This is a PPT of Robotics, the topic is Robot Perception for agriculture.
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Robotics for Agricultural Automation_ All You Need to Know.pdfCIOWomenMagazine
In this article, we will delve into the various aspects of robotics for agricultural automation, exploring its benefits, applications, challenges, and future prospects.
The document discusses the use of robots in agriculture. It begins with an introduction to robots and their main components. The next sections cover the need for agricultural robots, types of robots used, and how they are applied in tasks like spraying, weeding, harvesting. The document also discusses autonomous robots, tele-controlled robots and examples like fruit picking robots. It explores the future scope of robot suits and solar-powered robots. In conclusion, robots can benefit agriculture by improving efficiency, productivity and product quality while reducing labor costs and use of pesticides.
Revolutionizing Agriculture with Robots.pptxADISHPRAMOD
Agriculture robots have the potential to revolutionize farming by performing tasks more efficiently and sustainably. They can automate labor-intensive jobs like planting, harvesting, and monitoring crops. This reduces costs and labor needs while improving yields. Current robot types include autonomous tractors, harvesting machines, weeding systems and drones. Future agriculture robots may be more autonomous, multi-functional and specialized through advances in AI, robotics and data analysis. While high upfront costs remain a challenge, robotics have significant advantages for increasing agricultural productivity and yields over time through precision farming.
This document discusses the future of robotic agriculture. It begins with an introduction to robots and their ability to perform complex tasks automatically. It then outlines various types of agricultural robots, including those that can harvest crops, apply pesticides precisely via computer vision, and more. The document discusses the need for robotic agriculture to help with labor-intensive tasks. It explores applications like harvesting and utilities. Benefits include reduced pesticide use and focusing farmers on yields. Challenges involve jobs loss and high costs initially. The conclusion is that robotic opportunities in agriculture are vast and the technology can help overcome problems.
Artificial intelligence can benefit the agriculture sector by increasing productivity and sustainability. As the global population grows, AI technologies like drones, automated systems, agricultural robots, remote sensing, and decision support systems can help monitor crop conditions, identify issues, automate processes, and support farmers' decisions. While these applications may have initial financial and expertise barriers, their benefits include enhanced crop yields, quality and safety, efficient farm management, and reduced risks. Overall, AI can help modernize agriculture and optimize outputs to better feed the world's growing population.
This document discusses agricultural robots and their uses. It begins with introductions to robots and agricultural robotics. Agricultural robots can be used for tasks like harvesting, weed control, forestry work, horticulture, and fruit picking. They allow processes like plowing, seeding, fertilizing, weeding and harvesting to be done with less manual labor, saving time and money. The document then describes a specific robot the author developed that can detect plant diseases using image processing and then spray only the affected plants with pesticide, reducing waste and pollution compared to uniform spraying. It concludes that this targeted treatment can help farmers better control diseases and pesticides in an environmentally friendly manner.
This document discusses how robotics and automation can help increase agricultural productivity and efficiency to meet rising global food demands. It provides examples of different types of agricultural robots currently being developed and used, such as fruit picking robots, drones for crop monitoring, forestry robots, and weed removal robots. The benefits of agricultural robots include reducing manual labor needs, increasing production speeds and yields, allowing 24-hour operation, and improving precision. Swarm robotics approaches using multiple small robots working cooperatively, such as projects involving "Robobees," are also discussed as a potential solution for pollination.
IRJET - Bluetooth Controlled Farm RobotIRJET Journal
1) The document describes a Bluetooth-controlled farm robot that was developed to perform agricultural operations like plowing, seeding, fruit picking, and pesticide spraying.
2) The robot uses Bluetooth to allow for manual control via a pairing app, which helps navigate the robot outside of fields.
3) The goal of the robot is to automate agricultural operations to increase yields and efficiency while decreasing labor costs and improving profitability for farmers.
Robotics for Agricultural Automation_ All You Need to Know.pdfCIOWomenMagazine
In this article, we will delve into the various aspects of robotics for agricultural automation, exploring its benefits, applications, challenges, and future prospects.
The document discusses the use of robots in agriculture. It begins with an introduction to robots and their main components. The next sections cover the need for agricultural robots, types of robots used, and how they are applied in tasks like spraying, weeding, harvesting. The document also discusses autonomous robots, tele-controlled robots and examples like fruit picking robots. It explores the future scope of robot suits and solar-powered robots. In conclusion, robots can benefit agriculture by improving efficiency, productivity and product quality while reducing labor costs and use of pesticides.
