An NLP Hybrid Recommendation System In Crop Selection For Farmers
The use of the method named Recommender Systems with Machine Learning is of emerging field. Recommending programs mostly cover a collection of techniques and algorithms that can recommend ‘related’ objects to consumers. Basically, recommendation mechanisms are classified into two major categories: collaborative screening and content-based screening. A recommendation framework is a computer program that lets a customer find crops by estimating each item's farmer's rating and showing them the things, they will be highly recommended for. Recommendation services play a significant part in helping farmers to identify crops and issues that they care for and information. A recommender program refers to a program worthy of forecasting a range of products for a farmer's potential interest, and suggests the crop and the appropriate best prices. The main factor that need a recommender program in our culture is that because of the proliferation of the network, individuals have so many choices to choose from. The farmers ask the question to the system then the question converts into the text by the convertors. The converted text is applied on the data warehouse thru machine learning recommendation algorithms. The UBCF, IBCF and SVD are used for recommendations for promoting crops and its tools for the farmers which demonstrates that the results have achieved good performance. By applying the machine learning algorithms the farm management system are providing rich recommendations and insights to the farmers.