Artificial intelligence projectsppt.pptx

Artificial intelligence

1. AI DRIVEN AGRICULTURAL CHATBOT FOR SMALL SCALE FARMERS GUIDED BY PRESENTED BY K.MADHUBALA(AP/CSE ) K.ANBUMAHESHWARI(20CSE002) T.MUNEESWARI(20CSE014 )

2. OBJECTIVES The objective of this project voice chat capabilities and integrates crop disease detection, and autonomous operations functionalities revolutionize crop management. This system aims to provide farmers with a user-friendly platform for engaging in agricultural discussions, enabling seamless exchange of information, analysis of crop-related queries, and dissemination of expert advice. By leveraging AI capabilities, the system aims to enhance decision-making processes, optimize farming practices, and ultimately contribute to increased agricultural productivity and sustainability. It enables farmers to engage in discussions with AI-driven assistance to exchange knowledge, share experiences, and learn from each other's successes and challenges.

3. ABSTRACT Artificial Intelligence (AI) is the development of an AI-driven agricultural Chat Bot tailored to address challenges faced by small-scale farmers. Collaborative industry support, encompassing technology firms, agribusinesses, government agencies, and financial institutions, will play a pivotal role in ensuring the success, accessibility, and sustainability of this transformative solution. The project aligns with a broader vision of empowering small-scale farmers through innovative AI technologies for enhanced agricultural productivity and livelihoods.

4. DOMAIN INTRODUCTION AI refers to a family of technologies that allow computers and other machines to perform tasks previously thought to rely on human experience, creativity and ingenuity. The study of computer systems that attempt to model and apply the intelligence of the human mind.AI refers to the creation of intelligent machines that can mimic human capabilities such as learning, reasoning, and problem-solving. AI is in the development of computer functions associated with human intelligence. The field of AI is rapidly evolving, with breakthroughs and innovations reshaping industries and influencing various aspects of our daily lives. Artificial Intelligence:

5. PROBLEM STATEMENT: How can we create an advanced agricultural webpage to address challenges faced by small-scale farmers? This involves enhancing crop disease detection, autonomous operations and crop management with the aim of doubling agricultural output .

6. LITERATURE SURVEY

7. Title Year Author Methodology Advantages Disadvantages Application of Artificial Intelligence in Agriculture 2020 Mohanty , S. P., Hughes, D. P., & Salathé , M. This paper provides a comprehensive review of various AI techniques applied in agriculture, including machine learning, computer vision, and expert systems. It discusses applications such as crop monitoring, disease detection, yield prediction, and irrigation management. Increased Efficiency: AI technologies can automate various agricultural tasks, monitoring crop health, managing irrigation systems, and controlling pests. This automation leads to increased efficiency in farming operations, saving time and labour for farmers. Text based user interface

8. Title Year Author Methodology Advantages Disadvantages Artificial Intelligence in Agriculture 2020 Koirala , A., Puri , S., & Bhandari,D . Chat bot are conversational virtual assistance automate interactions with end users. AI powered chat bots, using machine learning techniques, understand natural language and interact with users in a personalized way. Advantages the query is based on prediction, then the future predictions on the requested agricultural products will be represented in the graphical format and displayed to the user. Automatic talk bot will be created. It is only provide a text-based user interface.

9. Title Year Author Methodology Advantages Disadvantages Agriculture Helper Chat Bot Using Deep Learning 2023 Ms. M. Anitha *1, Mr.CH. Satyanarayana Reddy*2, Ms.CH.Deepika *3 A chat bot uses conversational interaction to automate interactions to farmers . AI powered chat bots can understand natural language and respond to users in a personalised manner thanks to ML techniques. Remote Monitoring and Management Initial Investment Cost is very high.

10. SUMMARY OF LITERATURE SURVEY In Prediction Algorithm use the Chat bot are conversational virtual assistants which automate interactions with end users. Artificial intelligence powered chat bots, using machine learning techniques, understand natural language and interact with users in a personalized way. In Prediction Algorithm used for only the Chat bots typically provide a text-based user interface. Implementing and managing IOT and AI systems requires specialized knowledge and technical expertise. Farmers may need training and support to effectively utilize these technologies. The surveyed papers discuss a wide range of applications including crop monitoring, disease detection , precision farming, and decision support systems.

