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Remote Sensing in Precision Agriculture Production

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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: closed (31 December 2023) | Viewed by 4961

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Special Issue Editors

Prof. Dr. Abdul Rashid Mohamed Sharif

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Guest Editor

Department of Biological and Agricultural Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
Interests: remote sensing; precision agriculture; GIS; decision support systems; land management
Special Issues, Collections and Topics in MDPI journals

Dr. Sanaz Shafian

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Guest Editor

School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Interests: remote sensing; multispectral and hyperspectral imaging; thermal imaging

Dr. Redmond R. Shamshiri

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Guest Editor

Leibniz Institute for Agricultural Engineering and Bioeconomy, 14469 Potsdam, Germany
Interests: precision agriculture; wireless sensors; IoT; digital agriculture; system analysis and control; agricultural modeling and simulation; agricultural robotics; greenhouse automation
Special Issues, Collections and Topics in MDPI journals

Dr. Siva Kumar Balasundram

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Guest Editor

Department of Agriculture Technology, Universiti Putra Malaysia, Serdang, Malaysia
Interests: precision agriculture; digital agriculture

Special Issue Information

Dear Colleagues,

Improving agricultural productivity requires innovative solutions that offer a better yield and quality for indoor and outdoor farming. Farmers require precision technology to obtain and interpret data to better control crop growth, preventing losses caused by adverse weather conditions or infectious pests and thus facilitating return on investments. Every year, plant diseases contribute to significant losses in global harvest, costing an estimated USD 220 billion. The abundant use of chemicals such as bactericides, fungicides, and nematicides to control plant diseases is causing adverse effects on many agroecosystems. Since the early years of precision agriculture, remote sensing has been one of the main methods for monitoring crop growth and generating data for predictive and prescriptive analytics, such as yield prediction and optimization of fertilizer use. Remote sensing plays an important role in site-specific management by providing technology and methodologies to respond to various challenges in modern farming, including precision crop monitoring, resource management, early disease detection, yield estimation, plant phenotyping, estimation of the leaf area index, and many more. With the advances in electronic and information technologies, various sensing systems and algorithms have been developed for remote sensing in precision agriculture. Currently, there are three main remote sensing platforms for this purpose: close-range, such as ground-based or handheld sensors; middle-range, such as drone-based sensors or imaging devices mounted on autonomous unmanned aerial vehicles (UAVs); and far-range platforms, such as piloted airplanes or satellite-based sensors. More recently, different customized algorithms and techniques, including artificial intelligence and machine learning methods, have been proposed to process remote sensing data.

This Special Issue aims to bring together research reports that describe new, recently developed perspectives in remote sensing for precision agriculture applications, based on innovative tools emerging from basic and applied research. The objective of the Special Issue is to increase awareness of the implications of using remote sensing solutions, including but not limited to (1) data generation and comparison using smart sensors and satellite imagery, (2) data analysis methods for estimation of crop trains, (3) monitoring of crop growth for the detection of crop stress, estimation of yield for recommending harvesting dates, and improving crop quality, (4) precision management of resources from different remote sensing platforms for managing plant disease problems in a balanced and optimized manner, and (5) the use of artificial intelligence, Internet-of-Things, digital twin, and other new cutting-edge research topics in the area of remote sensing for precision agriculture.

Prof. Dr. Abdul Rashid Mohamed Sharif
Dr. Sanaz Shafian
Dr. Redmond R. Shamshiri
Dr. Siva Kumar Balasundram
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.

Keywords

precision farming
remote sensing
crop growth
yield monitoring
variable applicator

Published Papers (4 papers)

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Research

27 pages, 9769 KiB

Open AccessArticle

Comparing Remote and Proximal Sensing of Agrometeorological Parameters across Different Agricultural Regions in Croatia: A Case Study Using ERA5-Land, Agri4Cast, and In Situ Stations during the Period 2019–2021

by Dora Kreković, Vlatko Galić, Krunoslav Tržec, Ivana Podnar Žarko and Mario Kušek

