AI IN AGRICULTURE(AZRA).pptx............

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1. LINGARAJ APPA ENGINEERING COLLEGE SEMINAR TOPIC :- AI IN AGRICULTURE STUDENT NAME :- AZRA FATIMA USN :- 3LA20CS006 UNDER THE GUIDANCE OF PROF VEERESH BIRADAR

2. TABLE OF CONTENT :- INTRODUCTION PRECISION FARMING CROP MONITORING AND OPTIMIZATION LIVESTOCK MANAGEMENT AUTONOMOUS FARMING MACHINERY PRIDICTIVE ANALYSIS FOR YEILD FORECASTING DISEASE AND PEST DETECTION GREENHOUSE AUTOMATIONS APPLICATIONS OF AI IN AGRICULTURE ADVANTAGES OF AI IN AGRICULTURE DISADVANTAGES OF AI IN AGRICULTURE FUTURE OF AI IN AGRICULTURE CONCLUSION

3. Introduction to AI in Agriculture Artificial Intelligence (AI) is revolutionizing the agricultural industry, offering innovative solutions to optimize crop production, livestock management, and resource utilization. Technology has played a significant role in improving the agricultural productivity

4. Precision Farming Site-Specific Management devices like sensors and drones uses AI to collect information about things like soil weather and crops. This helps farmers to know exactly what there crop need. Optimized Inputs AI analyses data to prescribe the precise amounts of water, fertilizer, and pesticides needed, reducing waste and environmental impact. Yield Prediction Machine learning models forecast crop yields based on environmental factors, allowing farmers to plan ahead more effectively.

5. Crop Monitoring and Optimization It involves the use of AI power sensors like drones and satellite to monitor crop health, soil quality and moisture level. This will provides the real-time data for crop growth and help them to identify any issues before they become major problem By monitoring crop health framers can take proactive steps to prevent crop damage and ensure a healthy harvest

6. Livestock Management AI-powered livestock management enables monitoring and optimization of animal health, feeding, and reproduction. Sensors track vital signs, behavior, and environmental conditions to identify issues early and give them personalize care. Automated feeding systems and robotic milking parlors boost efficiency, while predictive analytics forecast market trends and optimize livestock production .

7. Autonomous Farming Machinery AI-powered autonomous tractors, harvesters, and other farming equipment are revolutionizing agriculture. These machines can navigate fields, detect weeds and pests, and optimize resource usage without human intervention. Sensors, computer vision, and machine learning enable precise control of planting, irrigation, and harvesting to boost yields and efficiency. Autonomous systems also reduce labor costs and improve worker safety.

8. Predictive Analytics for Yield Forecasting 1 Help in future scopes This technology can help farmers for future and make inform decision on when to plant and harvest crops 2 Optimizing Resource Allocation Predictive yield models enable farmers to allocate resources like water, fertilizers, and labor more efficiently, boosting productivity and profitability. 3 Early Issue Identification AI can detect emerging threats to crop health early, allowing farmers to intervene proactively and mitigate potential losses. 4 Adaptive Decision-Making With real-time data and AI-generated insights, farmers can make agile, informed decisions to adapt to changing conditions and optimize yields.

9. Disease and Pest Detection Early Diagnosis AI-powered disease and pest detection tools can identify issues early, allowing farmers to address problems before widespread damage occurs. Precision Monitoring closely keeping an eye on things like soil, weather and crops by using AI power camera and sensors to detect weeds and pest in crops Targeted Treatment Insights from AI detection enable precision application of pesticides and other treatments, reducing waste and environmental impact . Proactive Planning Historical data and predictive models help farmers anticipate and prepare for potential disease or pest outbreaks.

10. Greenhouse Automation 1 Climate Control AI-powered sensors monitor temperature, humidity, and light levels, automatically adjusting environmental settings to optimize growing conditions. 2 Irrigation Management AI algorithms analyze soil moisture and plant needs, precisely controlling water delivery to maximize efficiency and eliminate waste. 3 Pest and Disease Detection Computer vision systems scan plants, identifying early signs of pests or diseases, triggering targeted treatment before problems escalate.

11. Applications of AI in Agriculture Crop and soil monitoring using drones Automatic weeding and harvesting. Insect plant and disease detection. Soil health monitoring and analysis. Autonomous farming machinery for planting , irrigating and harvesting Livestock monitoring and management

12. Advantages of AI in Agriculture Increased Yields AI-powered precision farming techniques optimize resource allocation and automate tasks, leading to higher crop yields and improved overall productivity. Reduced Costs AI can help farmers make more informed decisions, reduce waste, and streamline operations, leading to significant cost savings. Enhanced Sustainability AI-driven precision agriculture techniques minimize the use of water, fertilizers, and pesticides, promoting sustainable farming practices.

13. Disadvantages of AI in Agriculture 1 Initial Investment Implementing AI-based solutions can require significant upfront investment, which may be a barrier for smaller farms. 2 Technological Dependence Farmers may become overly reliant on AI, leading to a loss of traditional farming knowledge and skills. 3 Required specialized training The use of Complexity of AI system may require specialized training and technical experties that posesing barrier for some farmers. 4 Job Displacement Automation and AI-powered solutions could potentially lead to the displacement of human farm workers.

14. The Future of AI in Agriculture As AI continues to evolve, the future of agriculture promises unprecedented efficiency and innovation. Smart tools will help farmers grow crops more frequently. Robots will do more farm work making it faster. Machine will find and deal with pests and diseases early. Farmers will get personalized advice from computer. New plant verities will be created faster with AI.

15. Conclusion These technologies have the potiential to increase productivity and reduce cost and optimize crop production. AI technologies transforming the agricultural industry into sustainable and efficient agriculture.

16. REFRENCES "Artificial Intelligence Techniques for Agriculture and Environmental Sustainability" by Pandian Vasant and N. Sundararajan (2019):[Link]( https://www.springer.com/gp/book/9783030157047 ) Applications of Artificial Intelligence Techniques in Agriculture: A Review" by A. G. Radhakrishnan et al. (2017):[Link]( https://www.sciencedirect.com/science/article/pii/S1319562X16300879 ) "Deep Learning Techniques for Agriculture Applications: A Review" by Qamar Zia et al. (2019):[Link]( https://www.mdpi.com/2073-431X/9/6/290 ) 4. "A Review of Machine Learning and Robotics Applications in Agriculture" by Shakeel Sarwar et al. (2020):[Link]( https://www.mdpi.com/2504-446X/4/1/15 ) 5. "AI in Agriculture: Present Status and Future Trends" by Rajendra A. Patil and Gajanan R. Patil (2021):[Link]( https://www.mdpi.com/2504-446X/5/2/51 )

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