With the advancement of the internet, individuals are becoming more reliant
on online applications to meet most of their needs. In the meantime, they
have very little spare time to devote to the selection and decision-making
process. As a result, the need for recommender systems to help tackle this
problem is expanding. Recommender systems successfully provide
consumers with individualized recommendations on a variety of goods,
simplifying their duties. The goal of this research is to create a recommender
system for farmers based on tree data structures. Recommender system has
become interesting research by simplifying and saving time in the
decision-making process of users. We conducted although a lot of research
in various fields, there are insufficient in the agriculture sector. This issue is
more necessary for farmers in Quangnam-Danang or all Vietnam countries
by severe climate features. Storm from that, this research designs a system
based on tree data structures. The proposed model combines the you only
look once (YOLO) algorithm in a convolutional neural network (CNN)
model with a similarity tree in computing similarity. By experiments on 400
samples and evaluating precision, accuracy, and the value of the predictive
test as determined by its positive predictive value (PPV), the research proves
that the proposed model is feasible and gain better results compared with
other state-of-the-art models.

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