture. It describes how IoT can help with crop water management through soil moisture sensors, pest management using motion sensors, and precision agriculture. Sensors monitor soil moisture and detect predator movement, sending alerts to farmers. This allows for optimized irrigation, reduced crop damage, water conservation, and increased productivity and profits for farmers.
IOT can be used for smart farming applications by connecting devices to monitor and automate agricultural tasks. Soil moisture sensors, temperature sensors, and PIR motion sensors connected to an Arduino board can help farmers precisely manage crop watering, detect predators for pest management, and monitor field conditions. This allows for optimized water usage, high crop yields, and reduced damage compared to traditional farming methods. While the upfront costs may be high, IOT for agriculture can increase profits for farmers through greater productivity and efficiency.
This document discusses smart agriculture and Internet of Things (IoT) sensors. It describes how modern agriculture uses data from various sources to manage farm activities, while smart agriculture focuses on soil conditions, weather, and crops. Smart systems differentiate themselves by recording and analyzing data to provide actionable insights. The document then lists several factors that can be measured by IoT sensors, including soil temperature, moisture, weather conditions, and provides details on how soil moisture and rain drop sensors work to measure these variables.
This document discusses how IoT can be applied to smart agriculture. It begins by defining IoT and explaining its applications, particularly in agriculture where it can help boost yields, monitor crops and livestock, and make farming more efficient. It then outlines several major IoT applications in agriculture like soil mapping, irrigation control, and precision fertilizer application. The document also discusses technologies used like sensors, automated equipment, and cloud computing. It ends by addressing current challenges in agriculture and future expectations around technologies like IoT, robots, and vertical farming.
This document summarizes Precision AI, a company that is developing artificial intelligence technology to help farmers apply agricultural chemicals like herbicides more precisely and efficiently. The company was founded in 2018 and has 24 employees. Precision AI uses drones equipped with cameras and AI models to identify weeds and generate precise maps of where herbicides need to be applied. This allows farmers to spray only the weeds and avoid spraying bare soil, resulting in potential savings of 50% of chemical costs. Precision AI aims to launch its first product, an AI-enabled ground spraying system, in 2022 and develop autonomous drone spraying capabilities going forward to further reduce farming costs and environmental impact.
We can predict soil moisture level and motion of predators.
Irrigation system can be monitored .
Damage caused by predators is reduced.
Increased productivity.
Water conservation.
Profit to farmers.
Early detection of diseases, precision agriculture through IoT sensors, and calculating crop yields using drone images and AI are three promising use cases for applying AI to agriculture. AI can help farmers detect plant diseases earlier through image analysis of crop fields, optimize water and pesticide use through real-time soil and environment monitoring, and estimate crop yields automatically. These applications of AI could significantly impact farmers and national economies by improving agricultural outcomes.
Both climate change and global food demand are expected to become more severe in the upcoming decades. In terms of consistently growing population, the agricultural industry will need to embrace better methods to feed our people with a sufficient and healthy supply of food. The Internet of Things technology (IoT) is a breakthrough technology system that evolved from the convergence of wireless technologies and the Internet. Machine-to-machine (M2M) communication systems will be embedded in an objects’ manufacture and will operate automatically without human-to-computer interaction. This will allow information to be transmitted among wireless devices amongst the machines themselves. With IoT innovation, farmers and growers will be able to boost productivity, strengthen pest control and reduce possible energy waste during cultivation.
This document discusses Internet of Things (IoT) applications in agriculture. It describes how IoT can help with crop water management through soil moisture sensors, pest management using motion sensors, and precision agriculture. Sensors monitor soil moisture and detect predator movement, sending alerts to farmers. This allows for optimized irrigation, reduced crop damage, water conservation, and increased productivity and profits for farmers.
Internet of Things ( IOT) in AgricultureAmey Khebade
IOT applications in agriculture allow farmers to more efficiently monitor soil conditions, control irrigation, and track livestock. Sensors can measure soil moisture and temperature to automate irrigation only when needed, reducing water and fertilizer waste. Wireless sensors attached to cows generate health and location data to help farmers. Drones and smart irrigation systems also help optimize crop growth and resource use through remote monitoring and automated controls.
