A long short‐term memory‐based model for greenhouse climate prediction. Issue 1 (19th August 2021)

• Record Type: Journal Article
• Authors: Liu, Yuwen; Li, Dejuan; Wan, Shaohua; Wang, Fan; Dou, Wanchun; Xu, Xiaolong; Li, Shancang; Ma, Rui; Qi, Lianyong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 1(2022)
• Page Start: 135
• Page End: 151
• Publication Date: 2021-08-19
• Subjects: climate prediction -- greenhouse -- LSTM -- sliding window; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22620
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.