Agricultural Commodities Price Prediction System using ML Algorithm
Vinutha M1, Gayitri H.M2
1Vinutha M, Student, Agricultural University, Berasia, Madhya Pradesh, India.

2Gayitri H.M, Agricultural University, Berasia, Madhya Pradesh, India.

Manuscript received on 24 April 2021 | Revised Manuscript received on 02 May 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 28-31 | Volume-1 Issue-1, May 2021. | Retrieval Number:100.1/ijae.H0102072819

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Abstract: Most of the India’s population depends on agriculture for their source of income. But, as and when it evolved, the complexities also grew with it, which made the farmers to struggle. Few major difficulties being faced by the marginal and small farmers at present are, high investment cost, unpaid loans, lack of basic awareness about agriculture, marketing issues, drought, etc. So, the proposed work gives solution to few of these issues through a web application. It firstly gives an “Agricultural Guide” in the form of a PDF file that contains overall information starting from farm land details to marketing their cultivated product and secondly, it includes a crop price prediction system that uses the Naive Bayes algorithm. The crop price prediction system is specifically developed for Mysuru District. The dataset containing Crop name, Rainfall, Yield, Max Trade, Crop Price is used as its attributes. The web app contains agricultural departments as one of its users, that is the different regulated agricultural markets present in Mysuru, the staff of any particular department can upload the training dataset and can predict the price. The farmers can know the predicted price by contacting the respective agricultural departments. This application is developed using Visual Studio IDE and SQL Server Management Studio Express .The application is bilingual with Kannada and English languages and the Android version of the same application is provided for faster access. Hence the application helps to reduce some of the problems being faced by the farmers.

Keywords: Agriculture, Naive Bayes, Price Prediction, Web Application.