Artificial Intelligence is one of the emerging technologies in the field of agriculture which tries to simulate human reasoning in intelligent systems. It is making a revolution in agriculture by replacing inefficient traditional methods with more efficient AI based methods. AI is used in agriculture in various ways such as automation, robots, drones, soil and crop monitoring, and predictive analytics. This paper provides various applications of AI tools in agriculture. Matthew N. O. Sadiku | Sarhan M. Musa | Abayomi Ajayi-Majebi "Artificial Intelligence in Agriculture" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38513.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/38513/artificial-intelligence-in-agriculture/matthew-n-o-sadiku
Application of DATA SCIENCE in Agricultural Industry.pptxShreyaBose29
Data science involves extracting knowledge and insights from structured and unstructured data using statistical and computational methods, machine learning, and artificial intelligence. It has many applications in agriculture, including precision farming to optimize crop yields, monitoring crop and livestock health to prevent disease spread, analyzing soil properties and weather patterns to improve management practices, and predicting crop yields. While data science can help farmers increase profits and sustainability, challenges remain such as limited data availability, data quality issues, lack of interoperability between data sources, and low adoption rates among farmers due to lack of awareness. Emerging technologies, greater collaboration, and strengthened data privacy are needed for data science to reach its full potential in agriculture.
This document presents a project on the design and analysis of an IoT-based fully automatic space gardening system. A group of students created a small engineered greenhouse that can grow plants by controlling the environment using sensors and a Raspberry Pi microcontroller. The system monitors water levels, temperature, light, moisture, and pH to automatically control plant growth. It was presented with the goal of developing sustainable food production for long-term space missions.
This document describes an IoT project using an Arduino Uno, GSM shield, and relay to remotely control a water pump via mobile phone. The system allows farmers to turn a water pump on and off from anywhere using an AT command text message to the GSM shield. Key hardware includes the Arduino for control logic, GSM shield for cellular connectivity, and relay to switch the water pump power. The system aims to help farmers remotely operate water pumps for irrigation from off-site locations using a mobile phone.
1) The presentation discusses the use of IoT (Internet of Things) in agriculture, including how sensors can provide farmers real-time data on crop yields, weather, soil nutrition to improve techniques. 2) IoT applications presented include crop monitoring, weather monitoring, soil testing, farm machinery navigation using drones, robots, and sensors. 3) While IoT can save time, improve security and efficiency, barriers to adoption include lack of infrastructure, high costs, and issues around security and privacy.
Artificial intelligence has great potential to help address challenges in agriculture and improve efficiency. It can be used for weather forecasting to help farmers determine optimal sowing times, soil and crop health monitoring to identify nutrient deficiencies and diseases, and analyzing crop health with drones to detect issues early. While AI is already being used in these applications, the industry remains underserved and challenges like irregular water access and climate change still exist. Further development of robust AI solutions could help automate farming tasks to boost yields and quality using fewer resources to help address food demands of a growing population.
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
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
This document defines smart farming as using modern technology to increase agricultural production and quality. It discusses the history of smart agriculture focusing on supporting development and food security. The objectives of smart farming are to sustainably increase yields and incomes while adapting to climate change and reducing emissions. The advantages include maximizing outputs with minimal resources, while disadvantages are reliance on continuous internet and farmers learning new technologies.
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.
Artificial intelligence has the potential to help address challenges facing the agricultural sector as the global population increases. New technologies like drones, driverless tractors, automated irrigation, and machine learning are helping farmers monitor crops and soils, apply inputs precisely, and increase yields. Startups are developing tools using computer vision, satellites, and deep learning to diagnose plant health, predict weather, and optimize resource use. These AI solutions aim to help farmers "do more with less" and help feed the world's growing population in a sustainable way.
This document discusses how IoT can help transform agriculture through smart farming applications. It begins by discussing how IoT has four main components: digital sensors, connectivity, middleware, and applications/analytics. It then discusses how IoT is becoming more viable through cheaper hardware, better software development, and improved connectivity. The document outlines several smart agriculture applications of IoT including increased business efficiency through automation, enhanced product quality and volumes, ability to detect anomalies, and data collection. It concludes by discussing IoT maturity phases and the benefits of using technologies like drones for tasks like irrigation, fertilizing, crop monitoring and analysis.
Future of Indian Agricultural Education: Must-Have Skills and Creative Capaci...B SWAMINATHAN
Here are 5 cases with job descriptions and required skills/qualifications for agricultural jobs. The cases provide a brief survey of the types of skills and qualifications needed for roles in areas like procurement, sales, research, project assistance and more. This overview highlights the diversity of career opportunities for agricultural graduates and the mix of technical knowledge and soft skills required.
