Research

19 pages, 5535 KiB

Open AccessArticle

Assessing Interactions between Nitrogen Supply and Leaf Blast in Rice by Hyperspectral Imaging

by Angeline Wanjiku Maina, Mathias Becker and Erich-Christian Oerke

Remote Sens. 2024, 16(6), 939; https://doi.org/10.3390/rs16060939 - 07 Mar 2024

Viewed by 590

Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have [...] Read more.

Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have not been studied. In this study, hyperspectral imaging was used to assess the effect of N supply on symptoms of rice leaf blast under greenhouse conditions. Three rice genotypes differing in blast susceptibility grown at low, medium, and high N supply were inoculated at the four-leaf stage with three M. oryzae isolates differing in virulence. The reflectance spectra (400 to 1000 nm) of healthy and symptomatic leaves were analyzed using the spectral angle mapper algorithm for supervised classification. Mineral N supply increased the contents of chlorophyll and total N. The number and area of lesions and total blast severity varied depending on rice genotype—M. oryzae isolate interactions and the amount of mineral N applied. The reflectance spectra of healthy tissue and of blast symptom subareas differed with N supply; rice genotypes differed in the response to N supply. Infected plants at high mineral N supply could be distinguished from those at low N supply due to higher differences in the spectra of symptom subareas. Results reveal the potential (and limitations) of hyperspectral imaging for quantifying N effects on rice leaves, disease severity, and symptom expression. The impact of these findings on plant phenotyping and remote sensing under field conditions is discussed. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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16 pages, 11840 KiB

Open AccessArticle

Application of Machine Learning for Disease Detection Tasks in Olive Trees Using Hyperspectral Data

by Ioannis Navrozidis, Xanthoula Eirini Pantazi, Anastasia Lagopodi, Dionysios Bochtis and Thomas K. Alexandridis

Remote Sens. 2023, 15(24), 5683; https://doi.org/10.3390/rs15245683 - 11 Dec 2023

Viewed by 982

Abstract

Timely and accurate detection of diseases plays a significant role in attaining optimal growing conditions of olive crops. This study evaluated the use of two machine learning algorithms, Random Forest (RF) and XGBoost (XGB), in conjunction with the feature selection methods Recursive Feature [...] Read more.

Timely and accurate detection of diseases plays a significant role in attaining optimal growing conditions of olive crops. This study evaluated the use of two machine learning algorithms, Random Forest (RF) and XGBoost (XGB), in conjunction with the feature selection methods Recursive Feature Elimination (RFE) and Mutual Information (MI), for detecting stress in olive trees using hyperspectral data. The research was conducted in Halkidiki, Northern Greece, and focused on identifying stress caused by biotic and abiotic factors through the analysis of hyperspectral images. Both the RF and XGB algorithms demonstrated high efficacy in stress classification, achieving roc-auc scores of 0.977 and 0.955, respectively. The study also highlighted the effectiveness of RFE and MI in optimizing the classification process, with RF and XGB requiring a reduced number of hyperspectral features for an optimal performance of 1.00 on both occasions. Key wavelengths indicative of stress were identified in the visible to near-infrared spectrum, suggesting their strong correlation with olive tree stress. These findings contribute to precision agriculture by demonstrating the viability of using machine learning for stress detection in olive trees, and underscores the importance of feature selection in improving classifier performance. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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43 pages, 18503 KiB

Open AccessArticle

Suitability of Satellite Imagery for Surveillance of Maize Ear Damage by Cotton Bollworm (Helicoverpa armigera) Larvae

by Fruzsina Enikő Sári-Barnácz, Mihály Zalai, Stefan Toepfer, Gábor Milics, Dóra Iványi, Mariann Tóthné Kun, János Mészáros, Mátyás Árvai and József Kiss

Remote Sens. 2023, 15(23), 5602; https://doi.org/10.3390/rs15235602 - 01 Dec 2023

Viewed by 996

Abstract

The cotton bollworm (Helicoverpa armigera, Lepidoptera: Noctuidae) poses significant risks to maize. Changes in the maize plant, such as its phenology, influence the short-distance movement and oviposition of cotton bollworm adults and, thus, the distribution of the subsequent larval damage. We [...] Read more.

