Crop Prediction System using Machine Learning

Crop Prediction System using Machine Learning

India being an agricultural country, its economy predominantly depends on agriculture yield growth and allied agro industry products. In India, agriculture is largely influenced by rainwater which is highly unpredictable. Agriculture growth also depends on diverse soil parameters, namely Nitrogen, Phosphorus, Potassium, Crop rotation, Soil moisture, Surface temperature, and also on weather aspects which include temperature, rainfall, etc. India now is rapidly progressing towards technical development.

Thus, technology will prove to be beneficial to agriculture which will increase crop productivity resulting in better yields for the farmer. The proposed project provides a solution for Smart Agriculture by monitoring the agricultural field which can assist the farmers in increasing productivity to a great extent. Weather forecast data obtained from IMD (Indian Metrological Department) such as temperature and rainfall and soil parameters repository gives insight into which crops are suitable to be cultivated in a particular area. This work presents a system, in form of an android based application, which uses data analytics techniques in order to predict the most profitable crop in the current weather and soil conditions

The proposed system will integrate the data obtained from the repository, weather department and by applying machine learning algorithm: Multiple Linear Regression, a prediction of most suitable crops according to current environmental conditions is made. This provides a farmer with a variety of options of crops that can be cultivated. Thus, the project develops a system by integrating data from various sources, data analytics, prediction analysis which can improve crop yield productivity and increase the profit margins of farmers helping them over the longer run.

MODULES:

Input: The prediction of the crop is dependent on numerous factors such as Soil Nutrients, weather, and past crop production in order to predict the crop accurately. All these factors are location reliant and thus the location of the user is taken as an input to the system.
Data Acquisition: Depending on the current user location, the system mines the soil properties in the respective area from the soil repository. In a similar approach, weather parameters are extracted from the weather data set.
Data Processing: A crop can be cultivable only if apropos conditions are met. These include extensive parameters allied to soil and weather. These constraints are compared and the apt crops are ascertained. Multiple Linear Regression is used by the system to predict the crop. The prediction is based on past production data of crops i.e.: identifying the tangible weather and soil parameters and comparing it with current conditions which will predict the crop more accurately and in a practical manner.
Output: The most profitable crop is predicted by the system using the Multiple Linear Regression algorithm and the user is provided with multiple suggestions of crop conferring to the duration of the crop.

Algorithm used

K Nearest Neighbor, Decision Tree, Naive Bayes, Support Vector Machine.

supported operating system

1. window
2.linux
3.ubantu

Cost and technology

Project Details contact - +91 9964716807
project title Crop Prediction Using Machine Learning
project type Final year project.
Project price ₹ 12000 INR
Project discount ₹ 2000 INR
Final price ₹ 10000 INR
Documentation charges extra cost for documentation for all project
project setup Free

CONCLUSION

The proposed system takes the soil N, P, K, and pH values into consideration and determines which are the best productive crops that can be grown in that suitable soil conditions. Since the system lists all potential crops it helps the farmer determine which crop to be grown in their area. This system thus helps the farmer to decide on the maximum profitable crop and also helps in finding new crops that can be cultivated which have not been cultivated till that time by the farmer. In the future, this system can be implemented further using IOT to get the real-time values of the soil. In the farm, the sensors can be installed to collect information about the current soil conditions, and the systems can therefore increase the accuracy of the correctness of the results. Hence, farming can be done in a smart way.

Project demo

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