  The prediction of extreme greenhouse temperatures to which crops are
susceptible is essential in the field of greenhouse planting. It can help avoid
heat or freezing damage and economic losses. Therefore, it's important to
develop models that can predict them accurately. Due to the lack of extreme
temperature data in datasets, it is challenging for models to accurately
predict it. In this paper, we propose an improved loss function, which is
suitable for a variety of machine learning models. By increasing the weight of
extreme temperature samples and reducing the possibility of misjudging extreme
temperature as normal, the proposed loss function can enhance the prediction
results in extreme situations. To verify the effectiveness of the proposed
method, we implement the improved loss function in LightGBM, long short-term
memory, and artificial neural network and conduct experiments on a real-world
greenhouse dataset. The results show that the performance of models with the
improved loss function is enhanced compared to the original models in extreme
cases. The improved models can be used to guarantee the timely judgment of
extreme temperatures in agricultural greenhouses, thereby preventing
unnecessary losses caused by incorrect predictions.
