基于STM32的农业大棚温湿度自动控制系统设计文献综述

 2022-08-05 14:53:01

Greenhouse environmental control system based on SW-SVR

Abstract

Greenhouse environmental control systems using sensor networks are becoming more widespread and sophisticated. To match the produce of expert farmers, these systems collect data about cultivation environment and growth situation, and aim to control the environment for cultivating high quality crops. However, with no agriculture experience, it is difficult for system users to set control parameters of several devices properly. In order to reproduce prediction control performed by expert farmersrsquo; cultivation without human intervention, the authors propose a smart greenhouse environmental control system based on sliding window-based support vector regression (SW-SVR). The proposed system performs prediction control based on accurate predictions in real time. SW-SVR is a new machine learning algorithm for time series data prediction. The prediction model automatically adjusts to the current environment periodically, predicts time series data with high accuracy and low computational complexity. The proposed system using SW-SVR enables system users to optimize controls for crops. Meanwhile, since plant growth is related to the photosynthesis and transpiration of leaves, the authors developed wireless scattered light sensors which measure leaf area size indirectly so as to estimate plant growth. Our experimental results, using data of scattered light sensors on-site, outside weather data, and forecast data as independent variables of SW-SVR for hydroponic culture of tomatoes, show the proposed system reduced prediction error of nitrogen absorption amount by 59.44% as Mean Absolute Error (MAE) and 52.89% as Root Mean Squared Error (RMSE) compared with SVR, and reduced training data by 43.07% on average. Furthermore, the sugar content of tomatoes cultivated by the prototype system increased 1.54 times compared with usual tomatoes.

Keywords: Support vector regression (SVR); time series data prediction; greenhouse environmental control system; scattered light sensors

1. Introduction

Greenhouse environmental control systems using sensor networks have been studied and developed increasingly [1-3]. To match the produce of expert farmers, these systems collect data about cultivation environment and growth situation, and aim to control the environment for cultivating high quality crops. However, with no agriculture experience, it is difficult for system users to set control parameters of several devices properly depending on environmental factors such as seasons and weathers. Many conventional systems use control devices based on comparison between sensor data and setting values that individual system users have set. These settings depend on the ability of the system users.

In actual fact, prediction control is done by expert farmers depending on their experiences for cultivating high quality crops, and optimizes control for crops. For example, in melon cultivation, they predict the water absorption amount based on the growth level of melons and micrometeorological data, such as air temperature, moisture, CO2 concentration, and soil moisture so as to decide appropriate amount of watering every morning. However, it is hard to reproduce expert farmersrsquo; implicit prediction from agricultural data which properties change with the lapse of time. For that reason, although many researchers have investigated fundamental researches of prediction methods which can apply to agricultural data, it is hard to apply these conventional prediction methods to environments required for accurate predictions in real time in order to perform necessary control with appropriate timing.

In this paper, we propose a smart greenhouse environmental control system based on SW-SVR [4]. The proposed system performs prediction control based on accurate predictions in real time. The purpose of this study is to reproduce prediction control performed by expert farmersrsquo; cultivation without human intervention. SW-SVR is a new machine learning algorithm for time series data prediction. The prediction model automatically adjusts to the current environment periodically, predicts time series data with high accuracy and low computational complexity.

The remainder of this paper is organized as follows. Section 2 shows related work in time series data prediction. The basic idea of the proposed system is described in section 3. Prototype implementation and experimental results are shown in section 4 and section 5 respectively. Conclusion is finally made in section 6.

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