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Remote Sensing in Smart Agriculture

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 25925

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Dr. Xingang Xu

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Guest Editor

Ministry of Agriculture of the People's Republic of China, Beijing, China
Interests: canopy cover estimation based on remote sensing

Special Issue Information

Dear colleagues，

Smart agriculture is a new trend of agricultural development explored as a dividend of the modern information technology revolution. It is an advanced stage of agricultural development integrating intensive production, intelligent remote control, precision management, data analysis, and field operation. Smart agriculture plays an important role in improving resource utilization and land productivity, ensuring food security, achieving carbon neutrality, and promoting the Sustainable Development Goals of the United Nations.

Smart agriculture is the "ecological integration" and "gene recombination" of modern information technology and the whole industrial chain of agricultural production, operation, management, and service. Among them, satellite, UAV, and ground remote sensing technology play an important role in information support for the production and management of field planting. Remote sensing can provide growth, nutrient, pest, disease, and phenotype information for crops, fruit trees, tea trees, and others. It is useful for fertilizing, irrigating , pesticide spreading, and crop breeding to make accurate decisions.

In recent years, smart agriculture has become a hot spot in the agricultural development of various countries. Remote sensing technology provides high spatial and temporal monitoring information for the development of smart agriculture. Based on the above background, we wish to publish a Special Issue on the topic of field crops for international peers to discuss the latest remote sensing technology in the field of smart agriculture and jointly promote better application of remote sensing technology in agriculture.

Dr. Xingang Xu
Guest Editor

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Keywords

crop
nitrogen
water stress
pest and disease
growth prediction
yield
vertical structure
phenotyping
deep learning
machine learning
hyperspectral
LiDAR
UAV
smart agriculture.

Published Papers (11 papers)

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19 pages, 3334 KiB

Open AccessArticle

UAV-Based Remote Sensing to Evaluate Daily Water Demand Characteristics of Maize: A Case Study from Yuci Lifang Organic Dry Farming Experimental Base in Jinzhong City, China

by Yaoyu Li, Tengteng Qu, Yuzhi Wang, Qixin Zhao, Shujie Jia, Zhe Yin, Zhaodong Guo, Guofang Wang, Fuzhong Li and Wuping Zhang

Agronomy 2024, 14(4), 729; https://doi.org/10.3390/agronomy14040729 - 01 Apr 2024

Viewed by 527

Abstract

Soil moisture critically influences crop growth, especially in dryland environments. Precise agricultural management requires real-time monitoring of stratified soil moisture and assessment of crops’ daily water needs. We aim to provide low-cost, high-throughput information acquisition services for dryland regions with underdeveloped infrastructure and offer scientific support for sustainable water resource management. We used UAVs (Unmanned Aerial Vehicles) with multi-spectral sensors for routine maize monitoring, capturing leaf reflectance. Constructing vegetation indices, we quantified the relationship between leaf water content and surface soil moisture, using the Biswas model to predict deep soil moisture distribution. We used UVAs to monitor crop height and calculated the daily water demand for the entire growth period of corn using the Penman Montes equation. We found an R² of 0.8603, RMSE of 2.455%, and MAE of 2.099% between NDVI and canopy leaf water content. A strong linear correlation (R² = 0.7510) between canopy leaf water content and soil moisture was observed in the top 20 cm of soil. Deep soil moisture inversion from the top 20 cm soil moisture showed an R² of 0.9984, with RE mostly under 10%, but exceeding 20% at 120 cm depth. We also constructed a maize height model aligning with a sigmoidal growth curve (R² = 0.9724). Maize’s daily water demand varied from 0.7121 to 9.4263 mm, exhibiting a downward-opening parabolic trend. Integration of rainfall and soil water data allowed for dynamic irrigation adjustments, mitigating drought and water stress effects on crops. We highlighted UAV multi-spectral imaging’s effectiveness in monitoring crop water needs, facilitating quick daily water requirement estimations. Our work offers a scientific foundation for managing maize cultivation in drylands, enhancing water resource utilization. Full article

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

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18 pages, 6310 KiB

Open AccessArticle

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

by Yuanyuan Ma, Chunxia Qiu, Jie Zhang, Di Pan, Chunkai Zheng, Heguang Sun, Haikuan Feng and Xiaoyu Song

Agronomy 2023, 13(12), 3071; https://doi.org/10.3390/agronomy13123071 - 15 Dec 2023

Viewed by 714

Abstract

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R² values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm². Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm², respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health. Full article

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

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16 pages, 18278 KiB

Open AccessArticle

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

by Dorijan Radočaj, Mladen Jurišić and Vjekoslav Tadić

Agronomy 2023, 13(10), 2516; https://doi.org/10.3390/agronomy13102516 - 29 Sep 2023

Cited by 2 | Viewed by 649

Abstract

This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R² of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC. Full article

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

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18 pages, 11629 KiB

Open AccessArticle

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

by Daiwei Zhang, Chunyang Ying, Lei Wu, Zhongqiu Meng, Xiaofei Wang and Youhua Ma

