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Crop Disease Detection Using Remote Sensing Image Analysis

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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 January 2022) | Viewed by 37860

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Special Issue Editor

Dr. Xanthoula Eirini Pantazi

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Guest Editor

Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: artificial intelligence; machine learning; data mining; data fusion; precision agriculture; biosystems; engineering; automation; sensors; yield prediction; crop disease detection; weed management
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Special Issue Information

Dear Colleagues,

Climate change and climate variability impact requires strategic innovations for timely and accurate plant disease assessment. Crop condition monitoring has a significant impact on disease control, limiting the tremendous effect to agricultural production, degrading yield and quality and consequently leading to severe financial loss for farmers. Conventional disease control is often based on the hypothesis that pathogenic factors are propagated uniformly over cultivated fields. Precision farming tools oriented to disease propagation assessment and location-dependent management are capable of leading to a lower environmental footprint yielded through lower pesticide application and relevant financial losses. Remote-sensing-based technologies have proven more effective compared to conventional ones on occasions where iterative large-scale measurements are needed as the only sole method for data acquisition. Innovative imaging sensor tools are capable of improving spatial and spectral resolution accuracies that enable not only the assessment of foliar symptoms (image, texture, and spectral sensors) and spatial disease manifestation, but also the evaluation of early detection approaches, aiming to detect changes in leaf optical behavior due to infection occurrence, which are not yet perceived by the human vision system (hyperspectral images). Recently, different approaches that are oriented to disease monitoring and detection through employing optical sensors fitted on a variety of platforms have been demonstrated, including portable solutions to satellite, aircraft, and unmanned aerial vehicles (UAVs) for efficient crop monitoring. Simultaneously, noticeable progress in the Artificial Intelligence (AI) field enables successful supervised and unsupervised image analysis based on deep learning methods to enhance the performance of crop health monitoring. This Special Issue aims to gather relevant research work of novel applications that employ remote sensing techniques for plant disease detection.

Dr. Xanthoula Eirini Pantazi
Guest Editor

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Keywords

Field spectroscopy
Crop health status
Precision agriculture
Deep learning
Data mining
Hyperspectral sensors
Sensor fusion
Data fusion
Multispectral sensors
Machine learning
Artificial Intelligence

Published Papers (10 papers)

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20 pages, 7816 KiB

Open AccessArticle

Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery

by Vasilis Psiroukis, Borja Espejo-Garcia, Andreas Chitos, Athanasios Dedousis, Konstantinos Karantzalos and Spyros Fountas

Remote Sens. 2022, 14(3), 731; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030731 - 04 Feb 2022

Cited by 15 | Viewed by 3122

Abstract

Broccoli is an example of a high-value crop that requires delicate handling throughout the growing season and during its post-harvesting treatment. As broccoli heads can be easily damaged, they are still harvested by hand. Moreover, human scouting is required to initially identify the field segments where several broccoli plants have reached the desired maturity level, such that they can be harvested while they are in the optimal condition. The aim of this study was to automate this process using state-of-the-art Object Detection architectures trained on georeferenced orthomosaic-derived RGB images captured from low-altitude UAV flights, and to assess their capacity to effectively detect and classify broccoli heads based on their maturity level. The results revealed that the object detection approach for automated maturity classification achieved comparable results to physical scouting overall, especially for the two best-performing architectures, namely Faster R-CNN and CenterNet. Their respective performances were consistently over 80% mAP@50 and 70% mAP@75 when using three levels of maturity, and even higher when simplifying the use case into a two-class problem, exceeding 91% and 83%, respectively. At the same time, geometrical transformations for data augmentations reported improvements, while colour distortions were counterproductive. The best-performing architecture and the trained model could be tested as a prototype in real-time UAV detections in order to assist in on-field broccoli maturity detection. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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19 pages, 7459 KiB

Open AccessArticle

Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements

by Lili Luo, Qingrui Chang, Qi Wang and Yong Huang

Remote Sens. 2021, 13(22), 4560; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224560 - 13 Nov 2021

Cited by 15 | Viewed by 2853

Abstract

Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (R_λ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VI_s) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VI_c) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on R_λ, VI_s, VI_c, and R_λ + VI_s + VI_c and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VI_c-based classification models was 100%, which is significantly higher than that of the VI_s and R_λ-based models. Among the Anth regression models, the accuracy of the MLR model based on R_λ + VI_s + VI_c was the highest (R²_c = 0.85; R²_v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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18 pages, 9368 KiB

Open AccessArticle

Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat

by Imran Haider Khan, Haiyan Liu, Wei Li, Aizhong Cao, Xue Wang, Hongyan Liu, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao and Xia Yao

Remote Sens. 2021, 13(18), 3612; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183612 - 10 Sep 2021

Cited by 34 | Viewed by 5231

Abstract

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R²) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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24 pages, 11218 KiB

