Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction

Abstract

Climate change has led to more frequent extreme weather events such as heatwaves, droughts, and storms, which significantly impact agriculture, causing crop damage. Greenhouse cultivation not only provides a manageable environment that protects crops from external weather conditions and pests but also requires precise microclimate control. However, greenhouse microclimates are complex since various heat transfer mechanisms would be difficult to model properly. This study proposes an innovative hybrid model (DF-RF-ANN), which seamlessly fuses three components: the dynamic factor (DF) model to extract unobserved factors, the random forest (RF) to identify key input factors, and a backpropagation neural network (BPNN) to predict greenhouse microclimate, including internal temperature, relative humidity, photosynthetically active radiation, and carbon dioxide. The proposed model utilized gridded meteorological big data and was applied to a greenhouse in Taichung, Taiwan. Two comparative models were configured using the BPNN and the Long short-term memory neural network (LSTM). The results demonstrate that DF-RF-ANN effectively captures the trends of the observations and generates predictions much closer to the observations compared to LSTM and BPNN. The proposed DF-RF-ANN model hits a milestone in multi-horizon and multi-factor microclimate predictions and offers a cost-effective and easily accessible approach. This approach could be particularly beneficial for small-scale farmers to make the best use of resources under extreme climatic events for contributing to sustainable development goals (SDGs) and the transition towards a green economy.

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

“…Interpolation, a basic yet often effective method, shows balanced performance across all variables, particularly in situations where data points are closely aligned in time or space [63]. Shifted Interpolation, despite its potential in handling lagged correlations, does not perform well in this dataset, possibly due to the complex interactions in micro-climate variables not aligning well with shifted patterns [64]. The research found Linear Regression to be the most effective method for imputing humidity and intensity data across various time frames.…”