CATTLE DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE

PPT on LSD Prediction

1. TITLE:“ CATTLE DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE ”

2. TABLE OF CONTENTS INTRODUCTION AIM & OBJECTIVES JUSTIFICATION OF PROJECT PROBLEM STATEMENT LITERATURE SURVEY SYSTEM ARCHITECTURE DATA FLOW DIAGRAM SYSTEM REQUIREMENTS PROPOSED SYSTEM EXPECTED OUTCOME IMPLIMENTATION METHODOLOGY APPLICATIONS ADVANTAGES

3. In Cattle Disease P rediction, a large number of multi-source cattle electronic medical record data are collected It uses data analysis for finding the correlation between symptoms, disease and treatment T he Apriori algorithm is used to correlate the specific disease name and the treatment. INTRODUCTION

4. AIM AND OBJECTIVES The Aim of our project is to build a real time application useful for medical practitioners to handle the cattle disease in a better way. To provide medical sector application. To help the veterinary doctors in decision making. To provide useful patterns based on old data and finds the relationship between cattle diseases, symptoms and treatments. To provide a new online community, where we collect huge variety of medical information useful for medical practitioners.

5. Identification of symptoms, cattle diseases, and providing proper treatments is a complex task in the current medical sector. Manual process of identifying the cattle disease and treatment is too complex and time-consuming and also expensive. This systems collect the data, stores in database and retrieves the same in future to handle the cattle disease in a better way. JUSTIFICATION OF THE PROJECT

6. PROBLEM STATEMENT Performing summarization and come out with useful conclusions for medical fraternity as well as patient community is a important factor in medical sector. Identifying cattle disease types is a difficult process in the current medical sector. Present system collects the data, stores in database and retrieves the same in future, but no extraction of useful information which helps the medical practitioners to handle the cattle disease in a better way.

7. The Existing method requires veterinarians to maintain manual data collection, which is a very challenging and perplexing duty for the cattle department. It is also challenging to establish a link between the symptoms of cattle diseases and the appropriate therapies. EXISTING SYSTEM

8. PROPOSED SYSTEM To find a correlation between the cattle disease, symptoms, and treatment Disease recommendation and treatment recommendation based on symptoms using the eclat algorithm The eclat algorithm is used to find the patterns. This system is planned to build as real time application which is useful for doctors to handle cattle disease. In order to determine the association between symptoms, diseases, treatments, and pattern prediction, we will gather a number of frequently occurring data sets and store them in a database for the pattern prediction.

9. NO TITLE AUTHOR METHODOLOGY REFERENCES YEAR LIMITATIONS 1 Cattle Disease Auxiliary Diagnosis and Treatment System Based on Data Analysis and Mining Lijing Niu, Chenhao Yang, Yongxing Du, Ling Qin, Baoshan Li The focus is to classify the disease into disease categories according to the space vector model (SVM) algorithm IEEE (International Conference on Computer and Communication System 2021 The disadvantage of this method is to select as many symptoms as possible when using the system, only in this way can the disease diagnosis be more accurately realized. 2 Developing Mobile Intelligent System For Cattle Disease Diagnosis and First Aid Action Suggestion . Wiwik Anggraeni, A.Muklason, A.F.Ashari,Wah yu A, Darminto The core intelligent engine is implemented using Fuzzy Neural Network, while the real application is developed under Android operating system IEEE International Conference on Complex, Intelligent, and Software Intensive Systems 2021 Only suitable for first aid action, not suitable for complex disease. Android app is developed, visualization problem LITERATURE SURVEY

