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Special Issue "Advances in Agricultural Remote Sensing and Artificial Intelligence"

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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 (15 April 2023) | Viewed by 3733

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Special Issue Editors

Dr. Muhammad Moshiur Rahman

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Guest Editor

Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2350, Australia
Interests: remote sensing; agriculture; crop modeling; machine learning

Prof. Dr. Andrew Robson

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Guest Editor

Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2350, Australia
Interests: agriculture remote sensing; spatial analysis; plant biosecurity

Special Issue Information

Dear Colleagues,

Agriculture plays a critical role in the global economy. With the continuing expansion of the human population, the pressure on the agricultural system is increasing faster than ever. Precise and frequent monitoring of agricultural health and productivity is now critical for food security and economic sustainability. When remote sensing is used as a tool to monitor agriculture, the analytics must be reliable and accurate. Machine learning technology has provided highly accurate solutions to geospatial problems for many years. The availability of data at multiple scales over large geographical areas has great potential to enable interesting methodologies and knowledge development in the agricultural domain. On the other hand, advanced machine learning has emerged as a powerful approach for analyzing remote sensing data. There is a growing trend to develop such an approach to assist in the digital transformation of agriculture, such as land use monitoring, crop yield forecasting and optimization, crop diseases and pest management, etc. The aim of this Special Issue is to disseminate the latest research findings in the machine learning methods for crops monitoring using remote sensing. This includes but is not limited to crop yield prediction, cropland area change, crop phenology, agricultural drought and water stress, crop classification, weeds detection, disease detection, yield estimation, plants counting, etc. Papers are required to include a novelty, such as a new satellite sensor or data archive, a new approach to analysis, or a novel application to improve crop monitoring and evaluation. I look forward to receiving your contributions.

Dr. Muhammad Moshiur Rahman
Prof. Dr. Andrew Robson
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 2500 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

crop growth and health condition
crop phenology
crop yield forecasting
drought stress and irrigation
machine learning
image processing
crop classification
satellite remote sensing
crop monitoring and mapping

Published Papers (3 papers)

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Research

Open AccessArticle

Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field

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Remote Sens. 2023, 15(7), 1817; https://doi.org/10.3390/rs15071817 - 29 Mar 2023

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Abstract

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality. Full article

(This article belongs to the Special Issue Advances in Agricultural Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Vegetation Cover Dynamics in the High Atlas Mountains of Morocco

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Remote Sens. 2023, 15(5), 1366; https://doi.org/10.3390/rs15051366 - 28 Feb 2023

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Abstract

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method. Full article

(This article belongs to the Special Issue Advances in Agricultural Remote Sensing and Artificial Intelligence)

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Open AccessArticle

Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models

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Remote Sens. 2023, 15(2), 453; https://doi.org/10.3390/rs15020453 - 12 Jan 2023

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Abstract

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops. Full article

(This article belongs to the Special Issue Advances in Agricultural Remote Sensing and Artificial Intelligence)

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