Artificial Inteligence IN AGRICULTURE...
- 1. NAVSAHYADRI EDUCATION SOCIETY’S GROUP OF INSTITUTIONS FACULTY OF ENGINEERING Department of Artificial Intelligence & Machine Learning “AI IN AGRICULTURE” Presented By Sunny Anil Gore under the guidance of Prof.B.B.Deshmukh Associate Professor Academic Year (2024-25) Savitribai Phule Pune University
- 3. Introduction Introducing the application of Artificial Intelligence (AI) in agriculture marks a pivotal shift in the way we cultivate crops and manage farming operations. With AI's transformative capabilities, farmers can harness data-driven insights to enhance productivity, optimize resource allocation, and mitigate risks. From precision farming techniques to predictive analytics and robotics, AI empowers agricultural stakeholders to make smarter decisions, ultimately fostering a more sustainable and resilient food production system. This introduction sets the stage for exploring how AI is revolutionizing every aspect of agriculture, driving innovation, and shaping the future of farming worldwide.
- 4. Literature survey 1.Mahibha G, Balasubramanian P. Impact of Artificial Intelligence in Agriculture with Special Reference to Agriculture Information Research. Curr Agri Res 2023; 11(1). . doi : http://dx.doi.org/10.12944/CARJ.11.1.25 2. Artificial Intelligence in Agriculture To cite this article: Jiali Zha 2020 J. Phys.: Conf. Ser. 1693 012058. 3 E. Elbasi et al., "Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review," in IEEE Access, vol. 11, pp. 171-202, 2023, doi: 10.1109/ACCESS.2022.3232485. 4 A. Holzinger, I. Fister, I. Fister, H. -P. Kaul and S. Asseng, "Human-Centered AI in Smart Farming: Toward Agriculture 5.0," in IEEE Access, vol. 12, pp. 62199-62214, 2024, doi: 10.1109/ACCESS.2024.3395532. 5.J Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review 6. A G. Boulanger, ―The expert system PLANT/CD: A case study in applying the general purpose inference system ADVISE to predicting https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9999442
- 5. TYPES OF CYBER SECURITY
- 6. Need Of AI In Agriculture 1. Productivity Enhancement: AI can help farmers increase productivity by optimizing resource allocation, improving crop management practices, and enhancing decision-making based on data- driven insights. 2. Resource Efficiency: With AI, farmers can minimize waste and optimize the use of resources such as water, fertilizers, and pesticides, thereby promoting sustainable agriculture and reducing environmental impact. 3. Risk Mitigation: AI-powered predictive analytics enable farmers to anticipate and mitigate risks such as adverse weather conditions, pest outbreaks, and market fluctuations, minimizing crop losses and ensuring profitability. 4. Labor Optimization: AI-driven automation and robotics reduce the need for manual labor, addressing labor shortages and increasing operational efficiency on farms.
- 7. AI Used In Agriculture 1.Crop Monitoring:-. Crop monitoring with AI involves using advanced technology like drones and satellites to analyze crop health and detect issues like pests or nutrient deficiencies, helping farmers make informed decisions to maximize yield and quality 2.Predictive Analytics:-Predictive analysis in agriculture uses AI to forecast future outcomes based on historical data, weather patterns, soil conditions, and market trends. It helps farmers make informed decisions about planting, harvesting, and managing their crops to optimize yields and minimize risks. 3.Precision Farming:-Precision farming, powered by AI, employs technology like GPS, sensors, and data analytics to customize agricultural practices based on specific field conditions. It enables farmers to apply resources such as water, fertilizers, and pesticides more efficiently, resulting in higher yields, reduced waste, and minimized environmental impact.
- 8. Benefits Of AI In Agriculture Precision Agriculture Crop Monitoring Soil Management Weather Forecasting Irrigation Management Food Safety Pest and disease management
- 9. Limitation of AI In Agriculture High Cost Dependency Accessibility Job Displacement Environmental Impact
- 10. Motivation, Purpose and Scope and Objective of Seminar AI in agriculture aims to transform farming practices by optimizing resource use, enhancing sustainability, ensuring food security, and driving economic growth. By leveraging advanced technologies like machine learning and robotics, AI enables precision farming, improves crop yields, and minimizes environmental impact. Additionally, it fosters rural development by creating new opportunities and enhances resilience to climate change through predictive analytics and adaptive management strategies..
- 11. Working In the context of AI in agriculture, "working" refers to the functionality and effectiveness of AI technologies in improving farming practices and outcomes. This includes the successful implementation of AI algorithms, systems, and tools to perform tasks such as data analysis, decision-making support, automation of farming operations, and predictive modeling. For example, AI systems "work" by collecting data from various sources such as sensors, satellites, and drones, processing this data using machine learning algorithms to extract valuable insights, and providing actionable recommendations to farmers. Additionally, AI-driven automation and robotics "work" by autonomously performing tasks like planting, irrigation, and harvesting, thereby increasing efficiency and productivity on the farm.
- 12. Discussions and Conclusions AI in Agriculture: A Brighter FutureAI can transform agriculture by: 1. Optimizing resources: Using data to make the most of water, land, and energy. 2. Boosting productivity: Automating tasks and predicting yields to increase efficiency. 3. Enhancing sustainability: Reducing waste, promoting eco-friendly practices, and ensuring food safety. By embracing AI in agriculture, we can create a more resilient, efficient, and equitable food system for everyone.
- 13. Future enhancement Future AI enhancements in agriculture will focus on integrating technologies like IoT, blockchain, and edge computing. This will create more efficient farming systems by: 1. Processing real-time data from IoT sensors for instant insights. 2. Using blockchain for traceability and transparency in the supply chain. 3. Running AI algorithms on farm equipment (edge computing) for improved efficiency. These advancements will help farmers optimize productivity, sustainability, and resilience in the face of challenges.