The cotton bollworm (Helicoverpa armigera, Lepidoptera: Noctuidae) poses significant risks to maize. Changes in the maize plant, such as its phenology, influence the short-distance movement and oviposition of cotton bollworm adults and, thus, the distribution of the subsequent larval damage. We aim to provide an overview of future approaches to the surveillance of maize ear damage by cotton bollworm larvae based on remote sensing. We focus on finding a near-optimal combination of Landsat 8 or Sentinel-2 spectral bands, vegetation indices, and maize phenology to achieve the best predictions. The study areas were 21 sweet and grain maze fields in Hungary in 2017, 2020, and 2021. Correlations among the percentage of damage and the time series of satellite images were explored. Based on our results, Sentinel-2 satellite imagery is suggested for damage surveillance, as 82% of all the extremes of the correlation coefficients were stronger, and this satellite provided 20–64% more cloud-free images. We identified that the maturity groups of maize are an essential factor in cotton bollworm surveillance. No correlations were found before canopy closure (BBCH 18). Visible bands were the most suitable for damage surveillance in mid–late grain maize (|r_median| = 0.49–0.51), while the SWIR bands, NDWI, NDVI, and PSRI were suitable in mid–late grain maize fields (|r_median| = 0.25–0.49) and sweet maize fields (|r_median| = 0.24–0.41). Our findings aim to support prediction tools for cotton bollworm damage, providing information for the pest management decisions of advisors and farmers. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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20 pages, 7502 KiB

Open AccessArticle

A Discriminative Model for Early Detection of Anthracnose in Strawberry Plants Based on Hyperspectral Imaging Technology

by Chao Liu, Yifei Cao, Ejiao Wu, Risheng Yang, Huanliang Xu and Yushan Qiao

Remote Sens. 2023, 15(18), 4640; https://doi.org/10.3390/rs15184640 - 21 Sep 2023

Cited by 1 | Viewed by 1287

Abstract

Strawberry anthracnose, caused by Colletotrichum spp., is a major disease that causes tremendous damage to cultivated strawberry plants (Fragaria × ananassa Duch.). Examining and distinguishing plants potentially carrying the pathogen is one of the most effective ways to prevent and control strawberry [...] Read more.

Strawberry anthracnose, caused by Colletotrichum spp., is a major disease that causes tremendous damage to cultivated strawberry plants (Fragaria × ananassa Duch.). Examining and distinguishing plants potentially carrying the pathogen is one of the most effective ways to prevent and control strawberry anthracnose disease. Herein, we used this method on Colletotrichum gloeosporioides at the crown site on indoor strawberry plants and established a classification and distinguishing model based on measurement of the spectral and textural characteristics of the disease-free zone near the disease center. The results, based on the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and interval random frog (IRF), extracted 5, 14, and 11 characteristic wavelengths, respectively. The SPA extracted fewer effective characteristic wavelengths, while IRF covered more information. A total of 12 dimensional texture features (TFs) were extracted from the first three minimum noise fraction (MNF) images using a grayscale co-occurrence matrix (GLCM). The combined dataset modeling of spectral and TFs performed better than single-feature modeling. The accuracy rates of the IRF + TF + BP model test set for healthy, asymptomatic, and symptomatic samples were 99.1%, 93.5%, and 94.5%, the recall rates were 100%, 94%, and 93%, and the F1 scores were 0.9955, 0.9375, and 0.9374, respectively. The total modeling time was 10.9 s, meaning that this model demonstrated the best comprehensive performance of all the constructed models. The model lays a technical foundation for the early, non-destructive detection of strawberry anthracnose. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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19 pages, 6979 KiB

Open AccessArticle

Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery

by Dong Xu, Yuwei Lu, Heng Liang, Zhen Lu, Lejun Yu and Qian Liu

Remote Sens. 2023, 15(12), 3114; https://doi.org/10.3390/rs15123114 - 14 Jun 2023

Cited by 1 | Viewed by 1640

Abstract

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor [...] Read more.

