Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Precision Remote Sensing and Information Detection in Agriculture
share announcement

Precision Remote Sensing and Information Detection in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (10 May 2024) | Viewed by 4213

Special Issue Editors

Dr. Tongxi Hu

E-Mail Website
Guest Editor
Institute for Sustainability Energy and Environment, University of Illinois, Urbana, IL 61801, USA
Interests: agroecosystem modeling; remote sensing; machine learning; digital agriculture
Prof. Dr. Kebiao Mao

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: artificial intelligence; deep learning; retrieval paradigm; soil moisture retrieval; land surface temperature retrieval; water vapor content retrieval; near surface temperature retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Kaiguang Zhao

E-Mail Website
Guest Editor
Ohio Agricultural Research and Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH 44691, USA
Interests: carbon monitoring; ecosystem structure and functioning; land dynamics; lidar; ecological modeling; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is facing daunting challenges imposed by the increasing global population, natural resource scarcity, and climate change. Yet, there are unprecedented opportunities for the future, including the remarkable emergence of innovations in technological advances, such as precision remote sensing, which will help optimize agricultural management and thus improve agricultural sustainability.

Pivotal technologies for data collection, including airborne sensing, Unmanned Aerial Vehicles (UAV), real-time kinematics (RTK), and global positioning systems (GPS), are being used to monitor yields, weeds, chemical (herbicides, insecticides, and fertilizers) use etc. The collected data can influence farmer decisions with respect to seeding, fertilizer and chemical applications, irrigation scheduling, and other farm input use, which could lead to economic savings on farms and reduce the impact on the environment.

This Special Issue aims to cover a wide range of data collection approaches, such as UAVs (also known as drones), to monitor croplands and thus optimize management practices so that outcomes are robust and resource-efficient.

Dr. Tongxi Hu
Prof. Dr. Kebiao Mao
Dr. Kaiguang Zhao
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. Agriculture 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 farming
  • UAV
  • IoT
  • smart agriculture
  • digital agriculture
  • remote sensing
  • data fusion

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 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

21 pages, 8707 KiB  
Article
Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing
by Yihan Yao, Jibo Yue, Yang Liu, Hao Yang, Haikuan Feng, Jianing Shen, Jingyu Hu and Qian Liu
Agriculture 2024, 14(7), 1175; https://doi.org/10.3390/agriculture14071175 - 18 Jul 2024
Cited by 1 | Viewed by 1519
Abstract
Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, [...] Read more.
Maize, an important cereal crop and crucial industrial material, is widely used in various fields, including food, feed, and industry. Maize is also a highly adaptable crop, capable of thriving under various climatic and soil conditions. Against the backdrop of intensified climate change, studying the classification of maize growth stages can aid in adjusting planting strategies to enhance yield and quality. Accurate classification of the growth stages of maize breeding materials is important for enhancing yield and quality in breeding endeavors. Traditional remote sensing-based crop growth stage classifications mainly rely on time series vegetation index (VI) analyses; however, VIs are prone to saturation under high-coverage conditions. Maize phenotypic traits at different growth stages may improve the accuracy of crop growth stage classifications. Therefore, we developed a method for classifying maize growth stages during the vegetative growth phase by combining maize phenotypic traits with different classification algorithms. First, we tested various VIs, texture features (TFs), and combinations of VI and TF as input features to estimate the leaf chlorophyll content (LCC), leaf area index (LAI), and fractional vegetation cover (FVC). We determined the optimal feature inputs and estimation methods and completed crop height (CH) extraction. Then, we tested different combinations of maize phenotypic traits as input variables to determine their accuracy in classifying growth stages and to identify the optimal combination and classification method. Finally, we compared the proposed method with traditional growth stage classification methods based on remote sensing VIs and machine learning models. The results indicate that (1) when the VI+TFs are used as input features, random forest regression (RFR) shows a good estimation performance for the LCC (R2: 0.920, RMSE: 3.655 SPAD units, MAE: 2.698 SPAD units), Gaussian process regression (GPR) performs well for the LAI (R2: 0.621, RMSE: 0.494, MAE: 0.397), and linear regression (LR) exhibits a good estimation performance for the FVC (R2: 0.777, RMSE: 0.051, MAE: 0.040); (2) when using the maize LCC, LAI, FVC, and CH phenotypic traits to classify maize growth stages, the random forest (RF) classification method achieved the highest accuracy (accuracy: 0.951, precision: 0.951, recall: 0.951, F1: 0.951); and (3) the effectiveness of the growth stage classification based on maize phenotypic traits outperforms that of traditional remote sensing-based crop growth stage classifications. Full article
(This article belongs to the Special Issue Precision Remote Sensing and Information Detection in Agriculture)
►▼ Show Figures

Figure 1

20 pages, 20723 KiB  
Article
Advancing Cassava Age Estimation in Precision Agriculture: Strategic Application of the BRAH Algorithm
by Sornkitja Boonprong, Tunlawit Satapanajaru and Ngamlamai Piolueang
Agriculture 2024, 14(7), 1075; https://doi.org/10.3390/agriculture14071075 - 4 Jul 2024
Viewed by 850
Abstract
Cassava crop age estimation is crucial for optimizing irrigation, fertilization, and pest management, which are key components of precision agriculture. Accurate knowledge of crop age allows for effective resource application, minimizing environmental impact and enhancing yield predictions. The Bare Land Referenced Algorithm from [...] Read more.
Cassava crop age estimation is crucial for optimizing irrigation, fertilization, and pest management, which are key components of precision agriculture. Accurate knowledge of crop age allows for effective resource application, minimizing environmental impact and enhancing yield predictions. The Bare Land Referenced Algorithm from Hyper-Temporal Data (BRAH) is used for bare land classification and cassava crop age estimation, but it traditionally requires manual NDVI thresholding, which is challenging with large datasets. To address this limitation, we propose automating the thresholding process using Otsu’s method and enhancing the image contrast with histogram equalization. This study applies these enhancements to the BRAH algorithm for bare land classification and cassava crop age estimation in Ratchaburi, Thailand, utilizing a dataset of 604 Landsat satellite images from 1987 to 2024. Our research demonstrates the accuracy and practicality of the BRAH algorithm, with Otsu’s method providing 94% accuracy in detecting the bare land validation locations with an average deviation of 8.78 days between the acquisition date and the validated date. This approach facilitates precise agricultural planning and management, promoting sustainable farming practices and supporting several Sustainable Development Goals (SDGs). Full article
(This article belongs to the Special Issue Precision Remote Sensing and Information Detection in Agriculture)
►▼ Show Figures

Graphical abstract

20 pages, 5864 KiB  
Article
UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers
by Rafael Alexandre Pena Barata, Gabriel Araújo e Silva Ferraz, Nicole Lopes Bento, Lucas Santos Santana, Diego Bedin Marin, Drucylla Guerra Mattos, Felipe Schwerz, Giuseppe Rossi, Leonardo Conti and Gianluca Bambi
Agriculture 2024, 14(3), 356; https://doi.org/10.3390/agriculture14030356 - 23 Feb 2024
Cited by 2 | Viewed by 1311
Abstract
Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings [...] Read more.
Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach. Full article
(This article belongs to the Special Issue Precision Remote Sensing and Information Detection in Agriculture)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop