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Remote Sensing
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Smart Agriculture Based on Remote Sensing and Artificial Intelligence
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Smart Agriculture Based on Remote Sensing and Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 17 May 2024 | Viewed by 2695

Special Issue Editors

Dr. Chongya Jiang

E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop growth monitoring
Dr. Zhan Li

E-Mail Website
Guest Editor
BASF Digital Farming GmbH, Im Zollhafen 24, 50678 Köln, Germany
Interests: crop mapping
Dr. Zhou Zhang

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Wisconsin-Madison, 230 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706, USA
Interests: crop yield prediction and phenotyping
Special Issues, Collections and Topics in MDPI journals
Dr. Wang Zhou

E-Mail Website
Guest Editor
College of Agriculture, Sun Yat-sen University, Guangzhou 510275, China
Interests: crop modeling

Special Issue Information

Dear Colleagues,

Smart agriculture leverages remote sensing to generate high-quality, timely, and actionable insights into farmlands, leading to improved crop yield, efficient use of resources, and sustainable practices. Remote sensing data for smart agriculture have the characteristics of multiple varieties, large volumes, and diverse application requirements. It is therefore necessary to adopt innovative technologies to process and manage remote sensing data and to extract and understand agricultural information in order to provide better basis for smart agriculture decision making. The use of artificial intelligence methods, in particular, deep learning methods, has become one of the most powerful tools in recent years. The unified, reliable, and easy implementation characteristics of artificial intelligence provide a new approach to exploitation and utilization of remote sensing data for smarter agriculture.

In this context, this Special Issue aims to explore the recent advances in remote sensing technologies and applications and artificial intelligence in the agriculture domain, with a focus on crops. Papers of a theoretical, technical, and applicative nature are welcome. Data sources could be from remote sensors on various platforms including ground-based, proximal, drone, aircraft, and satellites. Topics of interest include but are not limited to:

  • AI for crop type and soil mapping;
  • AI for crop growth monitoring;
  • AI for crop growth modeling;
  • AI for crop yield prediction;
  • AI for stress/weed/disease/insect detection;
  • AI for decision making of management practices;
  • AI for tractor/robot navigation/operations;
  • AI for phenotyping.

We look forward to seeing your contributions to this Special Issue.

Dr. Chongya Jiang
Dr. Zhan Li
Dr. Zhou Zhang
Dr. Wang Zhou
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

  • smart agriculture
  • remote sensing
  • artificial intelligence

Published Papers (2 papers)

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19 pages, 11073 KiB  
Article
Optimal Integration of Optical and SAR Data for Improving Alfalfa Yield and Quality Traits Prediction: New Insights into Satellite-Based Forage Crop Monitoring
by Jiang Chen, Tong Yu, Jerome H. Cherney and Zhou Zhang
Remote Sens. 2024, 16(5), 734; https://doi.org/10.3390/rs16050734 - 20 Feb 2024
Viewed by 731
Abstract
Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, [...] Read more.
Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R2 = 0.846 and RMSE = 0.0354 kg/m2; CP: R2 = 0.636 and RMSE = 1.57%; ADF: R2 = 0.559 and RMSE = 1.926%; NDF: R2 = 0.58 and RMSE = 2.097%; NDFD: R2 = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites. Full article
(This article belongs to the Special Issue Smart Agriculture Based on Remote Sensing and Artificial Intelligence)
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22 pages, 7024 KiB  
Article
Contrastive-Learning-Based Time-Series Feature Representation for Parcel-Based Crop Mapping Using Incomplete Sentinel-2 Image Sequences
by Ya’nan Zhou, Yan Wang, Na’na Yan, Li Feng, Yuehong Chen, Tianjun Wu, Jianwei Gao, Xiwang Zhang and Weiwei Zhu
Remote Sens. 2023, 15(20), 5009; https://doi.org/10.3390/rs15205009 - 18 Oct 2023
Viewed by 1143
Abstract
Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image [...] Read more.
Parcel-based crop classification using multi-temporal satellite optical images plays a vital role in precision agriculture. However, optical image sequences may be incomplete due to the occlusion of clouds and shadows. Thus, exploring inherent time-series features to identify crop types from incomplete optical image sequences is a significant challenge. This study developed a contrastive-learning-based framework for time-series feature representation to improve crop classification using incomplete Sentinel-2 image sequences. Central to this method was the combined use of inherent time-series feature representation and machine-learning-based classifications. First, preprocessed multi-temporal Sentinel-2 satellite images were overlaid onto precise farmland parcel maps to generate raw time-series spectral features (with missing values) for each parcel. Second, an enhanced contrastive learning model was established to map the raw time-series spectral features to their inherent feature representation (without missing values). Thirdly, eXtreme Gradient-Boosting-based and Long Short-Term Memory-based classifiers were applied to feature representation to produce crop classification maps. The proposed method is further discussed and validated through parcel-based time-series crop classifications in two study areas (one in Dijon of France and the other in Zhaosu of China) with multi-temporal Sentinel-2 images in comparison to the existing methods. The classification results, demonstrating significant improvements greater than 3% in overall accuracy and 0.04 in F1 scores over comparison methods, indicate the effectiveness of the proposed contrastive-learning-based time-series feature representation for parcel-based crop classification utilizing incomplete Sentinel-2 image sequences. Full article
(This article belongs to the Special Issue Smart Agriculture Based on Remote Sensing and Artificial Intelligence)
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