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CPDOS: A Web-Based AI Platform to Optimize Crop Planting Density.

Authors :
Zhu, Rongsheng
Zhang, Zhixin
Cao, Yangyang
Hu, Zhenbang
Li, Yang
Cao, Haifeng
Zhao, Zhenqing
Xin, Dawei
Chen, Qingshan
Source :
Agronomy. Oct2023, Vol. 13 Issue 10, p2465. 25p.
Publication Year :
2023

Abstract

Increasing crop yield is a significant objective in modern agriculture, with adjusted planting density and rational fertilization strategies standing out as the foremost approaches for attaining such a goal. Through the use of modern artificial intelligence techniques such as genetic algorithms and neural networks, the CPDOS (Crop Planting Density Optimization System), an online intelligent system that can automate the modeling, optimization, and analysis of the two models, was developed in the present study. The goal of the system is to optimize the planting density model and fertilizer application in combination with other computer system development techniques. The CPDOS comprises three main modules: yield density optimization module, optimal planting density range module, and fertilization and planting density optimization module. The three modules are complemented by two modules for data input and result visualization, culminating in the comprehensive process of optimizing planting density and fertilizer allocation through the CPDOS. The CPDOS was tested using potato, corn, and soybean data, and the results show that the optimization effects of planting density and fertilizer application were satisfactory. The CPDOS is an automated crop planting optimization system that integrates algorithms and models and is driven by artificial intelligence technology. The introduction of the CPDOS reduces the barriers to utilizing these algorithms and models, facilitating wider adoption of intelligently optimized planting technology. The platform's launch will accelerate the swift advancement of this field. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734395
Volume :
13
Issue :
10
Database :
Academic Search Index
Journal :
Agronomy
Publication Type :
Academic Journal
Accession number :
173263954
Full Text :
https://doi.org/10.3390/agronomy13102465