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Drones
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Yield Prediction Using Data from Unmanned Aerial Vehicles
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Yield Prediction Using Data from Unmanned Aerial Vehicles

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 (15 March 2024) | Viewed by 11911

Special Issue Editors

Prof. Dr. Uwe Knauer

E-Mail Website
Guest Editor
Professorship for Digital Technologies in Plant Production, Anhalt University of Applied Sciences, Strenzfelder Allee 28, D-06406 Bernburg, Germany
Interests: hyperspectral imaging; remote sensing; machine learning; pattern recognition; programming C; programming C++; shell programming; java programming; data analysis; programming in MATLAB; signal processing; image processing; databases
Dr. András Jung

E-Mail Website
Guest Editor
Institute of Cartography and Geoinformatics, Eötvös Loránd University, 1117 Budapest, Hungary
Interests: hyperspectral remote sensing; field spectroscopy; mobile and snapshot imaging spectroscopy; precision farming; agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of unmanned aerial vehicles (UAVs) in agriculture is only a few years old, yet the potential of this remote sensing technology for the implementation of precision agriculture can already be seen. This technology sets new standards for the spatiotemporal monitoring of fields and is also highly variable in terms of carrier systems and camera sensor technology in order to adapt to the broad range of applications.

With regard to yield, phenotypic dissection by repeated UAV campaigns could provide information on how final yield is formed during the growth phase and how direct and indirect morphological, physiological, and environmental elements influence yield. Moreover, pre-harvest yield estimates could be used to determine input factors such as nutrients, pesticides, and water in order to optimize yield potential. The need for robust multi-temporal modeling approaches across years as well as the challenges posed by highly variable imaging conditions impose limits on yield estimation using UAV data.

This Special Issue aims to present state-of-the-art methods and results of yield estimation using UAVs as platforms to collect remote sensing data in agriculture. The type of sensors used may include, but is not limited to, high resolution RGB cameras, multispectral and hyperspectral cameras, LiDAR sensors, and TIR sensors. A fusion of different UAV sensors in combination with other ground-based or satellite-based sensor systems used for modeling the yield estimation is conceivable and desirable. Different modeling approaches and comparisons between, for example, multivariate regression, decision trees, support vector machines, or artificial neural networks are also encouraged. There is no preference for the agricultural crop, but UAV data from multi-year field trials and time series datasets within the vegetation period are preferred. Additionally, contributions by validation experiments for UAV data in crop production are highly encouraged.

In this Special Issue, original research articles focusing on methodological and/or best practice and reviews are welcome. We look forward to receiving your contributions.

Prof. Dr. Uwe Knauer
Dr. András Jung
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

  • UAV application
  • Yield estimation/ prediction
  • Deep learning (DL)
  • Multi-temporal
  • Sensor-fusion
  • Image processing
  • Hyperspectral
  • Multispectral
  • Crop production
  • Remote Sensing

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Published Papers (3 papers)

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Research

18 pages, 6727 KiB  
Article
Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru
by David Saravia, Lamberto Valqui-Valqui, Wilian Salazar, Javier Quille-Mamani, Elgar Barboza, Rossana Porras-Jorge, Pedro Injante and Carlos I. Arbizu
Drones 2023, 7(5), 325; https://doi.org/10.3390/drones7050325 - 19 May 2023
Cited by 8 | Viewed by 3623
Abstract
In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, [...] Read more.
In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem. Full article
(This article belongs to the Special Issue Yield Prediction Using Data from Unmanned Aerial Vehicles)
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17 pages, 2492 KiB  
Article
Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning
by Xiyong Zhao, Yanzhou Li, Yongli Chen, Xi Qiao and Wanqiang Qian
Drones 2023, 7(1), 2; https://doi.org/10.3390/drones7010002 - 21 Dec 2022
Cited by 13 | Viewed by 3838
Abstract
Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted [...] Read more.
Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monitoring and data collection were conducted during a cyanobacterial bloom period. Finally, 155 sets of valid data were obtained. The imaging data were obtained using a multispectral UAV, water samples were collected from the lake, and the chl-a concentration was obtained in the laboratory. Then, the images were preprocessed to extract the information from different wavebands. The univariate regression of each vegetation index and the regression using band information were used for comparative analysis. Four machine learning algorithms were used to build the model: support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and convolutional neural network (CNN). The results showed that the effect of estimating the chl-a concentration via multiple regression using vegetation indices was generally better than that via regression with a single vegetation index and original band information. The CNN model obtained the best results (R2 = 0.7917, RMSE = 8.7660, and MRE = 0.2461). This study showed the reliability of using multiple regression based on vegetation indices to estimate the chl-a of surface water. Full article
(This article belongs to the Special Issue Yield Prediction Using Data from Unmanned Aerial Vehicles)
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17 pages, 7341 KiB  
Article
Assessing the Temporal and Spatial Variability of Coffee Plantation Using RPA-Based RGB Imaging
by Maurício Martello, José Paulo Molin, Graciele Angnes and Matheus Gabriel Acorsi
Drones 2022, 6(10), 267; https://doi.org/10.3390/drones6100267 - 21 Sep 2022
Cited by 2 | Viewed by 2074
Abstract
The biophysical parameters of coffee plants can provide important information to guide crop management. An alternative to traditional methods of sparse hand measurements to obtain this type of information can be the 3D modeling of the coffee canopy using aerial images from RGB [...] Read more.
The biophysical parameters of coffee plants can provide important information to guide crop management. An alternative to traditional methods of sparse hand measurements to obtain this type of information can be the 3D modeling of the coffee canopy using aerial images from RGB cameras attached to remotely piloted aircraft (RPA). This study aimed to explore the use of RGB aerial images to obtain 3D information of coffee crops, deriving plant height and volume information together with yield data during three growing seasons in a commercial production area of 10.24 ha, Minas Gerais state, Brazil. Seven data acquisition campaigns were conducted during the years 2019, 2020 and 2021. The flights were made at 70 m above ground level, with lateral and longitudinal overlaps of 75% and 80%, respectively. The images were processed, obtaining canopy surface models (CSMs) derived into plant height and volume data for each campaign. The results showed that it is possible to extract the plant height of coffee plants with an R2 of 0.86 and an RMSE of 0.4 m. It was possible to monitor the temporal variability of coffee plant height and volume based on aerial images and correlate this information with yield data. The results of the modeling analysis demonstrated the possibility of using these variables to help understand the spatial variability of coffee yield within the field. Full article
(This article belongs to the Special Issue Yield Prediction Using Data from Unmanned Aerial Vehicles)
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