Agricultural Price Prediction using Machine Learning


Agricultural Price Prediction using Machine Learning


Agriculture creates an economic future for developing countries, the demand for modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning, and Visualization. As the product, an android mobile application is developed. In this application, the users should input their location to start the prediction process. Data  preprocessing is started when the location is received by the system. The collected dataset is divided into 3 parts. 80 percent for training, 10 percent for testing, and 10 percent for validation. Finally, for a given location profitable crops and the predicted future prices of vegetables are shown in the application. Agricultural Price Prediction using Machine Learning


The Motive of the paper is to detect and determine the nature and quality of soil-based in a particular area, considering the toxicity level at the present instance of time and predicting its future value using the ML model.


Existing systems use Data Mining concepts for crop prediction. Also, they use a small level medium dataset for the training and testing. In this existing method, they are taking less amount of features for training the data, and selecting the target value is also a little bit complex.


  • Less amount of accuracy score
  • Small level dataset
  • Applicable on small level prediction work.


In our proposed method we approach the deep learning methods and machine learning algorithms. Using these kind of techniques, we predict the accuracy score highly. Also, we introduce the android app for the visualization and predicting future crop values based upon our models.


agricultural price prediction and visualization on android app and machine learning abstract
agricultural price prediction and visualization on android app and machine learning abstract


  • Increasing the accuracy score
  • For the live example android app in our project
  • A large number of features we are taking for the training and testing.



  • Operating System:???????? Windows 7, 8, and 10 (32 and 64 bit)
  • Front End:???????? Python
  • Packages:??? ???? NumPy, Pandas, matplotlib, Sklearn


  • Processor -??????? Dual Core
  • RAM -??????? 2 GB or above
  • Hard Disk -??????? 100 GB or above


               It has a moderate climate throughout the year in most parts of the country. As the country is small, cultivated crops are distributed all over the country, because of that a reasonable market price is remaining as a challenging issue for farmers. To overcome this problem, Agro-genius application advice to predict the most profitable crops and their expected price during harvesting time according to the location, by predicting different historical raw datasets using different machine learning algorithms. This system helps farmers to take correct decisions for selecting suitable crops, which will maximize the profit.

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