Revolutionizing Agriculture with Robots.pptxADISHPRAMOD
Agriculture robots have the potential to revolutionize farming by performing tasks more efficiently and sustainably. They can automate labor-intensive jobs like planting, harvesting, and monitoring crops. This reduces costs and labor needs while improving yields. Current robot types include autonomous tractors, harvesting machines, weeding systems and drones. Future agriculture robots may be more autonomous, multi-functional and specialized through advances in AI, robotics and data analysis. While high upfront costs remain a challenge, robotics have significant advantages for increasing agricultural productivity and yields over time through precision farming.
This document discusses the future of robotic agriculture. It begins with an introduction to robots and their ability to perform complex tasks automatically. It then outlines various types of agricultural robots, including those that can harvest crops, apply pesticides precisely via computer vision, and more. The document discusses the need for robotic agriculture to help with labor-intensive tasks. It explores applications like harvesting and utilities. Benefits include reduced pesticide use and focusing farmers on yields. Challenges involve jobs loss and high costs initially. The conclusion is that robotic opportunities in agriculture are vast and the technology can help overcome problems.
Artificial intelligence can benefit the agriculture sector by increasing productivity and sustainability. As the global population grows, AI technologies like drones, automated systems, agricultural robots, remote sensing, and decision support systems can help monitor crop conditions, identify issues, automate processes, and support farmers' decisions. While these applications may have initial financial and expertise barriers, their benefits include enhanced crop yields, quality and safety, efficient farm management, and reduced risks. Overall, AI can help modernize agriculture and optimize outputs to better feed the world's growing population.
This document discusses agricultural robots and their uses. It begins with introductions to robots and agricultural robotics. Agricultural robots can be used for tasks like harvesting, weed control, forestry work, horticulture, and fruit picking. They allow processes like plowing, seeding, fertilizing, weeding and harvesting to be done with less manual labor, saving time and money. The document then describes a specific robot the author developed that can detect plant diseases using image processing and then spray only the affected plants with pesticide, reducing waste and pollution compared to uniform spraying. It concludes that this targeted treatment can help farmers better control diseases and pesticides in an environmentally friendly manner.
This document discusses how robotics and automation can help increase agricultural productivity and efficiency to meet rising global food demands. It provides examples of different types of agricultural robots currently being developed and used, such as fruit picking robots, drones for crop monitoring, forestry robots, and weed removal robots. The benefits of agricultural robots include reducing manual labor needs, increasing production speeds and yields, allowing 24-hour operation, and improving precision. Swarm robotics approaches using multiple small robots working cooperatively, such as projects involving "Robobees," are also discussed as a potential solution for pollination.
IRJET - Bluetooth Controlled Farm RobotIRJET Journal
1) The document describes a Bluetooth-controlled farm robot that was developed to perform agricultural operations like plowing, seeding, fruit picking, and pesticide spraying.
2) The robot uses Bluetooth to allow for manual control via a pairing app, which helps navigate the robot outside of fields.
3) The goal of the robot is to automate agricultural operations to increase yields and efficiency while decreasing labor costs and improving profitability for farmers.
Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if the world is unknown or if conditions change and the models become inaccurate .In this paper, an adaptive approach is presented that closes the loop around the vehicle predictions. This approach is applied to an autonomous vehicle known as field robots used in agriculture.
This is the new technology to increase food production mostly horticulture production and also used in Agronomic crop production. This technology can overcome many problems which create problems at farm level as well as storage level.
This document describes the design of an autonomous agricultural robot. The robot is intended to automate farming tasks like plowing, seeding, watering, and leveling soil. It is controlled via an Android app using Bluetooth. Sensors monitor soil moisture, temperature, and humidity to optimize growing conditions. The robot aims to reduce farmer workload and increase crop yields through automated and precise farm operations. Key components include an Arduino microcontroller, motors, sensors, and a mobile app for remote control and data monitoring.
Agricultural robots can perform tasks like harvesting, weed control, monitoring farms, and allowing farmers to increase efficiency and precision. Current agricultural robots include Demeter for harvesting, weed controllers, forest robots, and fruit picking robots. Future agricultural robots may include flying micro robots. Robots provide advantages like working continuously without rest, but disadvantages include potential liability issues and changing the culture of agriculture. Overall, robots can enhance productivity in agriculture by performing dangerous, repetitive tasks.