10. 3 Cattle health monitoring system using Arduino and LabVIEW for early detection of diseases. Kunja Bihari Swain, Satyasopan Mahato, Meerina patro, sudeepta kumar pattnayak Arduino UNO, Arduino NANO, Xbee module and different types of sensors for taking the cattle body parameters have been used. IEEE International Conference on Sensing, Signal Processing and Security (ICSSS) 2020 Only used to monitor the cattle health condition. Doesn't predicts the relationship between symptoms, disease and treatments 4 Identification of Super Spreaders of Foot-and Mouth Disease in the cattle transportation network. Francisco Gomez, Jeisson Prieto, Juan Galvis, Fausto Moreno, Jimmy Vargas cattle transportation network, The characterization of this complex transportation network may aid in surveillance and control tasks. IEEE (outbreak case in Cesar) 2020 Only used to monitor the foot and mouth disease. Less accurate results generated 5 Early Detection of Leptospirosis in Cattle Urine Samples by Using Loop Mediated Isothermal Amplification (LAMP) Method. Widiasih, Dyah Ayu, Susetya, Heru, Widayanti, Rini The LAMP technique is one of the methods for the diagnosis of leptospirosis by using DNA amplification IEEE International Conference on Bioinformation, Biotechnology, and Biomedical Engineering 2019 This method need live culture of Leptospira sp. It became more complicated to get the live Leptospira serovars and cost effective

11. 6 Identification of Acidosis Disease in Cattle Using IoT. Fatih, Kamil Aykutalp It is aimed to help diagnose acidosis disease which is one of the digestive disorders that will occur in rumen parts of cattle as a result of relation International Conference on Computer Science and Engineering 2019 Only used to monitor one disease Acidosis Disease, Sensors used for monitoring, leads to less accurate results. 7 Near-Field Wireless Magnetic Link for an Ingestible Cattle Health Monitoring Pill. Seth Hoskins , Timothy Sobering, Daniel Andresen , and Steve Warren Ingestible pill technology offers promise for obtaining physiologic data from cattle IEEE Annual International Conference 2019 Using this technique may cause cattle near the heart and lungs, and it is an environment sheltered from weather, other animals, and external hazards to the physical integrity of wearable sensors. 8 Ambulatory Instrumentation Suitable for Long-Term Monitoring of Cattle Health. S. A. Schoenig , T. S. Hildreth , L. Nagl1 , H. Erickson , M. Spire , D. Andresen , and S. Warren Long-term health monitoring of free-range cattle imposes design constraints that are very different from those required for ambulatory human monitoring systems Annual International Conference of the IEEE 2019 the transition from current bulky apparatuses to wireless systems that incorporate both implantable sensors and low-profile, external devices appropriate for cow-bell and collar form factors.

12. 9 A Fuzzy Expert System to Diagnose Diseases with Neurological Signs in Domestic Animal Mahdi Jampour , Mohsen Jampour, Maryam Ashourzadeh, Mahdi Yaghoobi Fuzzy logic identify and analyze diseases to treat neurological symptoms and according to the talent levels of each of disease International Conference on Information Technology 2011 Only used in detection of determining diseases of animal with the neurological involvement. 10 Implementation of Smart Infrastructure and Non-Invasive Wearable for Real Time Tracking and Early Identification of Diseases in Cattle Farming using IoT Mr. V Gokul, Mr. Sitaram Tadepalli Ai is to notify and handle illnesses at earlier stage, abnormalities, emergency conditions, calving time and diseases using Internet of Things. International conference on I-SMAC 2017 High cost and farmers may get confused to use. 1 1 Learning to Detect the Onset of Disease in Cattle from Feedlot Watering Behavior. Scott Dick, Carlos Campos Bracho determine a time-series representation that is compatible with the usual tabular format for machine learning datasets. IEEE 2017 major problem encountered in this project is a sharp skewness in the datasets. The models are not yet up to the standards of the medical community for diagnostic tests.

