Improving Mapping Accuracy of Smallholder Potato Planting Areas by Embedding Prior Knowledge into a Novel Multi-temporal Deep Learning Network

IF 2.3 3区 农林科学 Q1 AGRONOMY
Sen Yang, Quan Feng, Xueze Gao, Wanxia Yang, Guanping Wang
{"title":"Improving Mapping Accuracy of Smallholder Potato Planting Areas by Embedding Prior Knowledge into a Novel Multi-temporal Deep Learning Network","authors":"Sen Yang, Quan Feng, Xueze Gao, Wanxia Yang, Guanping Wang","doi":"10.1007/s11540-024-09769-2","DOIUrl":null,"url":null,"abstract":"<p>Accurate and timely acquisition of potato spatial distribution is crucial for growth monitoring and yield forecasting. Currently, prior knowledge-based methods are very simple and efficient without collecting reference data, but their mapping accuracy in complex cropping planting systems is unsatisfactory. Deep learning approaches have the ability to automatically learn multilevel spatial and spectral features. However, these approaches still face particular challenges in improving potato mapping accuracy due to the limitations of adaptive features and the scarcity of ground samples. This study proposed a potato mapping method integrating a multi-temporal deep learning network and prior knowledge to overcome the shortcomings of the two methods. Specifically, a novel deep learning network, spectral-spatial–temporal ensemble network (SSTEN), was developed for smallholder potato area mapping by embedding unique prior knowledge. To obtain multi-year potato mapping results, we proposed a concise and efficient temporal transfer framework that combines sample generation, SSTEN transfer learning, and agriculture statistics to produce highly accurate potato maps for sample-free years. Independent ground validation data from 2021 to 2022 suggested that the SSTEN achieved an overall accuracy (OA), F1 and Kappa of 91.65%, 92.67% and 0.82, respectively, and its average overall accuracy was superior to other methods. Potato planting areas obtained by SSTEN were highly consistent with the corresponding agricultural statistical area (<i>R</i><sup>2</sup> &gt; 0.87). The results showed that incorporating prior knowledge into SSTEN could improve the accuracy of potato mapping. We also investigated the potential of the proposed temporal transfer method for potato mapping. Our transfer method yielded a high OA of 86.46% and an area error (AE) of 7.94%. The study potentially provides technical references for smallholder potato mapping in similar agricultural regions worldwide.</p>","PeriodicalId":20378,"journal":{"name":"Potato Research","volume":"153 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Potato Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11540-024-09769-2","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate and timely acquisition of potato spatial distribution is crucial for growth monitoring and yield forecasting. Currently, prior knowledge-based methods are very simple and efficient without collecting reference data, but their mapping accuracy in complex cropping planting systems is unsatisfactory. Deep learning approaches have the ability to automatically learn multilevel spatial and spectral features. However, these approaches still face particular challenges in improving potato mapping accuracy due to the limitations of adaptive features and the scarcity of ground samples. This study proposed a potato mapping method integrating a multi-temporal deep learning network and prior knowledge to overcome the shortcomings of the two methods. Specifically, a novel deep learning network, spectral-spatial–temporal ensemble network (SSTEN), was developed for smallholder potato area mapping by embedding unique prior knowledge. To obtain multi-year potato mapping results, we proposed a concise and efficient temporal transfer framework that combines sample generation, SSTEN transfer learning, and agriculture statistics to produce highly accurate potato maps for sample-free years. Independent ground validation data from 2021 to 2022 suggested that the SSTEN achieved an overall accuracy (OA), F1 and Kappa of 91.65%, 92.67% and 0.82, respectively, and its average overall accuracy was superior to other methods. Potato planting areas obtained by SSTEN were highly consistent with the corresponding agricultural statistical area (R2 > 0.87). The results showed that incorporating prior knowledge into SSTEN could improve the accuracy of potato mapping. We also investigated the potential of the proposed temporal transfer method for potato mapping. Our transfer method yielded a high OA of 86.46% and an area error (AE) of 7.94%. The study potentially provides technical references for smallholder potato mapping in similar agricultural regions worldwide.

Abstract Image

将先验知识嵌入新型多时相深度学习网络,提高小农马铃薯种植区的测绘精度
准确及时地获取马铃薯的空间分布情况对于生长监测和产量预测至关重要。目前,基于先验知识的方法在不收集参考数据的情况下非常简单高效,但其在复杂作物种植系统中的绘图精度却不尽人意。深度学习方法具有自动学习多层次空间和光谱特征的能力。然而,由于自适应特征的局限性和地面样本的稀缺性,这些方法在提高马铃薯测绘精度方面仍面临特殊挑战。本研究提出了一种整合了多时空深度学习网络和先验知识的马铃薯测绘方法,以克服这两种方法的不足。具体而言,通过嵌入独特的先验知识,为小农马铃薯面积测绘开发了一种新型深度学习网络--光谱-时空集合网络(SSTEN)。为了获得多年期马铃薯测绘结果,我们提出了一个简洁高效的时空转移框架,将样本生成、SSTEN 转移学习和农业统计结合起来,为无样本年份生成高精度的马铃薯地图。2021 年至 2022 年的独立地面验证数据表明,SSTEN 的总体精度(OA)、F1 和 Kappa 分别达到 91.65%、92.67% 和 0.82,其平均总体精度优于其他方法。SSTEN 得出的马铃薯种植面积与相应的农业统计面积高度一致(R2 > 0.87)。结果表明,将先验知识纳入 SSTEN 可以提高马铃薯绘图的准确性。我们还研究了所提出的时空转移方法在马铃薯测绘中的应用潜力。我们的转移方法产生了高达 86.46% 的 OA 和 7.94% 的面积误差 (AE)。这项研究可为全球类似农业地区的小农马铃薯测绘提供技术参考。
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
Potato Research
Potato Research AGRONOMY-
CiteScore
5.50
自引率
6.90%
发文量
66
审稿时长
>12 weeks
期刊介绍: Potato Research, the journal of the European Association for Potato Research (EAPR), promotes the exchange of information on all aspects of this fast-evolving global industry. It offers the latest developments in innovative research to scientists active in potato research. The journal includes authoritative coverage of new scientific developments, publishing original research and review papers on such topics as: Molecular sciences; Breeding; Physiology; Pathology; Nematology; Virology; Agronomy; Engineering and Utilization.
×
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