How are drones used for farming? The use of drones in agriculture is the future. Heavy lift drones capable of crop dusting and drones equipped with multispectral sensors will change the way in which farming is done.
The document discusses the concept of Internet of Things (IoT) and its applications in agriculture. It defines IoT and describes how physical objects can be connected to collect and exchange data. Some key applications of IoT in agriculture mentioned include monitoring soil moisture and temperature for controlled irrigation, livestock monitoring, pest monitoring, and mobile money transfers. However, constraints for implementing IoT in Indian agriculture include small land holdings, connectivity and affordability issues. Some case studies on precision agriculture and reducing water usage through IoT are also summarized.
Internet of Things & Its application in Smart AgricultureMohammad Zakriya
As we know Agriculture plays vital role in the development of agricultural country. In India about 70% of population depends upon farming and one third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the development of the country. The only solution to this problem is smart agriculture by modernizing the current traditional methods of agriculture. Hence the project aims at making agriculture smart using automation and IoT technologies.
Artificial Intelligence In Agriculture & Its Status in IndiaJanhviTripathi
Worldwide, agriculture is a $5 trillion industry, and with the ever increasing population, the world will need to produce 50% more food by 2050 which cannot be accomplished with the percentage of land under cultivation. Factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield. As a result, Artificial Intelligence is steadily emerging as part of the industry’s technological evolution which help can help farmers get more from the land while using resources more sustainably, yielding healthier crops, control pests, monitor soil, help with workload, etc
*All the media belongs to the respective owners*
Contact me for further queries & discussions...
Innovation Workshop: Global Best Innovative Practices in AgricultureHasan Zaman
This document outlines global best innovative practices in agriculture from 2003-2012 and the state of affairs in Bangladesh. It discusses how contract farming and producers' organizations have helped small farms adopt new technologies. Examples from South Korea, Thailand, South Africa, Brazil, Rwanda, and India show how information networks, participatory irrigation management, integrated decision support systems, sustainable development areas, and digital platforms have empowered farmers and improved incomes. Bangladesh's national agricultural policy now includes e-agriculture and public-private partnerships to improve extension services and farmer access to information through initiatives like Digital Bangladesh.
Role and applications of ICT in Organic FarmingSowmyaNataraj3
This document discusses the role of information and communication technologies (ICT) in organic farming. It begins with definitions of ICT and describes how ICT can benefit agriculture sectors through online services, e-commerce, and facilitating interactions. The document then discusses several ways ICT supports organic farming, including increasing access to information, aiding production and management, providing advisory services, enabling marketing and inputs access, and reducing greenhouse gas emissions. It provides examples of mobile apps, websites, and portals that provide information on organic farming. Finally, it summarizes several research studies that examined the use of ICT in organic farming advisory services and extension in countries like Bangladesh, Zambia, India, and a controlled study comparing ICT and traditional
Internet of Things ( IOT) in AgricultureAmey Khebade
IOT applications in agriculture allow farmers to more efficiently monitor soil conditions, control irrigation, and track livestock. Sensors can measure soil moisture and temperature to automate irrigation only when needed, reducing water and fertilizer waste. Wireless sensors attached to cows generate health and location data to help farmers. Drones and smart irrigation systems also help optimize crop growth and resource use through remote monitoring and automated controls.
Artificial Intelligence is one of the emerging technologies in the field of agriculture which tries to simulate human reasoning in intelligent systems. It is making a revolution in agriculture by replacing inefficient traditional methods with more efficient AI based methods. AI is used in agriculture in various ways such as automation, robots, drones, soil and crop monitoring, and predictive analytics. This paper provides various applications of AI tools in agriculture. Matthew N. O. Sadiku | Sarhan M. Musa | Abayomi Ajayi-Majebi "Artificial Intelligence in Agriculture" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38513.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/38513/artificial-intelligence-in-agriculture/matthew-n-o-sadiku
Application of DATA SCIENCE in Agricultural Industry.pptxShreyaBose29
Data science involves extracting knowledge and insights from structured and unstructured data using statistical and computational methods, machine learning, and artificial intelligence. It has many applications in agriculture, including precision farming to optimize crop yields, monitoring crop and livestock health to prevent disease spread, analyzing soil properties and weather patterns to improve management practices, and predicting crop yields. While data science can help farmers increase profits and sustainability, challenges remain such as limited data availability, data quality issues, lack of interoperability between data sources, and low adoption rates among farmers due to lack of awareness. Emerging technologies, greater collaboration, and strengthened data privacy are needed for data science to reach its full potential in agriculture.
