Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM

IF 2.2 2区 农林科学 Q2 AGRICULTURAL ENGINEERING
Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu
{"title":"Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM","authors":"Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu","doi":"10.25165/j.ijabe.20231602.7020","DOIUrl":null,"url":null,"abstract":": Crop coverage (CC) is an important parameter to represent crop growth characteristics, and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions. In this study, a novel CNN-LSTM model that combined the advantages of convolutional neural network (CNN) in feature extraction and long short-term memory (LSTM) in time series processing was proposed for multi-day ahead forecasting of maize CC. Considering the influence of climate change on maize growth, five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model. The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM. The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R 2 at all forecasting horizons. Subsequently, the performance of CNN-LSTM under univariate (historical maize CC) and multivariate (historical maize CC+microclimatic factors) input was compared, and the results indicated that additional microclimatic factors were effective in improving the forecasting performance. Furthermore, the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed, and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage. Therefore, CNN-LSTM can be considered a useful tool to forecast maize CC.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Biological Engineering","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.25165/j.ijabe.20231602.7020","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

: Crop coverage (CC) is an important parameter to represent crop growth characteristics, and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions. In this study, a novel CNN-LSTM model that combined the advantages of convolutional neural network (CNN) in feature extraction and long short-term memory (LSTM) in time series processing was proposed for multi-day ahead forecasting of maize CC. Considering the influence of climate change on maize growth, five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model. The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM. The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R 2 at all forecasting horizons. Subsequently, the performance of CNN-LSTM under univariate (historical maize CC) and multivariate (historical maize CC+microclimatic factors) input was compared, and the results indicated that additional microclimatic factors were effective in improving the forecasting performance. Furthermore, the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed, and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage. Therefore, CNN-LSTM can be considered a useful tool to forecast maize CC.
整合田间图像和小气候数据,利用CNN-LSTM实现玉米作物覆盖多日预报
:作物盖度是表征作物生长特征的重要参数,对作物盖度进行前瞻性预测有助于跟踪作物生长趋势,指导农业经营决策。本研究结合卷积神经网络(CNN)在特征提取方面的优势和时间序列处理方面的长短期记忆(LSTM)优势,提出了一种新的CNN-LSTM模型,用于玉米CC的多日预报,考虑气候变化对玉米生长的影响,将5个小气候因子与田间图像估计的历史玉米CC相结合,作为预测模型的输入变量。利用3年多的4个观测点的野外实验数据,对CNN-LSTM在未来3 ~ 7天的预测视界上的表现进行评价,并将预测结果与CNN和LSTM进行对比。结果表明,CNN-LSTM在各预测层均具有最低的RMSE和最高的r2。对比了单变量(历史玉米CC)和多变量(历史玉米CC+小气候因子)输入下CNN-LSTM的预测性能,结果表明,额外的小气候因子能有效提高预测性能。此外,还对CNN-LSTM在玉米不同生育期的3 d预报结果进行了分析,结果表明,在七叶期预报精度最高。因此,CNN-LSTM可以被认为是预测玉米CC的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.30
自引率
12.50%
发文量
88
审稿时长
24 weeks
期刊介绍: International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信