IoT BASED AUTOMATED PESTICIDE SPRAYER FOR DWARF PLANTSIRJET Journal
This document describes a proposed IoT-based automated pesticide spraying robot for dwarf plants. Some key points:
- The current manual pesticide spraying process exposes farmers to harmful chemicals. This proposed robot aims to reduce that exposure through remote control spraying.
- The robot would use an ESP-based wireless communication system to allow remote control spraying. This would reduce manual labor while increasing safety and efficiency.
- A literature review presented previous work on automated spraying robots and their benefits in reducing pesticide waste, improving coverage, and protecting farmer health. However, most previous systems still required some manual operation or had limitations like short range.
- The proposed robot design aims to address issues with previous systems by allowing fully
IRJET-Survey Paper on Agro-Bot Autonomous RobotIRJET Journal
This document summarizes an agricultural robot that can perform multiple tasks. The robot can conduct crop prediction, weather detection, grass cutting, and 360-degree spraying. It is controlled via an Android application and uses sensors to gather soil moisture, temperature, and other environmental data. The robot aims to increase farming efficiency and productivity through automation.
15 Disadvantages of Automated Farming: Balancing Efficiency with Environment ...CIOWomenMagazine
Here are some key disadvantages of automated farming: 1. Loss of Traditional Farming Practices, 2. Dependency on Technology, 3. High Initial Investment, 4. Environmental Concerns, etc.
An agricultural robot is a robot deployed for agricultural purposes. Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring, and soil analysis.
Agricultural robots automate slow, repetitive, and dull tasks for farmers, allowing them to focus more on improving overall production yields. Some of the most common robots in agriculture are used for: Harvesting and picking
This document discusses machine learning and robotics applications in agriculture. It provides examples of machine learning like self-driving cars and product recommendations. It also discusses advantages like identifying trends/patterns with no human intervention. Agricultural robots discussed include crop harvesting robots, weeding robots, and aerial drones. Challenges of machine learning in agriculture include damage from incorrect robot calibration. Applications discussed are species management, field condition management, crop management, disease and weed detection. Specific agricultural robots and their uses are also outlined.
Increase in the population brings lots of challanges the major being food production.
Smart farming technologies
Typical agriculture value chain
Future farms
Design and Development of a Multifunctional Agrobot “RaithaMitra” for Efficie...IRJET Journal
The document describes the design and development of an agricultural robot called RaithaMitra. It features a mobile robotic platform equipped with sensors, actuators, and instruments to perform various agricultural tasks with accuracy and efficiency. These tasks include weed cutting, pesticide spraying, seed placement, and soil moisture detection. The robot is controlled by an Arduino Uno microcontroller and can be operated through a Bluetooth-connected Android app. It uses DC motors for movement and actuators to control the weed cutter, pesticide sprayer, seed container, and other components. An integrated soil moisture sensor detects water levels and triggers irrigation when levels are low. The multifunctional robotic system aims to improve agricultural productivity and efficiency through precision farming techniques
An agricultural robot is a robot deployed for agricultural purposes. ... Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring and soil analysis.
Agricultural robots automate slow, repetitive, and dull tasks for farmers, allowing them to focus more on improving overall production yields. Some of the most common robots in agriculture are used for: Harvesting and picking.
Automation in agriculture is increasing to address issues like a growing population, labor shortages, and the need for more sustainable and efficient food production. Agricultural robots and autonomous machines are being developed for tasks like fruit picking, tractor operation, pruning, weeding, spraying, milking, and crop monitoring using drones. Automation provides benefits like increased productivity, uniformity of work, reduced labor needs and costs, but also has drawbacks such as high initial costs and a potential reduction in job opportunities. Future trends include using robots for precision pruning and indoor vertical farms for lettuce production.
Research and development in agricultural robotics: A perspective of digital f...Redmond R. Shamshiri
Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
The Future of Farming: Technology and Farm Contractor Partnershipswilliamshakes1
future of farming is bright, thanks to the integration of technology and the valuable contributions of farm contractor partnerships. By embracing these advancements, farmers can overcome traditional challenges and contribute to a more sustainable and food-secure world.
This document describes an IoT-based multipurpose agricultural robot called Agribot. The Agribot uses sensors to monitor soil moisture, temperature, humidity and other field conditions. It is controlled using an Android smartphone connected via ESP8266. The Agribot is designed to perform tasks like plowing, seed sowing, and watering crops with minimal human labor. It aims to increase agricultural productivity and efficiency using evolving IoT technology.