Remote Sens. 2024, 16(4), 641; https://doi.org/10.3390/rs16040641 - 8 Feb 2024

Viewed by 658

Abstract

The paper evaluates the usability of remote satellite-based and proximal ground-based agrometeorological data sources for precision agriculture and crop production in Croatia. The compared agrometeorological datasets stem from the open-access data sources Copernicus CDS and the Agri4Cast portal, and commercial in situ agrometeorological stations (PinovaMeteo) which monitor environmental parameters relevant to the physiological state of crops. The study compares relevant parameters for 10 different locations in Croatia for three consecutive years (2019, 2020, and 2021) to investigate whether model-based data from ERA5-Land and Agri4Cast are well-correlated with ground measurements from independent in situ stations (PinovaMeteo) for specific agrometeorological parameters (air and soil temperature, and precipitation). Our results indicate the following: both the ERA5-Land and Agri4Cast datasets show mostly strong positive correlations with ground observations for air temperature, modest correlations for soil temperature, but modest or even low correlations for precipitation. Analysis of the residuals indicates higher overall residual values, especially in areas with complex topography and near large bodies of water or the sea, and deviations of residuals that may limit the usability of satellite- and model-based data for decision-making in agriculture. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture Production)

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23 pages, 10295 KiB

Open AccessArticle

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

by Milad Vahidi, Sanaz Shafian, Summer Thomas and Rory Maguire

Remote Sens. 2023, 15(20), 5014; https://doi.org/10.3390/rs15205014 - 18 Oct 2023

Cited by 1 | Viewed by 1036

Abstract

Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R² of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture Production)

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22 pages, 7919 KiB

Open AccessArticle

Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform

by Xiaoyang Lu, Wanjian Li, Junqi Xiao, Hongyun Zhu, Dacheng Yang, Jing Yang, Xidan Xu, Yubin Lan and Yali Zhang

Remote Sens. 2023, 15(14), 3523; https://doi.org/10.3390/rs15143523 - 12 Jul 2023

Cited by 1 | Viewed by 1354

Abstract

The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R²) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R² increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R² increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees’ growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture Production)

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Graphical abstract

21 pages, 10743 KiB

Open AccessArticle

Estimation of Anthocyanins in Whole-Fertility Maize Leaves Based on Ground-Based Hyperspectral Measurements

by Shiyu Jiang, Qingrui Chang, Xiaoping Wang, Zhikang Zheng, Yu Zhang and Qi Wang

Remote Sens. 2023, 15(10), 2571; https://doi.org/10.3390/rs15102571 - 15 May 2023

Cited by 2 | Viewed by 1168

Abstract

The estimation of anthocyanin (Anth) content is very important for observing the physiological state of plants under environmental stress. The objective of this study was to estimate the Anth of maize leaves at different growth stages based on remote sensing methods. In this study, the hyperspectral reflectance and the corresponding Anth of maize leaves were measured at the critical growth stages of nodulation, tasseling, lactation, and finishing of maize. First-order differential spectra (FD) were derived from the original spectra (OS). First, the spectral parameters highly correlated with Anth were selected. A total of two sensitive bands (R_λ), five classical vegetation indices (VI_S), and six optimized vegetation indices (VI_C) were selected from the original and first-order spectra. Then, univariate regression models for Anth estimation (Anth-UR models) and multivariate regression models for estimating anthocyanins (Anth-MR models) were constructed based on these parameters at different growth stages of maize. It was shown that the first-order spectral conversion could effectively improve the correlation between R_λ, VI_C, and Anth, and VI_C are usually more sensitive to Anth than VI_S. In addition, the overall performance of Anth-MR models was better than that of Anth-UR models. Among them, Anth-MR models with the combination of three types of spectral parameters (FD(R_λ) + OS_VI_C + FD_VI_C/VI_S) as inputs had the best overall performance. Moreover, different growth stages had an impact on the Anth estimation models, with tasseling and lactation stages showing better results. The best-performing Anth-MR models for these two growth stages were as follows. For the tasseling stage, the best model was the FD(R_λ) + OS_VI_C + VI_S-based SVM model, with an R² of 0.868, RMSE of 0.007, and RPD of 2.19. For the lactation stage, the best-performing model was the FD(R_λ) + OS_VI_C + FD_VI_C-based RF model, with an R² of 0.797, RMSE of 0.007, and RPD of 2.24. These results will provide a scientific basis for better monitoring of Anth using remote sensing hyperspectral techniques. Full article

(This article belongs to the Special Issue Remote Sensing in Precision Agriculture Production)

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