Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu
{"title":"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","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":"{\"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}","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}
Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM
: 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.
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.