Intelligent Crop Recommendation System using Machine Learning

Aim:

      Predict the crop yield and deliver the end user with proper recommendations about required fertilizer ratio based on soil parameters. We also recommend the crop price using machine learning.

Introduction:

        Agriculture is India’s prime occupation. It plays a major role in Indian economy and provides a lot of employment opportunities for the people of the nation. Nowadays, farmers do not choose the correct crop to cultivate in that specific soil. Our crop prediction project dataset are collected from kaggle.com. The parameters for crop predictions are Location, temperature, humidity, ph, rainfall, N, P, K etc. Nowadays, these parameters should be considered while cultivating a certain kind of crop, on a specific type of soil. A crop recommender system, takes in consideration the various parameters of the soil, to predict the best kind of crop to be cultivated. This specific like recommender system model, will take into consideration, the parameters soil moisture content, humidity and temperature.

Existing system

         Now a day’s, dilettante farmers are hard to understand the cultivation process, crop type, climate change, etc. Farming is that the spine for every nation’s economy. Future agriculture depends on dilettante formers. But new farmers not so strong at farming, So Machine learning help to solve their problems The existing system predicts the crop yield by using the soil parameters and recommend Fertilizer using machine learning. It uses the crop yield information to make the end users decide on the crop to be sown. Hence the system is not simple enough for dilettante farmers to understand.

Problem definition:

         This is an attempt to find the presence of any  relationships between the various attributes present in the dataset. Acquisition of Training Dataset This dataset has five columns with the attributes in the order-State, Nitrogen content, Phosphorous content, Potassium content, and average ph. vi) Rainfall Temperature dataset: This dataset contains crops, max, and min rainfall, max and min temperature, max and min rainfall, and ph values collect this type of dataset.

 Proposed system

         The system prepared predict major crops yield in a particular district in Tamil Nadu. The client on their first login has to register themselves on the Web application created by flask. The login details are stored in SQLite database. Once the user login into the system they gets all the access for predicting crop yield and using the input such as location, nitrogen, phosphorous, potassium and pH values depends on their forming land environment.. We can also find the primary nutrients of soil by given the input as crop name. It passes the various inputs to the controller which uses the Random Forest for classification. We recommend to the former how much fertilizer required in ratio based on soil parameters and the crop price using machine learning techniques.

Advantages:

        In addition to this, if the right crop is selected by the farmer then they will get the prediction about the yield also. The objective is to, 1. Build a robust model to give a correct and accurate prediction of crop sustainability in a given state for the particular soil type and climatic  conditions. 2. Provide recommendation of the best suitable crops in the area so that the farmer does not incur any loss3. Provide profit analysis of various crops based on the previous year’s data.