Author(s) :
Kaviya V. G, Gini R
Conference Name :
International Conference on scientific innovations in Science, Technology, and Management (NGCESl-2023)
Abstract :
For several years, farmers in India have had little liberty in choosing markets and purchasers for their produce. All states in the country, except three, degree that marketing and selling of farm produce must be directed through state-owned mandis, retail markets where mediators (middlemen) crush farmers to increase margins. According to research, mediators have become dominating buyers of the agricultural market, resulting them to take control over the plight of the farmers and gulping all the profits. The farmers work day and night expecting a good yield. They use a lot of financial resources lending money and buying fertilizers, seeds etc. So, they have the right to enjoy every rupee gained on their corp. In this context, we propose a system which brings farmers close to the retailers cutting the middlemen. Our system consists of a mobile or web application which will serve as a platform for farmer the growers and retailers or customers to sell and buy their farm products. This system aims at giving a profitable price to farmers to their farm products cutting the middlemen. This allows the retailers or the customers to buy products from the farmers at a lower than the normal price. Farmer uploads their product with details and buyers view these details and book that product with in a time. The bidding system is suitable for bulk buyers who would like to bargain for a certain product. They will be able to bid on a product as well as view other bids. This will help them get products at a better price. The consumer can give a rating and review only after having purchased a particular product. K-Nearest Neighbours (KNN) is proposed to recommendation system based on common product ratings, and make predictions using the average rating of top-k nearest neighbours. These are visible on each item’s page along with the average of the item’s average rating. K-Means is used to overcome sparsity problems and to form user clusters to reduce the amount of data that needs to be processed.
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