农业机械学报2026，Vol.57Issue(2)：276-289,14.DOI:10.6041/j.issn.1000-1298.2026.02.027
基于CNN和LSTM深度学习算法的作物播期差异遥感识别
Remote Sensing Identification of Crop Sowing Date Differences Based on CNN and LSTM Deep Learning Algorithms
摘要
Abstract
Remote sensing identification of the difference in crop sowing timing can provide spatial data support for precision agriculture and smart agriculture,and is of great value in promoting the transformation of agricultural production from"experience-driven"to"data-driven".The double threshold decision-making and deep learning algorithms were integrated,based on Landsat 8/9 OLI image data and field sampling data,the time series characteristics curves of crop NDVI was extracted,revealing the dynamic characteristics of crops from emergence to vigorous growth period(April 25th-July 30th),and identifying the sensitive growth stages of crop sowing timing differences.The growth slope of crops in the sensitive growth stage and the difference in NDVI between adjacent images were calculated.When the growth slope was greater than the sample average and the NDVI difference was greater than the sample average,it was determined as an early sowing sample;otherwise,it was a late sowing sample.Random forest,artificial neural network machine learning models,and convolutional neural network,long short-term memory network deep learning models were used for remote sensing identification of crop sowing timing differences.The results showed that the NDVI mean value of early sowing crops within the sensitive growth stage was generally higher than that of late sowing crops by 0.1～0.3,and the growth process of early sowing crops was significantly earlier.The rapid growth period of early sowing corn was 7 to 13 days earlier than that of late sowing corn,and early sowing sunflower was 10 to 16 days earlier.The growth peak of early sowing zucchini was 12 to 24 days earlier than that of late sowing,early sowing tomato was 5 to 20 days earlier,and early sowing melon was 18 to 35 days earlier.The classification results showed that the RF and CNN models performed better,with overall accuracies reaching 91.77%and 90.66%respectively,and Kappa coefficients of 0.91 and 0.90,which could effectively distinguish the early and late sowing situations of the five types of crops:corn,sunflower,zucchini,melon,and tomato.Through detailed comparison of the classification results of each model,the CNN classification image was more continuous and had lower fragmentation,and the CNN model was selected as the optimal classification model in this study.关键词
早晚播/NDVI时序特征/作物分类/深度学习/遥感Key words
early and late broadcasting/NDVI temporal characteristics/crop classification/deep learning/remote sensing分类
农业科技引用本文复制引用
白燕英,苗丰,李二珍,范泽华..基于CNN和LSTM深度学习算法的作物播期差异遥感识别[J].农业机械学报,2026,57(2):276-289,14.基金项目
内蒙古自治区自然科学基金项目(2023LHMS05014)、内蒙古自治区科技计划项目(2025YFHH0097)和内蒙古自治区科技重大专项(2021ZD0003) （2023LHMS05014）
