Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
4.8
Journals
Drones
Special Issues
Advances of UAV Remote Sensing for Plant Phenology
share announcement

Advances of UAV Remote Sensing for Plant Phenology

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 3589

Special Issue Editors

Dr. Deon Van Der Merwe

E-Mail Website
Guest Editor
Department of Physiological Sciences, College of Veterinary Medicine, Oklahoma State University, Stillwater, OK 74078, USA
Interests: UAV applications in agriculture; environmental assessment; vegetation analysis; toxicology
Dr. Ajay Sharda

E-Mail Website
Guest Editor
Biological and Agri-cultural Engineering, College of Engineering, Kansas State University, Manhattan, KS 66506, USA
Interests: agricultural machinery systems; sensor testing/development; mechatronic systems; hardware-in-the-loop simulations; high speed imaging; image analysis; unmanned vehicles, thermal infrared imaging

Special Issue Information

Dear Colleagues,

The relatively low altitude of most UAV-based remote sensing, together with rapid advances in related technologies, including sensors and data processing/management, results in exceptionally high levels of detail compared to other types of remote sensing. Plant phenology is an area of vegetation analysis in agriculture and ecosystem assessments that lends itself to UAV-based remote sensing because it often benefits from the particular advantages of this type of remote sensing.

The aim of this Special Issue is to present and highlight UAV applications in the area of plant phenology, including developments, challenges, practical methodologies, and theoretical advances, both in the acquisition and interpretation of data. Particular topics include, but are not limited to, the role of UAVs in:

  • The detection and quantification of plant life-cycle events;
  • Plant phenology metrics;
  • The role of phenology in plant identification and characterization;
  • Plant phenology patterns in space and time;
  • Phenology data processing and interpretation.

Articles may include original research or reviews.

Dr. Deon Van Der Merwe
Dr. Ajay Sharda
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. Drones 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

  • phenology
  • drone
  • UAS
  • UAV
  • remote sensing

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 20737 KiB  
Article
Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones
by Zongpeng Li, Zhen Chen, Qian Cheng, Shuaipeng Fei and Xinguo Zhou
Drones 2023, 7(8), 505; https://doi.org/10.3390/drones7080505 - 02 Aug 2023
Cited by 5 | Viewed by 1338
Abstract
Timely and accurate monitoring of winter wheat yields is beneficial for the macro-guidance of agricultural production and for making precise management decisions throughout the winter wheat reproductive period. The accuracy of crop yield prediction can be improved by combining unmanned aerial vehicle (UAV)-based [...] Read more.
Timely and accurate monitoring of winter wheat yields is beneficial for the macro-guidance of agricultural production and for making precise management decisions throughout the winter wheat reproductive period. The accuracy of crop yield prediction can be improved by combining unmanned aerial vehicle (UAV)-based multispectral data with deep learning algorithms. In this study, 16 yield-sensitive vegetation indices were constructed, and their correlations were analyzed based on UAV multispectral data of winter wheat at the heading, flowering, and filling stages. Seven input variable sets were obtained based on the combination of data from these three periods, and four generalized machine learning algorithms (Random Forest (RF), K-Nearest Neighbor (KNN), Bagging, and Gradient Boosting Regression (GBR)) and one deep learning algorithm (1D Convolutional Neural Network (1D-CNN)) were used to predict winter wheat yield. The results showed that the RF model had the best prediction performance among the generalised machine learning models. The CNN model achieved the best prediction accuracy based on all seven sets of input variables. Generalised machine learning models tended to underestimate or overestimate yields under different irrigation treatments, with good prediction performance for observed yields < 7.745 t·ha−1. The CNN model showed the best prediction performance based on most input variable groups across the range of observed yields. Most of the differences between observed and predicted values (Yi) for the CNN models were distributed between −0.1 t·ha−1 and 0.1 t·ha−1, and the model was relatively stable. Therefore, the CNN model is recommended in this study for yield prediction and as a reference for future precision agriculture research. Full article
(This article belongs to the Special Issue Advances of UAV Remote Sensing for Plant Phenology)
►▼ Show Figures

Figure 1

18 pages, 4765 KiB  
Article
Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data
by Yuxing Cui, Yishan Ji, Rong Liu, Weiyu Li, Yujiao Liu, Zehao Liu, Xuxiao Zong and Tao Yang
Drones 2023, 7(6), 378; https://doi.org/10.3390/drones7060378 - 05 Jun 2023
Cited by 3 | Viewed by 1870
Abstract
Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of [...] Read more.
Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of the faba bean yield, the dual-sensor (RGB and multi-spectral) data based on unmanned aerial vehicle (UAV) was collected and analyzed. For this, support vector machine (SVM), ridge regression (RR), partial least squares regression (PLS), and k-nearest neighbor (KNN) were used for yield estimation. Additionally, the fusing data from different growth periods based on UAV was first used for estimating faba bean yield to obtain better estimation accuracy. The results obtained are as follows: for a single-growth period, S2 (12 July 2019) had the best accuracy of the estimation model. For fusion data from the muti-growth period, S2 + S3 (12 August 2019) obtained the best estimation results. Furthermore, the coefficient of determination (R2) values for RF were higher than other machine learning algorithms, followed by PLS, and the estimation effects of fusion data from a dual-sensor were evidently better than from a single sensor. In a word, these results indicated that it was feasible to estimate the faba bean yield with high accuracy through data fusion based on dual-sensor data and different growth periods. Full article
(This article belongs to the Special Issue Advances of UAV Remote Sensing for Plant Phenology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop