Improved Loss Function-Based Prediction Method of Extreme Temperatures in Greenhouses

Abstract

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.

MV-CDN: Multi-Visual Collaborative Deep Network for Change Detection of Double-Temporal Hyperspectral Images

“…In this respect, deep neural networks (DNNs) possess arbitrary nonlinear expressiveness [19] and are regarded as the main content of studies. On the premise of full-band information, the breakthrough points of the DNN-based methods usually include supervised algorithms [20,21], feature enhancement [22,23], ways to avoid negative factors, optimization of objective loss function [24] for applicability requirements, etc. With the efforts of many researchers, extraordinary progress has been made in these respects.…”