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Journals
Agronomy
Special Issues
Synergistic Technology in Precision and Digital Agriculture
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Synergistic Technology in Precision and Digital Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 4352

Special Issue Editors

Prof. Dr. Wensheng Wang

E-Mail Website
Guest Editor
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 10081, China
Interests: Artificial Intelligence; machine learning; information technology; digital agriculture; agro informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Leifeng Guo

E-Mail Website
Guest Editor
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South St., Haidian District, Beijing 100086, China
Interests: sensors; drone; precision livestock management; smart agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Beibei Xu

E-Mail Website
Guest Editor
Institute of Agricultural Information (IAI), Agriculture Information Institute of Chinese Academy of Agriculture Sciences, No. 12 Zhongguancun South St., Haidian District, Beijing 100086, China
Interests: computer vision; deep learning; precision livestock; image processing; digitial agriculture

Special Issue Information

Dear Colleagues,

The rapid population growth has driven the demand for more food, which is associated with an increase in the need to use natural resources in a more sustainable way. Complicating factors such as lack of labor, difficulties in real-time monitoring, and high costs in management have presented serious challenges to large-scale and intensive farm-based production systems. This requires precise and cost-effective technology methods to address these challenges in animal agricultural systems. The use of precision agriculture machinery and equipment since the 1990s has provided important productive gains and maximized the use of agricultural inputs. Especially with the development of modern information technologies such as IoT (Internet of Things), big data, Artificial Intelligence (AI), and 5G and Blockchain, Precision and Digital Agriculture is demonstrating a tremendous impact on improving the resource use efficiency, productivity, quality, profitability, and sustainability of agricultural production.

In this Special Issue, we focus on aspects related to the application of synergistic technologies in precision and digital agriculture, thus helping to facilitate farming and improving management efficiency.

Prof. Dr. Wensheng Wang
Dr. Leifeng Guo
Dr. Beibei Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precision agriculture
  • digital technologies
  • artificial intelligence
  • remote sensing
  • smart sensors
  • blockchain technologies

Published Papers (1 paper)

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Research

26 pages, 7279 KiB  
Article
UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat
by Zongpeng Li, Zhen Chen, Qian Cheng, Fuyi Duan, Ruixiu Sui, Xiuqiao Huang and Honggang Xu
Agronomy 2022, 12(1), 202; https://doi.org/10.3390/agronomy12010202 - 14 Jan 2022
Cited by 28 | Viewed by 3573
Abstract
Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing [...] Read more.
Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage. Full article
(This article belongs to the Special Issue Synergistic Technology in Precision and Digital Agriculture)
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