13. 1 2 Ticks and tick-borne diseases in Africa: a disease transmission model HENRY G. MWAMBI measures ( i ) reducing κ (ii) reducing a, Increasing d increasing tick mortality Of course, reduction of hosts is also a feasible control measure. IMA Journal of Mathematics Applied in Medicine and Biology. 2013 The remain valuable in a research context and as a tool for a qualitative understanding of the underlying processes. 1 3 Common diseases and disorders of cattle at Lalmohan upazila, Bhola J. M. Nahid Nahian, Sharif Md. Ismail Hossain, Md. Shamsul Arifin, Md. Zakaria Islam, Md. Selim Ahmed Obtained data were analyzed by using statistical software 'STATA/IC-11.0’. International Journal of Natural and Social Sciences. 2017 To recapitulate, accurate planning and program should be taken on hand for prevention and control of common diseases of cattle population in coastal study area. 1 4 LUMPY SKIN DISEASE Eeva Tuppurainen, Tsviatko Alexandrov, Daniel Beltrán-Alcrudo Descriptive epidemiological characteristics were derived from the data and GIS software was applied to map their spatial distribution. Vaccination were used. FAO animal production and health. 2017 Only used for lumpy skin disease and time taken was more to detect the disease and treatment.

14. 1 5 Cattle disease identification using Prediction Techniques Noone Vijay Kishan , Sai Trinath Y, Sandeep Kavalur , Sangamesha V, Mr. Sumanth Reddy It uses three algorithms for data mining, i.e. Classifier of decision tree, Random forest classifier and classifier of Naïve Bayes Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India 2021 The sensor studies by using decision tree and random forest are not accurate. 1 6 Application of Artificial Intelligence Algorithm in Image Processing for Cattle Disease Diagnosis Bezawit Lake, Fekade Getahun , Fitsum T. Teshome It uses Digital image processing to manipulate images and the method of palpation was used and sent to CNN model Scientific Reseach Publishing 2022 a lot of training data is needed for the CNN to be effective and they fail to encode the position and orientation of objects. 1 7 Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving Mohammad W. Sahar, Annabelle Beaver, Marina A. G. von Keyserlingk and Daniel M. Weary It uses the prepartum behavior which is used to predict cows at risk of metritis, HYK, and mastitis after calving Animal Welfare Program, Faculty of Land and Food Systems, University of British 2020 Some cattles may not show prepartum behavior

15. 1 8 Integrating diverse data sources to predict disease risk in dairy cattle Jana Lasser , Caspar Matzhold Christa Egger-Danner, Birgit Fuerst-Waltl , Franz Steininger,Thomas Wittekand Peter Klimek Machine learning approaches such as black-box sensor systems in which prediction algorithms are used that are of commercial interest Section for Science of Complex Systems, Center for Medical Statistics, Informatics, Intelligent Systems 2021 As the black box mainly consists of electronic circuit, there are chances of damage results in providing wrong data. 1 9 Cattle medical diagnosis and prediction using machine learning Harsh J. Shah, Chirag Sharma , Chirag Joshi The system is built upon the “TensorFlow” machine learning library as well as the “ Keras ” deep neural network library in Python. International Research Journal of Engineering and Technology (IRJET) 2022 Symptoms difficult to observe and Low Infection Detection Rate 2 Livestock Disease Prediction System Daksh Ashar , Amit Kanojia , Rahul Parihar, Prof. Saniket Kudoo SVM algorithm used to prepare model & The multiclass classification algorithm is used to predict the disease VIVA-Tech International Journal for Research and Innovation ISSN 2021 The human errors may cause a big damage and the time taken to predict the diseases is

16. 2 1 Algorithms for Detecting Cattle Diseases at Early Stages and for Making Diagnoses and Related Recommendations Dmitry Yu. Pavkin , Alexei S. Dorokhov , Fedor E. Vladimirov , Igor M. Dovlatov and Konstantin S. Lyalin Algorithms are used in the software called “Systems of internal monitoring of cattle’s physiological state” Applied Science, MPDI 2021 They do not allow detecting diseases in their early stages because the algorithms used are outdated 22 Prediction of postpartum diseases of dairy cattle using machine learning A.M. Hidalgo, F. Zouari, H. Knijn & S. van der Beek CRV, Wassenaarweg , NW, Arnhem Used the random forest algorithm ( Breiman , 2001) which is an ensemble learning method that uses weak classifiers to build a strong one. Proceedings of the World Congress on Genetics Applied to Livestock Production 2015 evaluating the use of machine learning to predict several postpartum diseases however are lacking 2 3 Cow Face Recognition for a Small Sample Based on Siamese DB Capsule Network FENG XU 1,2, JING GAO 1,2, AND XIN PA Capsule Network with Dense Block module is proposed. Bivariate characteristics are obtained through Siamese Neural Network. IEEE 2022 Due to the lack of cooperation from cows, it is difficult to collect large amounts of data and verify the identity of individuals

