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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 May 2022) | Viewed by 41370

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Special Issue Editors

Dr. Jinya Su

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Guest Editor

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Interests: Intelligent autonomous systems; artifical intelligence; UAV inspection
Special Issues, Collections and Topics in MDPI journals

Dr. Cunjia Liu

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Guest Editor

Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: autonomous vehicles; flight control; situation awareness; bayesian filtering; remote sensing

Dr. Adrian Clark

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Guest Editor

School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3QS, UK
Interests: computer vision; remote sensing; UAVs; genetic programming; virtual and augmented reality

Special Issue Information

Dear Colleagues,

Agriculture, playing a crucial role in nearly all countries and regions (especially developing ones) and providing the main source of food, income and employment to their rural populations, is now facing severe challenges: 70% more food demand by 2050 to meet the needs of 9 Billion people (by UN Food and Agriculture Organization (FAO)) with an aging-population structure, decreasing natural resources, increasingly unpredictable climatic conditions, and the requirement of reducing environmental footprint. On the other hand, the advancement of sensing technology makes it possible to acquire data efficiently with unprecedented resolutions for timely non-destructive monitoring. Recently, Artificial Intelligence (AI) algorithms are able to analyze an unprecedented volume/ velocity/ variety (3V) of data. Moreover, robotics and automation technologies make precision and automated site-specific agriculture management possible in the near future.

In this Special Issue, we aim at disseminating the latest research findings in exploiting remote sensing technologies for smart agriculture, where remote sensing is able to make significant contributions to decision making and practical management interventions. It includes, but is not limited to, crop classification; moniotring diseases, pests, weeds, water stress and nutrient deficiencies; crop modelling; predicting yeild potential and its varaibility; and execution of management interventions.

Dr. Jinya Su
Dr. Cunjia Liu
Dr. Adrian Clark
Guest Editors

Manuscript Submission Information

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Keywords

remote sensing
satellite/UAV
machine learning
smart agriculture/farming
multi-source information fusion
crop stress monitoring
crop modelling
yield prediction

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Published Papers (7 papers)

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21 pages, 5625 KiB

Open AccessArticle

Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV

by Falv Wang, Mao Yang, Longfei Ma, Tong Zhang, Weilong Qin, Wei Li, Yinghua Zhang, Zhencai Sun, Zhimin Wang, Fei Li and Kang Yu

Remote Sens. 2022, 14(5), 1251; https://doi.org/10.3390/rs14051251 - 4 Mar 2022

Cited by 44 | Viewed by 4957

Abstract

One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R² values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R² = 0.74~0.92, RMSE = 0.95~2.87 t/ha, MAE = 0.75~2.18 t/ha, and RPD = 2.00~3.67) and validation dataset (R² = 0.50~0.75, RMSE = 1.56~2.57 t/ha, MAE = 1.44~2.05 t/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R² = 0.95~0.97, RMSE = 0.58~1.08 t/ha, MAE = 0.46~0.89 t/ha, and RPD = 3.95~6.35) and validation dataset (R² = 0.83~0.93, RMSE = 0.93~2.34 t/ha, MAE = 0.72~2.01 t/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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19 pages, 4355 KiB

Open AccessEditor’s ChoiceArticle

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

by Yucheng Wang, Jinya Su, Xiaojun Zhai, Fanlin Meng and Cunjia Liu

Remote Sens. 2022, 14(3), 782; https://doi.org/10.3390/rs14030782 - 8 Feb 2022

Cited by 15 | Viewed by 4891

Abstract

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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20 pages, 4178 KiB

Open AccessArticle

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

by Longyue Chen, Bo Xu, Chunjiang Zhao, Dandan Duan, Qiong Cao and Fan Wang

Remote Sens. 2021, 13(18), 3719; https://doi.org/10.3390/rs13183719 - 17 Sep 2021

Cited by 13 | Viewed by 3378

Abstract

The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R² and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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16 pages, 40826 KiB

Open AccessArticle

A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

by Jianqing Zhao, Xiaohu Zhang, Jiawei Yan, Xiaolei Qiu, Xia Yao, Yongchao Tian, Yan Zhu and Weixing Cao

Remote Sens. 2021, 13(16), 3095; https://doi.org/10.3390/rs13163095 - 5 Aug 2021

Cited by 159 | Viewed by 13296

Abstract

Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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19 pages, 5213 KiB

Open AccessArticle

Application of RGB Images Obtained by UAV in Coffee Farming

by Brenon Diennevam Souza Barbosa, Gabriel Araújo e Silva Ferraz, Luana Mendes dos Santos, Lucas Santos Santana, Diego Bedin Marin, Giuseppe Rossi and Leonardo Conti

Remote Sens. 2021, 13(12), 2397; https://doi.org/10.3390/rs13122397 - 19 Jun 2021

Cited by 23 | Viewed by 4805

Abstract

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R² values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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24 pages, 9895 KiB

Open AccessArticle

Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method

by Yue Yu, Yidan Bao, Jichun Wang, Hangjian Chu, Nan Zhao, Yong He and Yufei Liu

Remote Sens. 2021, 13(5), 901; https://doi.org/10.3390/rs13050901 - 27 Feb 2021

Cited by 52 | Viewed by 4045

Abstract

Visual navigation is developing rapidly and is of great significance to improve agricultural automation. The most important issue involved in visual navigation is extracting a guidance path from agricultural field images. Traditional image segmentation methods may fail to work in paddy field, for the colors of weed, duckweed, and eutrophic water surface are very similar to those of real rice seedings. To deal with these problems, a crop row segmentation and detection algorithm, designed for complex paddy fields, is proposed. Firstly, the original image is transformed to the grayscale image and then the treble-classification Otsu method classifies the pixels in the grayscale image into three clusters according to their gray values. Secondly, the binary image is divided into several horizontal strips, and feature points representing green plants are extracted. Lastly, the proposed double-dimensional adaptive clustering method, which can deal with gaps inside a single crop row and misleading points between real crop rows, is applied to obtain the clusters of real crop rows and the corresponding fitting line. Quantitative validation tests of efficiency and accuracy have proven that the combination of these two methods constitutes a new robust integrated solution, with attitude error and distance error within 0.02° and 10 pixels, respectively. The proposed method achieved better quantitative results than the detection method based on typical Otsu under various conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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14 pages, 5203 KiB

Open AccessTechnical Note

Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

by Wei Liu, Chaofei Sun, Yanan Zhao, Fei Xu, Yuli Song, Jieru Fan, Yilin Zhou and Xiangming Xu

Remote Sens. 2021, 13(18), 3753; https://doi.org/10.3390/rs13183753 - 18 Sep 2021

Cited by 12 | Viewed by 3483

Abstract

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr_{680–760 nm}) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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