This document presents a project on the design and analysis of an IoT-based fully automatic space gardening system. A group of students created a small engineered greenhouse that can grow plants by controlling the environment using sensors and a Raspberry Pi microcontroller. The system monitors water levels, temperature, light, moisture, and pH to automatically control plant growth. It was presented with the goal of developing sustainable food production for long-term space missions.
This document describes an IoT project using an Arduino Uno, GSM shield, and relay to remotely control a water pump via mobile phone. The system allows farmers to turn a water pump on and off from anywhere using an AT command text message to the GSM shield. Key hardware includes the Arduino for control logic, GSM shield for cellular connectivity, and relay to switch the water pump power. The system aims to help farmers remotely operate water pumps for irrigation from off-site locations using a mobile phone.
1) The presentation discusses the use of IoT (Internet of Things) in agriculture, including how sensors can provide farmers real-time data on crop yields, weather, soil nutrition to improve techniques. 2) IoT applications presented include crop monitoring, weather monitoring, soil testing, farm machinery navigation using drones, robots, and sensors. 3) While IoT can save time, improve security and efficiency, barriers to adoption include lack of infrastructure, high costs, and issues around security and privacy.
Artificial intelligence has great potential to help address challenges in agriculture and improve efficiency. It can be used for weather forecasting to help farmers determine optimal sowing times, soil and crop health monitoring to identify nutrient deficiencies and diseases, and analyzing crop health with drones to detect issues early. While AI is already being used in these applications, the industry remains underserved and challenges like irregular water access and climate change still exist. Further development of robust AI solutions could help automate farming tasks to boost yields and quality using fewer resources to help address food demands of a growing population.
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
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
This document defines smart farming as using modern technology to increase agricultural production and quality. It discusses the history of smart agriculture focusing on supporting development and food security. The objectives of smart farming are to sustainably increase yields and incomes while adapting to climate change and reducing emissions. The advantages include maximizing outputs with minimal resources, while disadvantages are reliance on continuous internet and farmers learning new technologies.
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.
Artificial intelligence has the potential to help address challenges facing the agricultural sector as the global population increases. New technologies like drones, driverless tractors, automated irrigation, and machine learning are helping farmers monitor crops and soils, apply inputs precisely, and increase yields. Startups are developing tools using computer vision, satellites, and deep learning to diagnose plant health, predict weather, and optimize resource use. These AI solutions aim to help farmers "do more with less" and help feed the world's growing population in a sustainable way.
This document discusses how IoT can help transform agriculture through smart farming applications. It begins by discussing how IoT has four main components: digital sensors, connectivity, middleware, and applications/analytics. It then discusses how IoT is becoming more viable through cheaper hardware, better software development, and improved connectivity. The document outlines several smart agriculture applications of IoT including increased business efficiency through automation, enhanced product quality and volumes, ability to detect anomalies, and data collection. It concludes by discussing IoT maturity phases and the benefits of using technologies like drones for tasks like irrigation, fertilizing, crop monitoring and analysis.
Future of Indian Agricultural Education: Must-Have Skills and Creative Capaci...B SWAMINATHAN
Here are 5 cases with job descriptions and required skills/qualifications for agricultural jobs. The cases provide a brief survey of the types of skills and qualifications needed for roles in areas like procurement, sales, research, project assistance and more. This overview highlights the diversity of career opportunities for agricultural graduates and the mix of technical knowledge and soft skills required.
How are drones used for farming? The use of drones in agriculture is the future. Heavy lift drones capable of crop dusting and drones equipped with multispectral sensors will change the way in which farming is done.
The document discusses the concept of Internet of Things (IoT) and its applications in agriculture. It defines IoT and describes how physical objects can be connected to collect and exchange data. Some key applications of IoT in agriculture mentioned include monitoring soil moisture and temperature for controlled irrigation, livestock monitoring, pest monitoring, and mobile money transfers. However, constraints for implementing IoT in Indian agriculture include small land holdings, connectivity and affordability issues. Some case studies on precision agriculture and reducing water usage through IoT are also summarized.