17. 2 4 Survey on Dairy Livestock Disease Prediction System Prof. A.V. Brahmane , Purva Dekate , Gayatri Dike, Sneha Musmade , Rutuja Shinde The model deployed takes the symptoms of the Livestock as input and does the analysis using Machine Learning algorithms (Decision tree classifier) to predict the precise disease. International Journal of Scientific Research & Engineering Trends (IJSRET) 2022 often unaware of whether the disease is mild or might prove fatal and precautions to be taken at appropriate time. 25 Research perspectives on animal health in the era of  artifcial intelligence Pauline Ezanno , Sebastien Picault , Gael Beaunee , Xavier Bailly , Facundo Muñoz, Raphael Duboz , Herve Monod and Jean‑Francois Guegan Use of AI methods (e.g., machine learning, expert systems, analytical technologies) converges today with the collecting of massive and complex data, and allows these fields to develop rapidly Ezanno et al. Veterinary Research 2021 The development of AI skills within the AH community remains limited in relation to the needs and constraints of AI approaches

18. SYSTEM REQUIREMENTS SOFTWARE REQUIREMENTS Windows XP version & Higher Visual Studio 2010 C# language SQL Server HARDWARE REQUIREMENTS Pentium IV onwards processor 1.2GHz processor speed 2GB + RAM 40GB + Hard disk space

19. SYSTEM ARCHITECTURE

20. DATAFLOW DIAGRAM 1. DOCTOR

21. 2. ADMIN

22. 3. VISITORS

23. METHODOLOGY

24. EXPECTED OUTCOME In this proposed system we are predicting the correlation between symptoms-diseases-treatments. As in current system it is difficult to identify the cattle disease and also its difficult to give the proper treatments.  

25. IMPLEMENTATION

26. TC# Description Expected Result Actual Result   Status of Execution Pass/Fail TC01  Execute/run the application Application should run without any interrupts  Application is executing properly Pass TC02 Verification of Login Page Enter User Name and Password. It should verify with database. Entered User Name and Password are successfully verifying with database. Pass TC03 Verification of Admin Page input User Name and password If Admin Login Name & Password is valid then it should navigate to respective Admin home page. If invalid then show message that Input Username & Password is wrong. Admin User Name & Password is valid then successfully navigating respective home page. If User Name & Password is not valid or wrong input then message box shown that User Name & Password wrong.  Pass               TEST CASES

27.   TC04 Verification of member Login member inputs id and password and clicks on submit button, if login is successful redirect user to member home page or else display message login failed User will input userid and password and clicks on login button and user redirected to member home page Pass   TC05 Admin view VDoctors/members Admin clicks on member button, all members should be displayed Admin clicks on members . all members displayed successfully Pass   TC06 View datasets When Member clicks on datasets link , all records should be displayed using dynamic table Member clicks on datasets link, all datasets fetched from excel-sheet and displayed in GUI Pass TC07 Admin Update Password Admin enters old password, New password and confirm password and clicks on submit button, new password should be updated Admin inputted old password, new password and confirm password and clicks on submit button, id old password is correct , new password will be updated Pass

28. TC08 Prediction Module Member clicks on prediction. Algorithm gets executed depending on testing datasets size and predicts the price. VDoctor clicks on predict button. Algorithm gets executed and outputs displayed on GUI Pass TC09 Results Analysis Member click on results link. Accuracy and efficiency should be displayed on dynamic table Member clicks on result analysis, results displayed on GUI Pass