Agronomy 2023, 13(9), 2350; https://doi.org/10.3390/agronomy13092350 - 10 Sep 2023

Cited by 1 | Viewed by 1343

Abstract

Timely and accurate extraction of crop planting structure information is of great importance for food security and sustainable agricultural development. However, long time series data with high spatial resolution have a much larger data volume, which seriously limits the quality and efficiency of the application of remote sensing to agriculture in complex crop rotation areas. To address this problem, this paper takes Lujiang County, a typical complex crop rotation region in the middle and lower reaches of the Yangtze River in China as an example, and proposes utilizing the Google Earth Engine (GEE) platform to extract the Normalized Difference Vegetation Index (NDVI), Normalized Difference Yellowness Index (NDYI) and Vertical-Horizontal Polarization (VH) time series sets of the whole planting year, and combining the Simple Non-Iterative Clustering (SNIC) multi-scale segmentation with the Support Vector Machine (SVM) and Random Forest (RF) algorithms to realize the fast and high-quality planting information of the main crop rotation patterns in the complex rotation region. The results show that by combining time series and object-oriented methods, SVM leads to better improvement than RF, with its overall accuracy and Kappa coefficient increasing by 4.44% and 0.0612, respectively, but RF is more suitable for extracting the planting structure in complex crop rotation areas. The RF algorithm combined with time series object-oriented extraction (OB + T + RF) achieved the highest accuracy, with an overall accuracy and Kappa coefficient of 98.93% and 0.9854, respectively. When compared to the pixel-oriented approach combined with the Support Vector Machine algorithm based on multi-temporal data (PB + M + SVM), the proposed method effectively reduces the presence of salt-and-pepper noise in the results, resulting in an improvement of 6.14% in overall accuracy and 0.0846 in Kappa coefficient. The research results can provide a new idea and a reliable reference method for obtaining crop planting structure information efficiently and accurately in complex crop rotation areas. Full article

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

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17 pages, 8614 KiB

Open AccessArticle

Estimation of Winter Wheat Canopy Chlorophyll Content Based on Canopy Spectral Transformation and Machine Learning Method

by Xiaokai Chen, Fenling Li, Botai Shi, Kai Fan, Zhenfa Li and Qingrui Chang

Agronomy 2023, 13(3), 783; https://doi.org/10.3390/agronomy13030783 - 08 Mar 2023

Cited by 5 | Viewed by 1778

Abstract

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (R_v²) was 0.88, the RMSE of the validation set (RMSE_v) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area. Full article

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

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18 pages, 3361 KiB

Open AccessArticle

Hyperspectral Estimation of Winter Wheat Leaf Water Content Based on Fractional Order Differentiation and Continuous Wavelet Transform

by Changchun Li, Zhen Xiao, Yanghua Liu, Xiaopeng Meng, Xinyan Li, Xin Wang, Yafeng Li, Chenyi Zhao, Lipeng Ren, Chen Yang and Yinghua Jiao

Agronomy 2023, 13(1), 56; https://doi.org/10.3390/agronomy13010056 - 23 Dec 2022

Cited by 1 | Viewed by 1432

Abstract

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R² were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R² in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R² in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. Full article

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

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17 pages, 7176 KiB

Open AccessArticle

A Novel Operational Rice Mapping Method Based on Multi-Source Satellite Images and Object-Oriented Classification

by Yanyan Shen, Jingcheng Zhang, Lingbo Yang, Xiaoxuan Zhou, Huizi Li and Xingjian Zhou

Agronomy 2022, 12(12), 3010; https://doi.org/10.3390/agronomy12123010 - 29 Nov 2022

Cited by 4 | Viewed by 1319

Abstract

Combining optical and synthetic aperture radar (SAR) data for crop mapping has become a crucial way to improve classification accuracy, especially in cloudy and rainy areas. However, the acquisition of optical images is significantly unstable due to the influence of cloudy and rainy weather, which seriously restricts the application of this method in practice. To solve this problem, this study proposed an optical-SAR imagery-based rice mapping method which has the advantages of less dependence on optical images, easy operation and high classification accuracy. To account for the trait of sparse availability of optical images, this method only needs one clear sky optical image in the rice growth period and combined it with multi-temporal SAR images to achieve a high accuracy rice mapping result. Meanwhile, this paper also proposed a comprehensively multi-scale segmentation parameter optimization algorithm, which considers the area consistency, shape error and location difference between the segmented object and reference object, and adopts an orthogonal experiment approach. Based on the optical image, the boundaries of the parcel objects can be segmented, which were subsequently used to perform the object-oriented classification. The results show that the overall accuracy of the proposed method in Yangzhou City is 94.64%. Moreover, according to a random pick test, it is encouraging that the proposed method has strong robustness in response to the instability of the acquisition time of SAR images. A relatively high overall accuracy of 90.09% suggested that the proposed method can provide a reliable rice mapping result in cloudy and rainy areas. Full article