Internet of Things & Its application in Smart AgricultureMohammad Zakriya
As we know Agriculture plays vital role in the development of agricultural country. In India about 70% of population depends upon farming and one third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the development of the country. The only solution to this problem is smart agriculture by modernizing the current traditional methods of agriculture. Hence the project aims at making agriculture smart using automation and IoT technologies.
Artificial Intelligence In Agriculture & Its Status in IndiaJanhviTripathi
Worldwide, agriculture is a $5 trillion industry, and with the ever increasing population, the world will need to produce 50% more food by 2050 which cannot be accomplished with the percentage of land under cultivation. Factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield. As a result, Artificial Intelligence is steadily emerging as part of the industry’s technological evolution which help can help farmers get more from the land while using resources more sustainably, yielding healthier crops, control pests, monitor soil, help with workload, etc
*All the media belongs to the respective owners*
Contact me for further queries & discussions...
Innovation Workshop: Global Best Innovative Practices in AgricultureHasan Zaman
This document outlines global best innovative practices in agriculture from 2003-2012 and the state of affairs in Bangladesh. It discusses how contract farming and producers' organizations have helped small farms adopt new technologies. Examples from South Korea, Thailand, South Africa, Brazil, Rwanda, and India show how information networks, participatory irrigation management, integrated decision support systems, sustainable development areas, and digital platforms have empowered farmers and improved incomes. Bangladesh's national agricultural policy now includes e-agriculture and public-private partnerships to improve extension services and farmer access to information through initiatives like Digital Bangladesh.
Role and applications of ICT in Organic FarmingSowmyaNataraj3
This document discusses the role of information and communication technologies (ICT) in organic farming. It begins with definitions of ICT and describes how ICT can benefit agriculture sectors through online services, e-commerce, and facilitating interactions. The document then discusses several ways ICT supports organic farming, including increasing access to information, aiding production and management, providing advisory services, enabling marketing and inputs access, and reducing greenhouse gas emissions. It provides examples of mobile apps, websites, and portals that provide information on organic farming. Finally, it summarizes several research studies that examined the use of ICT in organic farming advisory services and extension in countries like Bangladesh, Zambia, India, and a controlled study comparing ICT and traditional
SocialCops and UN Papua New Guinea: Presentation for Data Stocktaking WorkshopSocialCops
SocialCops presented at the UN Papua New Guinea's workshop on how Papua New Guinea can track its progress toward the UN Sustainable Development Goals and Vision 2050
Most businesses nowadays have an IT department to manage the technical side of their organization, with activities ranging from network and system administration to software development, and security. So, what exactly are these systems and why is digital technology important for businesses? Let's take a look.Definition of digital technology
The definition of digital technology encompasses digital devices, systems, and resources that help create, store, and manage data. An important aspect of digital technology is information technology (IT) which refers to the use of computers to process data and information. Most businesses use digital technology nowadays to manage operations and processes and to enhance the customer journey.
Importance of digital technology
Consumer behavior is changing, from searching and sharing information to shopping for actual products. To adapt, companies must adopt digital technology to assist customers through their buying journey.
Many businesses have a website and social media accounts to inform and educate customers about their products and services. A lot of them also accompany their brick-and-mortar business model with an eCommerce store to offer customers a more flexible shopping experience. Some innovative enterprises even make use of advanced technology like virtual reality and augmented reality to attract and engage their target groups.
Companies also adopt digital technology to increase their profitability. Since one advantage of technology is limitless communication, companies can extend their reach beyond domestic boundaries and access millions of customers worldwide.
Finally, digital transformation is not just important but a requirement for all modern businesses, as the majority of firms automate their processes, firms who refuse to make the change will lag behind and lose their competitive advantage. On the other hand, there are various incentives for companies to digitize. For example, production will run faster since machines are replacing humans in repetitive tasks. So, the coordination of corporate data in one system Allows everyone to work together more seamlessly. Digital technology examples in business
Technology is widely used by businesses to manage internal processes and enhance customer experience.
Digital Technology: Enterprise resource planning
Enterprise resource planning (ERP) is the use of technology and software to manage the main processes of a business in real-time.
It is part of business management software that allows companies to collect, store, monitor, and analyze data from various corporate activities.
Benefits of ERP :
Coordinate data from different departments to help the managers make better and more informed decisions.