Open AccessArticle

Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

by Siyuan Li, Jiannan Jiao and Chi Wang

Remote Sens. 2021, 13(17), 3510; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173510 - 04 Sep 2021

Cited by 17 | Viewed by 3530

Abstract

The monitoring of vegetation via remote sensing has been widely applied in various fields, such as crop diseases and pests, forest coverage and vegetation growth status, but such monitoring activities were mainly carried out in the daytime, resulting in limitations in sensing the status of vegetation at night. In this article, with the aim of monitoring the health status of outdoor plants at night by remote sensing, a polarized multispectral low-illumination-level imaging system (PMSIS) was established, and a fusion algorithm was proposed to detect vegetation by sensing the spectrum and polarization characteristics of the diffuse and specular reflection of vegetation. The normalized vegetation index (NDVI), degree of linear polarization (DoLP) and angle of polarization (AOP) are all calculated in the fusion algorithm to better detect the health status of plants in the night environment. Based on NDVI, DoLP and AOP fusion images (NDAI), a new index of night plant state detection (NPSDI) was proposed. A correlation analysis was made for the chlorophyll content (SPAD), nitrogen content (NC), NDVI and NPSDI to understand their capabilities to detect plants under stress. The scatter plot of NPSDI shows a good distinction between vegetation with different health levels, which can be seen from the high specificity and sensitivity values. It can be seen that NPSDI has a good correlation with NDVI (coefficient of determination R² = 0.968), PSAD (R² = 0.882) and NC (R² = 0.916), which highlights the potential of NPSDI in the identification of plant health status. The results clearly show that the proposed fusion algorithm can enhance the contrast effect and the generated fusion image will carry richer vegetation information, thereby monitoring the health status of plants at night more effectively. This algorithm has a great potential in using remote sensing platform to monitor the health of vegetation and crops. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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17 pages, 2908 KiB

Open AccessArticle

Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

by Qiong Zheng, Huichun Ye, Wenjiang Huang, Yingying Dong, Hao Jiang, Chongyang Wang, Dan Li, Li Wang and Shuisen Chen

Remote Sens. 2021, 13(2), 278; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020278 - 14 Jan 2021

Cited by 23 | Viewed by 3115

Abstract

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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26 pages, 7142 KiB

Open AccessArticle

A Modified Geometrical Optical Model of Row Crops Considering Multiple Scattering Frame

by Xu Ma and Yong Liu

Remote Sens. 2020, 12(21), 3600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213600 - 02 Nov 2020

Cited by 2 | Viewed by 1769 | Correction

Abstract

The canopy reflectance model is the physical basis of remote sensing inversion. In canopy reflectance modeling, the geometric optical (GO) approach is the most commonly used. However, it ignores the description of a multiple-scattering contribution, which causes an underestimation of the reflectance. Although researchers have tried to add a multiple-scattering contribution to the GO approach for forest modeling, different from forests, row crops have unique geometric characteristics. Therefore, the modeling approach originally applied to forests cannot be directly applied to row crops. In this study, we introduced the adding method and mathematical solution of integral radiative transfer equation into row modeling, and on the basis of improving the overlapping relationship of the gap probabilities involved in the single-scattering contribution, we derived multiple-scattering equations suitable for the GO approach. Based on these modifications, we established a row model that can accurately describe the single-scattering and multiple-scattering contributions in row crops. We validated the row model using computer simulations and in situ measurements and found that it can be used to simulate crop canopy reflectance at different growth stages. Moreover, the row model can be successfully used to simulate the distribution of reflectances (RMSEs < 0.0404). During computer validation, the row model also maintained high accuracy (RMSEs < 0.0062). Our results demonstrate that considering multiple scattering in GO-approach-based modeling can successfully address the underestimation of reflectance in the row crops. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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Graphical abstract

20 pages, 2327 KiB

Open AccessArticle

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

by HaiXiang Guan, HuanJun Liu, XiangTian Meng, Chong Luo, YiLin Bao, YuYang Ma, ZiYang Yu and XinLe Zhang

Remote Sens. 2020, 12(19), 3149; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193149 - 25 Sep 2020

Cited by 16 | Viewed by 2998

Abstract

Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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19 pages, 9459 KiB

Open AccessArticle

Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

by Francesco Savian, Marta Martini, Paolo Ermacora, Stefan Paulus and Anne-Katrin Mahlein

Remote Sens. 2020, 12(14), 2194; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142194 - 09 Jul 2020

Cited by 16 | Viewed by 3893

Abstract

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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19 pages, 3679 KiB

Open AccessArticle

Practical Recommendations for Hyperspectral and Thermal Proximal Disease Sensing in Potato and Leek Fields

by Simon Appeltans, Angela Guerrero, Said Nawar, Jan Pieters and Abdul M. Mouazen

Remote Sens. 2020, 12(12), 1939; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121939 - 15 Jun 2020

Cited by 14 | Viewed by 3491

Abstract

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17°, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis)

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