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

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12 pages, 2576 KiB

Open AccessArticle

Pollination Parameter Optimization and Field Verification of UAV-Based Pollination of ‘Kuerle Xiangli’

by Yuqing Wang, Ru Bai, Xiaoyan Lu, Shaowen Quan, Yan Liu, Caixia Lin, Jie Wei, Yongfeng Su and Ruiyun Yao

Agronomy 2022, 12(10), 2561; https://doi.org/10.3390/agronomy12102561 - 19 Oct 2022

Cited by 4 | Viewed by 1631

Abstract

In this study, we investigated unmanned aerial vehicle (UAV) pollination of ‘Kuerle Xiangli’, and screened the pollination operation parameters to determine the precise parameters needed for the implementation of a ‘Kuerle Xiangli’ UAV pollination operation. Different flight height gradients, nozzle atomization particle sizes, spraying volume, and flight routes were tested and their effects on the droplets deposited were compared. UAV operation parameters were screened and field operations were conducted, comparing the fruit set rate, cost, and efficiency of different pollination methods of ‘Kuerle Xiangli’. The results show that the mist droplet effect of 1 m above the top of the tree is higher compared with that of 2 m and 3 m. The mist droplet effect of 2 L/667 m² is better compared with that of 1.5 L/667 m² and 1 L/667 m². The mist droplet effect of 120 μm nozzle atomization particle size is better than that of 110 μm, 135 μm, and 150 μm. The mist droplet effect of flying above the canopy is better than that of flying between the rows of the canopy. The inflorescence and flower fruiting rates of ‘Kuerle Xiangli’ are 63.27% and 28.84%, respectively, and the inflorescence fruiting rate is not significantly different from hand and liquid sprayer pollination. The UAV pollination saves 12.69 USD/667 m² and 3.32 USD/667 m² compared with hand and liquid spray pollination, respectively. The efficiency of UAV pollination is greater than that of liquid and hand pollination. The best combination of parameters for pollination using a quadrotor UAV is 1 m from the top of the tree, 2 L/667 m² spray volume, 120 μm spray nozzle particle size, and the flight path above the canopy. The cost of UAV pollination is 11.83 USD/667 m² and the pollination efficiency 2.67 hm^2/unit·h. Full article

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

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17 pages, 5598 KiB

Open AccessArticle

Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

by Qilei Zhu, Xingang Xu, Zhendong Sun, Dong Liang, Xiaofei An, Liping Chen, Guijun Yang, Linsheng Huang, Sizhe Xu and Min Yang

Agronomy 2022, 12(5), 1051; https://doi.org/10.3390/agronomy12051051 - 27 Apr 2022

Cited by 10 | Viewed by 2552

Abstract

Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R² of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R² of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R² of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops. Full article

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

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27 pages, 9267 KiB

Open AccessArticle

A Methodology for the Automated Delineation of Crop Tree Crowns from UAV-Based Aerial Imagery by Means of Morphological Image Analysis

by Juan Manuel Ponce, Arturo Aquino, Diego Tejada, Basil Mohammed Al-Hadithi and José Manuel Andújar

Agronomy 2022, 12(1), 43; https://doi.org/10.3390/agronomy12010043 - 25 Dec 2021

Cited by 7 | Viewed by 2645

Abstract

The popularisation of aerial remote sensing using unmanned aerial vehicles (UAV), has boosted the capacities of agronomists and researchers to offer farmers valuable data regarding the status of their crops. This paper describes a methodology for the automated detection and individual delineation of tree crowns in aerial representations of crop fields by means of image processing and analysis techniques, providing accurate information about plant population and canopy coverage in intensive-farming orchards with a row-based plant arrangement. To that end, after pre-processing initial aerial captures by means of photogrammetry and morphological image analysis, a resulting binary representation of the land plot surveyed is treated at connected component-level in order to separate overlapping tree crown projections. Then, those components are morphologically transformed into a set of seeds with which tree crowns are finally delineated, establishing the boundaries between them when they appear overlapped. This solution was tested on images from three different orchards, achieving semantic segmentations in which more than 94% of tree canopy-belonging pixels were correctly classified, and more than 98% of trees were successfully detected when assessing the methodology capacities for estimating the overall plant population. According to these results, the methodology represents a promising tool for automating the inventorying of plants and estimating individual tree-canopy coverage in intensive tree-based orchards. Full article

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

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Review

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26 pages, 2221 KiB

Open AccessReview

Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture

by Aqleem Abbas, Zhenhao Zhang, Hongxia Zheng, Mohammad Murtaza Alami, Abdulmajeed F. Alrefaei, Qamar Abbas, Syed Atif Hasan Naqvi, Muhammad Junaid Rao, Walid F. A. Mosa, Qamar Abbas, Azhar Hussain, Muhammad Zeeshan Hassan and Lei Zhou

Agronomy 2023, 13(6), 1524; https://doi.org/10.3390/agronomy13061524 - 31 May 2023

Cited by 9 | Viewed by 9504

Abstract

Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures. Full article

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

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