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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 9630

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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

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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 (5 papers)

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Research

25 pages, 18902 KiB

Open AccessArticle

A Spatial–Temporal Bayesian Deep Image Prior Model for Moderate Resolution Imaging Spectroradiometer Temporal Mixture Analysis

by Yuxian Wang, Rongming Zhuo, Linlin Xu and Yuan Fang

Remote Sens. 2023, 15(15), 3782; https://doi.org/10.3390/rs15153782 - 29 Jul 2023

Cited by 1 | Viewed by 983

Abstract

Time-series remote sensing images are important in agricultural monitoring and investigation. However, most time-series data with high temporal resolution have the problem of insufficient spatial resolution which cannot meet the requirement of precision agriculture. The unmixing technique can obtain the object abundances with richer spatial information from the coarse-resolution images. Although the unmixing technique is widely used in hyperspectral data, it is insufficiently researched for time-series data. Temporal unmixing extends spectral unmixing to the time domain from the spectral domain, and describes the temporal characteristics rather than the spectral characteristics of different ground objects. Deep learning (DL) techniques have achieved promising performance for the unmixing problem in recent years, but there are still few studies on temporal mixture analysis (TMA), especially in the application of crop phenological monitoring. This paper presents a novel spatial–temporal deep image prior method based on a Bayesian framework (ST-Bdip), which innovatively combines the knowledge-driven TMA model and the DL-driven model. The normalized difference vegetation index (NDVI) time series of moderate resolution imaging spectroradiometer (MODIS) data is used as the object for TMA, while the extracted seasonal crop signatures and the fractional coverages are perceived as the temporal endmembers (tEMs) and corresponding abundances. The proposed ST-Bdip method mainly includes the following contributions. First, a deep image prior model based on U-Net architecture is designed to efficiently learn the spatial context information, which enhances the representation of abundance modeling compared to the traditional non-negative least squares algorithm. Second, The TMA model is incorporated into the U-Net training process to exploit the knowledge in the forward temporal model effectively. Third, the temporal noise heterogeneity in time-series images is considered in the model optimization process. Specifically, the anisotropic covariance matrix of observations from different time dimensions is modeled as a multivariate Gaussian distribution and incorporated into the calculation of the loss function. Fourth, the "purified means" approach is used to further optimize crop tEMs and the corresponding abundances. Finally, the expectation–maximization (EM) algorithm is designed to solve the maximum a posterior (MAP) problem of the model in the Bayesian framework. Experimental results on three synthetic datasets with different noise levels and two real MODIS datasets demonstrate the superiority of the proposed approach in comparison with seven traditional and advanced unmixing algorithms. Full article

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

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23 pages, 9722 KiB

Open AccessArticle

Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

by Yuying Liu, Xuecong Pu and Zhangquan Shen

Remote Sens. 2023, 15(13), 3384; https://doi.org/10.3390/rs15133384 - 03 Jul 2023

Cited by 1 | Viewed by 1110

Abstract

Large-scale crop mapping is of fundamental importance to tackle food security problems. SAR remote sensing has lately received great attention for crop type mapping due to its stability in the revisit cycle and is not hindered by cloud cover. However, most SAR image-classification studies focused on the application of backscattering characteristics with machine learning models, while few investigated the potential of the polarization decomposition and deep-learning models. This study investigated whether the radar polarization information mined by polarization decomposition, the patch strategy and the approaches for combining recurrent and convolutional neural networks (Conv2d + LSTM and ConvLSTM2d) could effectively improve the accuracy of crop type mapping. Sentinel-1 SLC and GRD products in 2020 were collected as data sources to extract VH, VV, VH/VV, VV + VH, Entropy, Anisotropy, and Alpha 7-dimensional features for classification. The results showed that the three-dimensional Convolutional Neural Network (Conv3d) was the best classifier with an accuracy and kappa up to 88.9% and 0.875, respectively, and the ConvLSTM2d and Conv2d + LSTM achieved the second and third position. Compared to backscatter coefficients, the polarization decomposition features could provide additional phase information for classification in the time dimension. The optimal patch size was 17, and the patch-based Conv3d outperformed the pixel-based Conv1d by 11.3% in accuracy and 0.128 in kappa. This study demonstrated the value of applying polarization decomposition features to deep-learning models and provided a strong technical support to efficient large-scale crop mapping. Full article

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

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15 pages, 6575 KiB

Open AccessArticle

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

by Monica F. Danilevicz, Roberto Lujan Rocha, Jacqueline Batley, Philipp E. Bayer, Mohammed Bennamoun, David Edwards and Michael B. Ashworth

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

Cited by 1 | Viewed by 1577

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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16 pages, 4049 KiB

Open AccessArticle

Vegetation Cover Dynamics in the High Atlas Mountains of Morocco

by Thanh Thi Nguyen, Nacer Aderdour, Hassan Rhinane and Andreas Buerkert

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

Cited by 3 | Viewed by 2203

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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20 pages, 2935 KiB

Open AccessArticle

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

by Shiyuan Liu, Bin Zhang, Weiguang Yang, Tingting Chen, Hui Zhang, Yongda Lin, Jiangtao Tan, Xi Li, Yu Gao, Suzhe Yao, Yubin Lan and Lei Zhang

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

Cited by 5 | Viewed by 2726

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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