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METHODS article

Front. Sustain. Food Syst.
Sec. Crop Biology and Sustainability
Volume 8 - 2024 | doi: 10.3389/fsufs.2024.1405051

Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

Provisionally accepted
Ruipeng Tang Ruipeng Tang 1*Narendra Kumar Narendra Kumar 1Mohamad Abu Talip Sofian Mohamad Abu Talip Sofian 1Jianrui Tang Jianrui Tang 2
  • 1 University of Malaya, Kuala Lumpur, Malaysia
  • 2 Shanghai Maritime University, pudong, Shanghai, China

The final, formatted version of the article will be published soon.

    The traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, the main goal of this study is to design a precise fertilization method for greenhouse vegetables based on the improved backpropagation neural network (IM-BPNN) algorithm to increase fertilizer utilization efficiency, reduce production costs, and improve the economic viability of agriculture. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score(zero-mean normalization) standardization method.Then, the BPNN(backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY(nutrient balance target yield) ,BPNN(backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the BPNN and NBTY algorithm, it shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture.

    Keywords: Greenhouse agriculture, fertilization prediction, nutrient management, smart agriculture, machine learning for greenhouse crop fertilization

    Received: 05 Apr 2024; Accepted: 24 Jun 2024.

    Copyright: © 2024 Tang, Kumar, Sofian and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ruipeng Tang, University of Malaya, Kuala Lumpur, Malaysia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.