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All Journal International Journal of Electrical and Computer Engineering
Duc-Hien Nguyen
The University of Danang
Computer Science & IT Electrical & Electronics Engineering

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An approach based on deep learning that recommends fertilizers and pesticides for agriculture recommendation Nguyen Ha Huy Cuong; Trung Hai Trinh; Duc-Hien Nguyen; Thanh Khiet Bui; Tran Anh Kiet; Phan Hieu Ho; Nguyen Thanh Thuy
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5580-5588

Abstract

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.
Co-Authors Nguyen Ha Huy Cuong Nguyen Thanh Thuy Phan Hieu Ho Thanh Khiet Bui Tran Anh Kiet Trung Hai Trinh