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Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring System

Anas Bilal1,*, Xiaowen Liu1, Haixia Long1,*, Muhammad Shafiq2, Muhammad Waqar3

1 College of Information Science Technology, Hainan Normal University, Haikou, 571158, China
2 School of Information Engineering, Qujing Normal University, Qujing, 655011, China
3 Department of Computer Science, COMSATS University, Islamabad, 45550, Pakistan

* Corresponding Authors: Anas Bilal. Email: ; Haixia Long. Email:

Computers, Materials & Continua 2023, 76(2), 2401-2426.


Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease datasets is used to detect weeds, pesticides, and diseases in the crop. The proposed model achieved an average accuracy of 95%, 97%, and 98% in plant seedlings, pests, and plant leaf disease detection, respectively. The system can help farmers pinpoint the precise measures required at the right time to increase yields.


Cite This Article

A. Bilal, X. Liu, H. Long, M. Shafiq and M. Waqar, "Increasing crop quality and yield with a machine learning-based crop monitoring system," Computers, Materials & Continua, vol. 76, no.2, pp. 2401–2426, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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