Create a central database for managers to check all the supply chain activities in one place.
Disadvantages of ERP:
Require a lot of time and resources to set up.
Require a large number of workers to undergo training.
The document summarizes an upcoming meeting organized by CTA to discuss strengthening e-Agriculture strategies in ACP countries. The meeting will bring together participants from government, farmers organizations, private sector, and international organizations to review the need for ICT strategies in agriculture and identify actions to strengthen their formulation and implementation. It will also discuss innovative tools and projects supporting the implementation of these strategies. A preparatory online discussion will identify issues regarding developing inclusive and efficient ICT strategies for agriculture and review existing processes in ACP countries.
The document discusses an upcoming meeting organized by CTA to focus on strengthening e-Agriculture strategies and policies in ACP countries. The meeting will bring together participants from government, farmers organizations, private sector, and international organizations to review the need for ICT strategies in agriculture, identify actions to strengthen their formulation and implementation, and discuss tools and projects to support the strategies. A preparatory online discussion will also take place to identify issues regarding developing inclusive and efficient ICT strategies for agriculture and examples from ACP countries. The goal is to produce guidance on strengthening e-agriculture strategies to better integrate technologies into agricultural activities.
Information and Communication Technology in dissemination of Agricultural Tec...Lokesh Waran
Information and Communication Technology in dissemination of Agricultural Technologies
Dr.J.Meenambigai
Associate Professor
Department of agricultural Extension
Faculty of Agriculture
Annamalai University
Chidambaram
Internet of Things Hydroponics Agriculture (IoTHA) Using Web/Mobile ApplicationsIRJET Journal
This document discusses using Internet of Things (IoT) technology to develop an automated hydroponics system using web and mobile applications. The system would monitor and control nutritional and environmental parameters to optimize plant growth without soil. Specifically, it details a study that developed an IoT-driven hydroponics testbed to cultivate tomatoes and collect data on their nutrient requirements and productivity. The results showed the plants grew well based on computer vision and visual analysis. Adopting this type of hydroponics farming using IoT could help address limited agricultural land and increase production capacity by allowing year-round farming.
Study use of ict for agriculture in giz projects giz snrd africaAgridurable
This document provides an overview of ICT for Agriculture (ICT4Ag), including its definition, past lessons, and current use in GIZ projects. ICT4Ag has the potential to transform smallholder farming and food security by providing farmers access to information on weather, prices, and best practices via technologies like mobile phones. However, past projects show that technology alone is not sufficient and must be well-integrated with human and social factors. The document then examines several current GIZ projects utilizing ICT4Ag across Africa and Asia to improve extension services, value chains, and innovation in the agricultural sector.
This document discusses the role of information technology in Indian agriculture. It outlines how IT can increase food production and productivity through tools like weather forecasting, digital marketplaces, mobile advisory services, greenhouse monitoring technologies, and GPS/GIS systems. The document also examines IT initiatives in India, benefits of IT for farmers, and challenges to expanding agricultural IT, with the goal of improving decision making and farm management through information access.
This is a presentation on ICT for development, presented to DFID, India for fund raising. This is a part of the United Nations Information Technology Services (UNITeS) programme. This programme was a finalist in the Stockholm Challenge Award 2001
International Food Policy Research Institute (IFPRI). 2023. Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 1. Stakeholder engagement for impacts. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
The document discusses the role of ICT in agricultural transformation through the experience of the Biovision Farmer Communication Programme in Kenya. It describes how ICT can enhance agricultural production through providing information on pest control, new varieties, and production optimization. ICT also improves markets by enabling access to up-to-date market data on prices and trends. Additionally, ICT builds farmer capacities by strengthening representation and social connections. The Biovision Programme has integrated ICT into its projects through a website, radio show, learning centers, mobile services, and call center to provide agricultural information to farmers.
This document summarizes the findings of a study on e-Agriculture policies and strategies in selected ACP and non-ACP countries. The study found that while a few countries like Ghana, Ivory Coast, Rwanda, Mali, Burkina Faso, and Bolivia have initiated e-Agriculture strategies or policies, most ACP countries have not developed or do not understand the need for such strategies. It identifies challenges around stakeholder engagement, infrastructure, and capacity. The document recommends that CTA and partners create task forces, develop policy toolkits, provide awareness and capacity building support, and ensure local leadership and relevance to farmers to help countries develop effective national e-Agriculture policies.