29. Results and Snapshots Home Page

30. Admin Login Page

31. Admin Home Page

32. Admin can add and delete cities

33. Admin can add veterinary doctor details

34. Admin manages the datasets

35. Admin can reset password

36. Doctor Login Page

37. Doctor page

38. Pattern prediction page

39. Cattle disease Prediction

40. Treatment Details for the predicted disease

41. Doctor can update passwords

42. Contact Us page

43. ADVANTAGES It will help veterinary doctors to find out side-effects of different drugs so they can prescribe better drugs to other cases with similar cattle disease. Faster Decision making. Reduces the rate of cattle disease based on the results of our system.

44. APPLICATION The concept can be implemented for a clinic or hospital for analyzing the relationship b/w symptoms-cattle diseases-drugs. The concept can be implemented for a drug manufacturing company, where they can know the list of popular drugs for cattle disease. The concept can be implemented as an online health community system where users or farmers can gather information based on the drug.

45. RESULT System finds correlation between cattle disease symptoms - disease types - treatment using data science algorithms. We use “Eclat algorithm” for pattern prediction as it is one of the efficient and powerful algorithm used to find patterns. This algorithm takes very less time to process datasets and to find the patterns.

46. In real time it is difficult to handle the cattle disease symptoms and disease types as animals cant explain their problems or pain that they are facing. In medical sector finding the cattle disease symptoms, diseases is a challenging task. This system finds the cattle disease symptoms and then predicting the correlation between symptoms-diseases-treatments. As in current system it is difficult to identify the cattle disease and also its difficult to give the proper treatments. System useful for the medical sector and helps veterinary doctors to identify the cattle disease types and related symptoms and can treat in a better way. CONCLUSION

47. FUTURE ENHANCEMENT In the future, we can add more training datasets to get better results. We can also use more algorithms for pattern prediction and can compare and can find the better algorithm.

48. REFERENCES Lijing Niu, Chenhao Yang, Yongxing Du, Ling Qin, Baoshan Li “Cattle Disease Auxiliary Diagnosis and Treatment System Based on Data Analysis and Mining” : IEEE (International Conference on Computer and Communication System – 2021. Wiwik Anggraeni, A.Muklason, A.F.Ashari, Wahyu A, Darminto “Developing Mobile Intelligent System For Cattle Disease Diagnosis and First Aid Action Suggestion.” : IEEE International Conference on Complex, Intelligent, and Software Intensive Systems – 2020 Kunja Bihari Swain, Satyasopan Mahato, Meerina patro, sudeepta kumar pattnayak “Cattle health monitoring system using Arduino and LabVIEW for early detection of diseases” : IEEE International Conference on Sensing, Signal Processing and Security (ICSSS)-2020 Francisco Gomez, Jeisson Prieto, Juan Galvis, Fausto Moreno, Jimmy Vargas “Identification of Super-Spreaders of Foot-and-Mouth Disease in the cattle transportation network.” :IEEE(outbreak case in Cesar).-2020

49. REFERENCES 5. Widiasih, Dyah Ayu, Susetya, Heru, Widayanti, Rini “Early Detection of Leptospirosis in Cattle Urine Samples by Using Loop-Mediated Isothermal Amplification (LAMP) Method.” : IEEE International Conference on Bio-information, Biotechnology, and Biomedical Engineering-2019. 6. Fatih, Kamil Aykutalp “Identification of Acidosis Disease in Cattle Using IoT” : International Conference on Computer Science and Engineering – 2019 7. Seth Hoskins , Timothy Sobering, Daniel Andresen , and Steve Warren “Near-Field Wireless Magnetic Link for an Ingestible Cattle Health Monitoring Pill” :IEEE Annual International Conference – 2019 8. S. A. Schoenig , T. S. Hildreth , L. Nagl1 , H. Erickson , M. Spire , D. Andresen , and S. Warren “Ambulatory Instrumentation Suitable for Long-Term Monitoring of Cattle Health”: Annual International Conference of the IEEE -2019

50. THANK YOU