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Remote Sensing
Special Issues
Machine Learning and High-Throughput Phenotyping in Precision Agriculture
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Machine Learning and High-Throughput Phenotyping in Precision Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1579

Special Issue Editors

Dr. Jorge Martínez-Guanter

E-Mail Website
Guest Editor
Department of Aerospace Engineering and Fluid Mechanics Agroforestry Engineering Area, University of Seville, Ctra. Sevilla-Utrera km.1, 41013 Seville, Spain
Interests: UAV imagery; ML for remote sensing; computer vision; crop protection strategies; AI-based weed mapping; satellite crop monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Akash Ashapure

E-Mail Website
Guest Editor
Terrestrial Information Systems Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: machine learning; multispectral hyperspectal image analysis; aquatic remote sensing; radiometric charactarization
Prof. Dr. Salah Er-Raki

E-Mail Website
Guest Editor
1. ProcEDE, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakech, B.P 549, Av.Abdelkarim Elkhattabi, Guéliz Marrakech, Morocco
2. Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
Interests: monitoring crop water requirements; crop water stress detection; multispectral remote sensing for agricultural applications; agronomic modeling; data assimilation; retrieval of biophysical crop variables from a multisensor remote sensing approach
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture employs diverse technical methods to gather information about the crop growth environment, enabling precise and accurate agricultural micro-management of the entire production process. A pivotal facet of precision agriculture, crop phenotype research delves into the structural attributes of crop individuals or collectives, alongside their functional traits encompassing physical, physiological, and biochemical properties. Consequently, high-throughput phenotypic monitoring can accelerate the entire breeding process and provide important data support for formulating management strategy in precision agriculture.

The evolution of crop phenotype measurement technology encompasses stages such as manual measurement, two-dimensional photogrammetry, and three-dimensional measurement. The ability of remote sensing technology to non-destructively gather surface data through diverse electromagnetic spectrum bands is progressively assuming a more prominent role in precision agriculture. The rapid advancement of spectral and imaging technologies has introduced sophisticated sensors such as multi/hyperspectral, chlorophyll fluorescence, and lidar, offering efficient avenues for procuring crop phenotype data. Deploying a variety of sensors across distinct remote sensing platforms (spaceborne, airborne, and ground-based) facilitates swift acquisition of phenotypic data, enabling comprehensive multi-scale, multi-temporal monitoring of growth dynamics throughout the crop's developmental phase.

Moreover, machine learning has made breakthroughs in the field of remote sensing image processing. In applications such as object recognition and segmentation, image processing based on machine learning performs better than traditional methods. This Special Issue aims to combine machine learning technology and high-throughput phenotypic data to obtain the growth information of crops, indirectly predict the crop yield, monitor crop growth and biotic/abiotic stress responses, and thus realize agricultural precision, digitalization, informatization and intelligent management.

Dr. Jorge Martínez-Guanter
Dr. Akash Ashapure
Prof. Dr. Salah Er-Raki
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.

Published Papers (2 papers)

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Research

20 pages, 5205 KiB  
Article
Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data
by Xiaoyue Du, Liyuan Zheng, Jiangpeng Zhu and Yong He
Remote Sens. 2024, 16(7), 1138; https://doi.org/10.3390/rs16071138 - 25 Mar 2024
Viewed by 586
Abstract
The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen [...] Read more.
The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model’s accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field. Full article
(This article belongs to the Special Issue Machine Learning and High-Throughput Phenotyping in Precision Agriculture)
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31 pages, 30389 KiB  
Article
Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pléiades Satellite Data in Field Breeding Experiments
by Dessislava Ganeva, Eugenia Roumenina, Petar Dimitrov, Alexander Gikov, Violeta Bozhanova, Rangel Dragov, Georgi Jelev and Krasimira Taneva
Remote Sens. 2024, 16(3), 559; https://doi.org/10.3390/rs16030559 - 31 Jan 2024
Viewed by 650
Abstract
Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source [...] Read more.
Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R2: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R2 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes. Full article
(This article belongs to the Special Issue Machine Learning and High-Throughput Phenotyping in Precision Agriculture)
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