![REFERENCES
•
[1] Prof. D.S. Zingade ,Omkar Buchade ,Nilesh Mehta ,Shubham Ghodekar ,Chandan Mehta “Crop Prediction System
using Machine Learning”.
• [2] Ashwani kumar Kushwaha, Swetabhattachrya “crop yield prediction using agro algorithm in hatoop”.
• [3] Girish L, Gangadhar S, Bharath T R, Balaji K S, Abhishek K T “Crop Yield and Rainfall Prediction in Tumakuru
District using Machine Learning”.
• [4] Rahul Katarya, Ashutosh Raturi, Abhinav Mehndiratta, Abhinav Thapper “Impact of Machine Learning Techniques
in Precision Agriculture”.
• [5] Pijush Samui, Venkata Ravibabu Mandla, Arun Krishna and Tarun Teja “Prediction of Rainfall Using Support Vector
Machine and Relevance Vector Machine”.
• [6] Himani Sharma, Sunil Kumar “A Survey on Decision Tree Algorithms of Classification in Data Mining”.
• [7] Pavan Patil, Virendra Panpatil, Prof. Shrikant Kokate “Crop Prediction System using Machine Learning Algorithms”.](https://image.slidesharecdn.com/pptofcropprediction-241028051754-77b30822/85/PPT-of-Crop-prediction-FROM-CSE-DEPT-CMR-11-320.jpg)
PPT of Crop prediction FROM CSE DEPT. CMR
- 1. CROP PREDICTION USING MACHINE LEARNING APPROACHES Paper ID:135 Presented by Khushboo Behra 218R1D5801 Department of CSE, CMR Engineering College, Hyderabad
- 2. ABSTRACT The vast majority of Indians work in agriculture which is not surprising given that India has the world's second-highest population. Farmers consistently plant the same crops without experimenting with new varieties, they randomly apply fertilizers without understanding the insufficient substance and amount. we have built the benefit of farmers. Based on the soil's composition and the upcoming weather, our technology will recommend the ideal crop for that plot of land.
- 3. 1. INTRODUCTION • Agriculture is a growing field of research. • In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. • In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. • In recent years, machine learning techniques have taken over the task of prediction, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. • The crop prediction is a significant problem in the agriculture sector . Every farmer tries to know crop yield and whether it meets their expectations.
- 4. 2. LITERATURE REVIEW:- BASED ON SOIL CONDITIONS This scheme allows for the categorization of soils and the prediction of crop yields. Wheat yield predictions are made using a hybrid support vector machine/artificial neural network. The authors also used Amazon S3 and the Heroku cloud to store agricultural data. The writers also provide details on machine learning and the many approaches to it. Seasonal crops, all-year crops, short- and long-term plantation crops, and fast-growing and slow-growing crops were all assigned distinct categories in the suggested Crop Selection System. BASED ON ENVIRONMENTAL CONDITIONS The new design uses a multi-modular approach, comprising a cropping template as well as soil and weather modules. Further, there is a module that monitors light and water in the crops, soil, and environment.
- 5. 3. EXISTING SYSTEM: The methodology enhances existing procedures in three different ways. A remote detecting network is applied to propose a working methodology. Next, a novel dimensionality reduction procedure is presented that uses a convolutional neural network (CNN) alongside long-term memory. A Gaussian process is used to investigate and examine the spatio-transient structure of the data and enhance its accuracy. The data set is divided into two parts that is 70% of the data are used for training and 30% used for testing. Based on the results, it is clear that the classification accuracy of Random forest Regression algorithm is better compared to other algorithms The decision tree, kNN, and Naive Bayes (NB) are used as learners to help determine the most appropriate crop, taking into consideration soil parameters, with the results showing high accuracy and potency.