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Contenu connexe
Similaire à ROBOT PERCEPTION FOR AGRICULTURE AND GOOD PRODUCTION1.1.pdf
Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if the world is unknown or if conditions change and the models become inaccurate .In this paper, an adaptive approach is presented that closes the loop around the vehicle predictions. This approach is applied to an autonomous vehicle known as field robots used in agriculture.
This is the new technology to increase food production mostly horticulture production and also used in Agronomic crop production. This technology can overcome many problems which create problems at farm level as well as storage level.
This document describes the design of an autonomous agricultural robot. The robot is intended to automate farming tasks like plowing, seeding, watering, and leveling soil. It is controlled via an Android app using Bluetooth. Sensors monitor soil moisture, temperature, and humidity to optimize growing conditions. The robot aims to reduce farmer workload and increase crop yields through automated and precise farm operations. Key components include an Arduino microcontroller, motors, sensors, and a mobile app for remote control and data monitoring.
Agricultural robots can perform tasks like harvesting, weed control, monitoring farms, and allowing farmers to increase efficiency and precision. Current agricultural robots include Demeter for harvesting, weed controllers, forest robots, and fruit picking robots. Future agricultural robots may include flying micro robots. Robots provide advantages like working continuously without rest, but disadvantages include potential liability issues and changing the culture of agriculture. Overall, robots can enhance productivity in agriculture by performing dangerous, repetitive tasks.
IoT BASED AUTOMATED PESTICIDE SPRAYER FOR DWARF PLANTSIRJET Journal
This document describes a proposed IoT-based automated pesticide spraying robot for dwarf plants. Some key points:
- The current manual pesticide spraying process exposes farmers to harmful chemicals. This proposed robot aims to reduce that exposure through remote control spraying.
- The robot would use an ESP-based wireless communication system to allow remote control spraying. This would reduce manual labor while increasing safety and efficiency.
- A literature review presented previous work on automated spraying robots and their benefits in reducing pesticide waste, improving coverage, and protecting farmer health. However, most previous systems still required some manual operation or had limitations like short range.
- The proposed robot design aims to address issues with previous systems by allowing fully
IRJET-Survey Paper on Agro-Bot Autonomous RobotIRJET Journal
This document summarizes an agricultural robot that can perform multiple tasks. The robot can conduct crop prediction, weather detection, grass cutting, and 360-degree spraying. It is controlled via an Android application and uses sensors to gather soil moisture, temperature, and other environmental data. The robot aims to increase farming efficiency and productivity through automation.
15 Disadvantages of Automated Farming: Balancing Efficiency with Environment ...CIOWomenMagazine
Here are some key disadvantages of automated farming: 1. Loss of Traditional Farming Practices, 2. Dependency on Technology, 3. High Initial Investment, 4. Environmental Concerns, etc.
An agricultural robot is a robot deployed for agricultural purposes. Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring, and soil analysis.
Agricultural robots automate slow, repetitive, and dull tasks for farmers, allowing them to focus more on improving overall production yields. Some of the most common robots in agriculture are used for: Harvesting and picking
This document discusses machine learning and robotics applications in agriculture. It provides examples of machine learning like self-driving cars and product recommendations. It also discusses advantages like identifying trends/patterns with no human intervention. Agricultural robots discussed include crop harvesting robots, weeding robots, and aerial drones. Challenges of machine learning in agriculture include damage from incorrect robot calibration. Applications discussed are species management, field condition management, crop management, disease and weed detection. Specific agricultural robots and their uses are also outlined.
Increase in the population brings lots of challanges the major being food production.
Smart farming technologies
Typical agriculture value chain
Future farms
Design and Development of a Multifunctional Agrobot “RaithaMitra” for Efficie...IRJET Journal
The document describes the design and development of an agricultural robot called RaithaMitra. It features a mobile robotic platform equipped with sensors, actuators, and instruments to perform various agricultural tasks with accuracy and efficiency. These tasks include weed cutting, pesticide spraying, seed placement, and soil moisture detection. The robot is controlled by an Arduino Uno microcontroller and can be operated through a Bluetooth-connected Android app. It uses DC motors for movement and actuators to control the weed cutter, pesticide sprayer, seed container, and other components. An integrated soil moisture sensor detects water levels and triggers irrigation when levels are low. The multifunctional robotic system aims to improve agricultural productivity and efficiency through precision farming techniques
An agricultural robot is a robot deployed for agricultural purposes. ... Emerging applications of robots or drones in agriculture include weed control, cloud seeding, planting seeds, harvesting, environmental monitoring and soil analysis.