Summary of findings - e-agriculture strategies in the ACPNawsheen Hosenally
This document summarizes the findings of a study on e-Agriculture policies and strategies in selected ACP and non-ACP countries. The study found that while a few countries like Ghana, Ivory Coast, Rwanda, Mali, Burkina Faso, and Bolivia have initiated e-Agriculture strategies or policies, most ACP countries have not developed or do not understand the need for such strategies. It identifies challenges around stakeholder engagement, infrastructure, and capacity. The document recommends that CTA and partners create task forces, develop policy toolkits, provide awareness and capacity building support, and ensure local leadership and relevance to farmers to help more ACP countries develop effective national e-Agriculture policies
The document summarizes the potential of digital innovations to manage climate risks in food systems. It discusses how digital tools can provide timely insights to farmers, how technologies can help manage climate risks across the food supply chain, and how digital innovations in forecasting support climate science. It also outlines some key challenges, such as insufficient digital infrastructure in rural areas and gender divides. Finally, it proposes recommendations like investing in bridging digital divides, strengthening information systems, and coordinating with actors to build digital capabilities.
The document outlines a project to create an interactive crowdmap that collects information on successful ICT4Ag (Information and Communication Technologies for Agriculture) initiatives across Africa. Field researchers will visit projects in countries like Senegal, Kenya, Uganda and Ghana to understand how ICTs influence farmer traditions. The crowdmap aims to both provide farmers with information on best practices and connect the strongest project ideas with potential financial supporters.
This document discusses greening digital systems in the NHS. It notes that data centers account for 2-3% of the UK's electricity use and the ICT industry could cut global greenhouse gas emissions by 15% and save up to €600 billion by 2020. The document proposes getting buy-in from key NHS leaders, creating a pilot group to trial existing sustainability tools, and determining what additional support is needed to advance greening the digital agenda in the NHS.
How do we Achieve Universal Access to Equitable Sanitation & Hygiene By 2030? Driving focus on behaviour change to ensure good hygiene practice and educate on self-sufficient practices to reduce the spread of preventable disease such as diarrhoea. A lead2030 Challenge Supported By Reckitt Benckiser (RB), A ONE YOUNG WORLD INITIATIVE FOR GLOBAL GOALS FOR SUSTAINABLE DEVELOPMENT. set by the United Nations General Assembly in 2015. The SDGs are part of Resolution 70/1 of the United Nations General Assembly "Transforming our World the 2030 Agenda".
ICTs can be used to provide information and communication services to wide users. They facilitate collecting, storing, and analyzing information that can be transmitted electronically. Examples of ICT uses include e-governance projects in India like Gyandoot in Madhya Pradesh, which established internet kiosks to provide government services to rural citizens, reducing time, costs, and improving access to information. ICTs can also connect rural communities, support economic development through initiatives like e-Choupal, and improve education, health, and community development.
Similar to how data science is solving life-threatening problems in Africa (20)
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
3. Some problems in Africa which data is solving
● Corruption: this costs Africa $68billion/year
● Agricultural products wastage: costs South Africa alone
$2.7billion
● Early detection and treatment of Tuberculosis
● Road infrastructure problems
● Post-election violence The good news: Data science can be used to
jumpstart the solution to all of Africa’s major
problems
9. BudgIT
Using data visualization to solve
corruption problems
BudgIT reduces corruption
by simplifying budget and
public spending data with
the aim of promoting
transparency and
accountability through data
visualization
10. Kudu in Uganda
Using data to solve food
wastage
Kudu uses mobile data
technology to reduce food and
waste by providing data which
informs government and food
organizations of which part of
Uganda produces a certain type
of food crop, what amount of
food can feed Uganda and the
necessary infrastructure to put
in place in order to reduce food
wastage
11. IBM project
Using data mapping to solve
cancer and tuberculosis
IBM started a data mapping project
to solve the problem of late
detection of tuberculosis and track
metastasis in cancer patients
12. Ma3Route
Using data to solve the problem
of road infrastructure
Kenya’s ma3Route uses real
time data to provide traffic
information to drivers and
passengers. The data will give
guided information on which
route people ply the most. This
will help the government to
make an informed decision on
the best place to build a road
network.
13. Umati
Using data to solve
Kenya’s Umati project tracks
hate speeches during election
periods and prevents them from
going viral on social media by
blocking them.