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R² value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R² value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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21 pages, 6924 KiB

Open AccessArticle

Red Palm Weevil Detection in Date Palm Using Temporal UAV Imagery

by Stephanie Delalieux, Tom Hardy, Michel Ferry, Susi Gomez, Lammert Kooistra, Maria Culman and Laurent Tits

Remote Sens. 2023, 15(5), 1380; https://doi.org/10.3390/rs15051380 - 28 Feb 2023

Cited by 7 | Viewed by 3189

Abstract

Red palm weevil (RPW) is widely considered a key pest of palms, creating extensive damages to the date palm trunk that inevitably leads to palm death if no pest eradication is done. This study evaluates the potential of a remote sensing approach for [...] Read more.

Red palm weevil (RPW) is widely considered a key pest of palms, creating extensive damages to the date palm trunk that inevitably leads to palm death if no pest eradication is done. This study evaluates the potential of a remote sensing approach for the timely and reliable detection of RPW infestation on the palm canopy. For two consecutive years, an experimental field with infested and control palms was regularly monitored by an Unmanned Aerial Vehicle (UAV) carrying RGB, multispectral, and thermal sensors. Simultaneously, detailed visual observations of the RPW effects on the palms were made to assess the evolution of infestation from the initial stage until palm death. A UAV-based image processing chain for nondestructive RPW detection was built based on segmentation and vegetation index analysis techniques. These algorithms reveal the potential of thermal data to detect RPW infestation. Maximum temperature values and standard deviations within the palm crown revealed a significant (α = 0.05) difference between infested and non-infested palms at a severe infestation stage but before any visual canopy symptoms were noticed. Furthermore, this proof-of-concept study showed that the temporal monitoring of spectral vegetation index values could contribute to the detection of infested palms before canopy symptoms are visible. The seasonal significant (α = 0.05) increase of greenness index values, as observed in non-infested trees, could not be observed in infested palms. These findings are of added value for steering management practices and future related studies, but further validation of the results is needed. The workflow and resulting maps are accessible through the Mapeo^® visualization platform. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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16 pages, 4430 KiB

Open AccessArticle

Ultra-High-Resolution UAV-Based Detection of Alternaria solani Infections in Potato Fields

by Ruben Van De Vijver, Koen Mertens, Kurt Heungens, David Nuyttens, Jana Wieme, Wouter H. Maes, Jonathan Van Beek, Ben Somers and Wouter Saeys

Remote Sens. 2022, 14(24), 6232; https://doi.org/10.3390/rs14246232 - 09 Dec 2022

Cited by 9 | Viewed by 2034

Abstract

Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application of plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer the advantage of a detailed [...] Read more.

Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application of plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer the advantage of a detailed top-down perspective, with high-contrast images ideally suited for detecting Alternaria solani lesions. A field experiment was conducted with 8 plots housing 256 infected plants which were monitored 6 times over a 16-day period with a UAV. A modified RGB camera, sensitive to NIR, was combined with a superzoom lens to obtain ultra-high-resolution images with a spatial resolution of 0.3 mm/px. More than 15,000 lesions were annotated with points in two full size images corresponding to 1250 cropped tiles of 256 by 256 pixels. A deep learning U-Net model was trained to predict the density of Alternaria solani lesions for every pixel. In this way, density maps were calculated to indicate disease hotspots as a guide for the farmer. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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15 pages, 5304 KiB

Open AccessArticle

Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs

by Jiale Jiang, Haiyan Liu, Chen Zhao, Can He, Jifeng Ma, Tao Cheng, Yan Zhu, Weixing Cao and Xia Yao

Remote Sens. 2022, 14(14), 3446; https://doi.org/10.3390/rs14143446 - 18 Jul 2022

Cited by 22 | Viewed by 2759

Abstract

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural [...] Read more.

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (<0.007 s per image) and smaller memory requirements for the model parameters (<20 MB), their accuracy was relatively low (~87%). In addition to the role of CNN architecture in controlling overall accuracy, environmental effects (e.g., residual water stains on healthy leaves) were found to cause misclassifications in the field images. Moreover, the small size of some target symptoms and the similarity of symptoms between some different diseases further reduced the accuracy. Overall, the study provides insight into the collective effects of model architecture, training strategies and input datasets on the performance of CNNs, providing guidance for robust CNN design for timely and accurate crop disease diagnosis in a real-world environment. Full article

(This article belongs to the Special Issue Spectral Imaging Technology for Crop Disease Detection)

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