Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Wei Zhang, Wenyu Zhong, Zhidong Liu, Baoxia Du, Mingliang Li, Meng Huang, Helong Yu, Bowei Chen () and You Tang ()
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Wei Zhang: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Wenyu Zhong: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Zhidong Liu: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Baoxia Du: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Mingliang Li: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Meng Huang: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Helong Yu: College of Information Technology, Jilin Agricultural University, Changchun 132101, China
Bowei Chen: Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao 266400, China
You Tang: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China

Sustainability, 2024, vol. 16, issue 10, 1-15

Abstract: Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Keywords: tomato; monitoring and data acquisition; BP neural network prediction model; genetic algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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