智慧农业（中英文）2025，Vol.7Issue(1)：97-110,14.DOI:10.12133/j.smartag.SA202410032
基于多模态数据表型特征提取的番茄生长高度预测方法
Tomato Growth Height Prediction Method by Phenotypic Feature Extraction Using Multi-modal Data
摘要
Abstract
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based mod-els that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of fea-tures,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef-fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model con-tinued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart to-mato planting management.关键词
番茄生长预测/深度学习/表型特征提取/多模态数据/递归神经网络/长短期记忆网络/大语言模型Key words
tomato growth prediction/deep learning/phenotypic feature extraction/multi-modal data/recurrent neural net-work/long short-term memory/large language model分类
信息技术与安全科学引用本文复制引用
宫宇,王玲,赵荣强,尤海波,周沫,刘劼..基于多模态数据表型特征提取的番茄生长高度预测方法[J].智慧农业（中英文）,2025,7(1):97-110,14.基金项目
The Fundamental Research Funds for the Central Universities in China(2023FRFK06013) （2023FRFK06013）
The Key Research and Development Program of Heilongjiang Province(2023ZX01A24) （2023ZX01A24）
Harbin Institute of Technology Horizontal Project(MH20240081) 中央高校基本科研业务费专项资金(2023FRFK06013) （MH20240081）
黑龙江省重点研发计划项目(2023ZX01A24) （2023ZX01A24）
哈尔滨工业大学横向项目(MH20240081) （MH20240081）