Agricultural robots automate slow, repetitive, and dull tasks for farmers, allowing them to focus more on improving overall production yields. Some of the most common robots in agriculture are used for: Harvesting and picking.
Automation in agriculture is increasing to address issues like a growing population, labor shortages, and the need for more sustainable and efficient food production. Agricultural robots and autonomous machines are being developed for tasks like fruit picking, tractor operation, pruning, weeding, spraying, milking, and crop monitoring using drones. Automation provides benefits like increased productivity, uniformity of work, reduced labor needs and costs, but also has drawbacks such as high initial costs and a potential reduction in job opportunities. Future trends include using robots for precision pruning and indoor vertical farms for lettuce production.
Research and development in agricultural robotics: A perspective of digital f...Redmond R. Shamshiri
Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
The Future of Farming: Technology and Farm Contractor Partnershipswilliamshakes1
future of farming is bright, thanks to the integration of technology and the valuable contributions of farm contractor partnerships. By embracing these advancements, farmers can overcome traditional challenges and contribute to a more sustainable and food-secure world.
This document describes an IoT-based multipurpose agricultural robot called Agribot. The Agribot uses sensors to monitor soil moisture, temperature, humidity and other field conditions. It is controlled using an Android smartphone connected via ESP8266. The Agribot is designed to perform tasks like plowing, seed sowing, and watering crops with minimal human labor. It aims to increase agricultural productivity and efficiency using evolving IoT technology.
Machine Learning in Agriculture Module 1Prasenjit Dey
Discuss the opportunities of incorporation of machine learning in agriculture. Briefly discuss different machine learning strategies. Briefly discuss the ways of machine learning can be used
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
3. OUTLINES
Introduction
Needs of robotic agriculture
How Robots works in agriculture
Future farm
Advantages of Robots in agriculture
Disadvantage of Robots in agriculture
Challenges in Adopting Robotics
Applications of Agriculture’s Robots
Conclusion
Reference
4. Introduction
The word robot was derived from the Czech word robota.
A robot is a mechanical, artificial agent and is usually an
electromechanical system.
The robot is able to autonomously, according to the program, or
under the control of a man running, most dangerous,difficult
and laborious, and persevering and precise tasks.
5. Agricultural Robotics is the logical proliferation of
automation technology into biosystems such as agriculture,
forestry, green house, horticulture etc.
In agriculture, the opportunities for robot-enhanced
productivity are immense - and the robots are appearing on
farms in various guises and in increasing numbers.
7. As the global demand for food continues to rise,
innovative solutions are emerging to help optimize
operations in agriculture.
These versatile machines can handle a wide range of
tasks, from planting and irrigation to pest control
and soil analysis.
These versatile machines can handle a wide range of
tasks, from planting and irrigation to pest control
and soil analysis.
8. How Robots works in agriculture
Autonomous Tractors and Equipment.
Harvesting Robots
Weeding Robots
Drones and UAVs (Unmanned Aerial
Vehicles)
Precision Agriculture
14. Disadvantages of Robots in Agriculture
High Initial Costs
Technical Complexity
Dependency on Technology
Limited Adaptability to Diverse
Environments
Job Displacement
15. Energy Requirements
Complexity of Integration
Limited Affordability for Small Farmers
Ethical Considerations
Vulnerability to Cybersecurity Threats
16. Challenges in Adopting Robotics
High Initial Costs
Technical Complexity
Job Displacement
Limited Adaptability
Energy Requirements
17. Applications of Agricultural Robots
Precision Farming
Crop Monitoring and Management
Harvesting and Sorting
Livestock Management
Pest Control
Data-Driven Decision Making
18. Conclusion
Many regions face challenges in securing an
adequate labor force for agricultural tasks.
Many regions face challenges in securing an
adequate labor force for agricultural tasks.
Robotics enables precision agriculture through the
use of advanced sensors and technology.
Robots reduce the reliance on manual labor, leading
to decreased labor costs, and can optimize resource
use, reducing expenses on inputs.
19. The increased efficiency and precision provided by
robots in tasks like planting, harvesting, and
resource management contribute to higher
productivity and reduced operational costs.
Precision agriculture, enabled by advanced sensors
and robotics, allows for targeted application of
resources, minimizing waste and environmental
impact. This not only benefits individual farmers
but also plays a crucial role in addressing global
challenges such as food security.