11. SOFTWARE AND HARDWARE REQUIREMENTS: Operating System : Windows 7 Technology Used : Python, Machine Learning, Artificial Intelligence IDE : PyCharm Database : SQLite3   SOFTWARE REQUIREMENTS:

12. HARDWARE REQUIREMENTS: Processor : Intel(R) Core(TM) i3 Processor Speed : 3.06 GHz RAM : 2 GB Hard Disk Drive : 250 GB CD-ROM Drive : Sony Monitor : “17” inches Keyboard : TVS Gold Mouse : Logitech

13. SYSTEM ARCHITECTURE

14. PROPOSED METHODOLOGY It develops models for voice recognition to enable spoken interactions and for image analysis to interpret visual agricultural cues. It implements algorithms for data processing, feature extraction, and recommendation generation based on user queries and context. It Continuously updates AI models with new data and techniques to enhance accuracy, relevance, and responsiveness. It explores various AI techniques such as machine learning, deep learning, natural language processing, and computer vision. The proposed system NLP Algorithm are use to implement the project. Natural Language Processing based on dialogue management and interactions to farmers.   The proposed system to develop a comprehensive AI-powered discussion system tailored specifically for agriculture, integrating voice and image recognition technologies.

15. Machine learning algorithms allow machines to learn about particular agricultural lands, the geographical structure of farming areas, and plants and crops using supervised and unsupervised learning methods. Using Deep Learning technique to provide an intelligent and personalized solution to the challenges faced by farmers in the agriculture domain. The chat bot aims to enhance decision-making, improve productivity, and mitigate risks through accurate and timely information delivery. First-generation SMS based and next generation social media tools like WhatsApp, farmers in India are digitally more connected to the agricultural information services. Further we develop the project voice based and image based query accept the webpage after find the solution then chat bot provide text based solution to farmers .

16. MODULES User Registration Admin Query Pre-processing Crop Information

17. Column Type uname text addr text city text mobile text email text pwd text U SER R EGISTRATION The process by which a user creates an account on a platform by providing relevant information. The user registration page allows organizers to collect all the required details of their attendees. They can have information like a name, address, city, mobile number, email address, password, etc. MODULES DESCRIPTION

18. Farmer sends text based query or voice based query to the chat bot. Farmer can also send images which is the crop’s disease image to the chat bot. Farmer receives generated solutions from chat bot according to farmer’s query. USER WORKING PROCESS: Farmer Chat Bot Send Query (Text, Voice, Image) Response Query(Text)

19. ADMIN: Admin can pre-processed all the data bases of solutions and the assumed queries previously. Admin can view all the functions and review famer’s query. Chat Bot Farmer Control Control Admin

20. Tokenization Normalization Part-Of- Speech Tagging Semantic Analysis Intent Detection Query Pre-Processing: Tokenization is the process of splitting the input sentences into a list of words. Input query will be converted into tokens. Normalization is the process of organizing data in a database. It includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency. Normalization is the process of converting the multiple representations of input to their single representation.

21. Part of speech tagging (POS tagging) is a process in natural language processing (NLP) that involves assigning a part of speech (such as noun, verb, adjective, adverb, etc.) to each word in a given text based on its grammatical role and context within a sentence. The goal of POS tagging is to automatically analyse and label the words in a sentence with their respective parts of speech, which helps in various NLP tasks such as syntactic analysis, information extraction, and machine translation. Semantic Analysis, also known as semantic parsing or semantic understanding, is a crucial component of natural language processing (NLP) that focuses on understanding the meaning of text and voice based query beyond its surface structure. Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations.

22. Collect common pests and diseases associated with each crop, along with prevention and treatment measures. Information on crop rotation, intercropping, and other sustainable farming practices. CROP INFORMATION:

23. SCREENSHOTS Home Page

24. User Registration

25. Farmer Login

26. User Page

27. Chat Bot

28. Upload Image

29. Crop Disease Identification and Management

30. Admin Login

31. View Users

32. CONCLUSION This project is used for farmers to communicate with system through text and voice chat and also image recognition. The accurate Disease detection and classification of the plant leaf image is very important for the successful cultivation of cropping and this can be done using image processing. This project discussed various techniques to segment the disease part of the plant. This project discussed classification techniques to extract the features of infected leaf and the classification of plant diseases throw SVM classifier.

33. FUTURE ENHANCEMENT In future, we like to implement image chat in mobile app. Before the problem of crop disease detection can be solved, the problems of identifying different species of plants need to be addressed. Fortunately, there has been much work already completed in this problem domain. Color features, such as the mean, standard deviation, skewness, and kurtosis are made on the pixel values of the various plant leaves can analyzed in future.

34. REFERENCES [1] "Deep Learning-Based Image Analysis for Plant Disease Detection" by Ferentinos , K. P. (2018) [2] "A Review on Applications of Deep Learning Techniques for Image Classification and Recognition" by Samanta , S., & Mehta, V. (2020) [3] "Machine Learning Techniques for Agriculture Crop Yield Prediction: A Review" by Araújo , M. C., & Coelho, A. L. (2019) [4] "Computer Vision and Deep Learning Techniques for Leaf Detection and Disease Detection: A Review" by Alshazly , H., & Alsoud , A. (2020) [5] "A Survey on Applications of Machine Learning Techniques in Precision Agriculture: A Review" by Kumar, S., & Singh, S. (2021)