- 6. 4. PROPOSED SYSTEM The proposed focuses on techniques that can predict Land range using Naïve Bayes, Decision tree, Support Vector Machine(SVM) and Logistic Regression. A comparative study is performed on classifiers to measure the better performance on an accurate rate. From this experiment, SVM gives highest accuracy rate, whereas for Crop Naïve Bayes gives the highest accuracy. Boruta is a random forest-based classification algorithm that involves the voting of versatile unbiased in distinct classifiers in decision trees. Machine learning algorithms including random forest, K-NN, Decision Tree, and Neural Network were utilised to create this system. Both a rain forecaster and a map viewer are included into the proposed system. Drawback - Weather information not considered as well as fertilizers not recommend Proposed System–Use the weather parameter while recommend crops to users and also recommend them about fertilizers. Drawback – Only suggest that crops which presented in the system Proposed System parameter – Use cloud computing so the information will be daily updated and user can see the Current Scenario.
- 7. 5. MODULES / SYSTEM ARCHITECTURE:- Service Provider In this module, the Service Provider has to login by using valid user name and password. After login successful he can do some operations such as Browse Agriculture Data Sets and Train & Test, View Trained and Tested Accuracy in Bar Chart, View Trained and Tested Accuracy Results, View All Crop Yield and Production Prediction, View All Crop Recommendations, Download Predicted Data Sets, View All Remote Users, View Crop Yield Prediction Per Acre Results. View and Authorize Users In this module, the admin can view the list of users who all registered. In this, the admin can view the user’s details such as, user name, email, address and admin authorizes the users. Remote User In this module, there are n numbers of users are present. User should register before doing any operations. Once user registers, their details will be stored to the database. After registration successful, he has to login by using authorized user name and password. Once Login is successful user will do some operations like PREDICT CROP YIELD AND PRODUCTION, PREDICT CROP RECOMMENDATION, VIEW YOUR PROFILE
- 9. 6. ALGORITHEMS USED: • Support vector machine – Svm is machine learning techniques based on an independent and distributed training dataset. svm algorithm are used for training dataset. • Logistic Regression classifiers – Logistic regression analysis studies the association between a categorical dependent variable and set of independent variables.. • KNN (K-nearest neighbours) - its Simple but very powerful algo classified based on a similarity measure whatever we have a new data to classify. We find its K- nearest neighbors from the training data. • Naive Bayes – The naïve bayes approach is a supervised learning method which is based on a simplistic hypothesis. Naive Bayes is a machine learning classification method also known as a probabilistic classifier • Decision Tree classifiers- Decision tree classified are used successfully in many diverse areas. Decision tree are used for testing dataset . if we have large amount of dataset then we used trained and classified algorithm.
- 10. CONCLUSION: Although there are several limitations to overcome to be able to implement ML algorithms in clinical practice, overall ML algorithms This paper highlighted the limitations of current systems and their practical usage on yield prediction. The drawback of Existing System are lack in improvement of single crop Farming System. The various algorithms are compared with their accuracy. The results obtained indicate that Random forest regression is the best classified algorithms used on the given datasets with an accuracy of 95%. The Proposed system copies up with the above stated drawback the System will provide the Statistical analysis of crop System is a stepping stone towards more automated resolution of farmer assistant system which will solve many problems of farmers.. Just providing a land's GPS coordinates allows us to access the government's weather prediction database, allowing us to accurately anticipate the harvest. The future work will be focused on updating the datasets from time to time to produce accurate predictions, and the processes can be automated.
- 11. REFERENCES • [1] Prof. D.S. Zingade ,Omkar Buchade ,Nilesh Mehta ,Shubham Ghodekar ,Chandan Mehta “Crop Prediction System using Machine Learning”. • [2] Ashwani kumar Kushwaha, Swetabhattachrya “crop yield prediction using agro algorithm in hatoop”. • [3] Girish L, Gangadhar S, Bharath T R, Balaji K S, Abhishek K T “Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning”. • [4] Rahul Katarya, Ashutosh Raturi, Abhinav Mehndiratta, Abhinav Thapper “Impact of Machine Learning Techniques in Precision Agriculture”. • [5] Pijush Samui, Venkata Ravibabu Mandla, Arun Krishna and Tarun Teja “Prediction of Rainfall Using Support Vector Machine and Relevance Vector Machine”. • [6] Himani Sharma, Sunil Kumar “A Survey on Decision Tree Algorithms of Classification in Data Mining”. • [7] Pavan Patil, Virendra Panpatil, Prof. Shrikant Kokate “Crop Prediction System using Machine Learning Algorithms”.
- 12. THANKYOU