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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 34712

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Dr. Haiguang Wang

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Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China
Interests: plant pathology; epidemiology; disease monitoring; disease prediction; disease image recognition; smart phytoprotection; climate change
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Special Issue Information

Dear Colleagues,

Fungal diseases are the most common diseases in crop plants. They can reduce the yield and quality of crop products, and pathogenic fungi may produce harmful metabolites, such as toxins, which seriously threaten agricultural safety, food safety, biological safety, and ecological safety. Climate changes, crop variety changes, cropping system reform, and the development of economy and trade have great impacts on crop fungal disease epidemics. Therefore, studies on the epidemiology and control of crop fungal diseases are of great importance.

In recent years, new techniques and methods based on molecular biology technology and modern information technology (artificial intelligence, image processing, Internet of Things, remote sensing technology, etc.) have been widely applied in research investigating the epidemiology of crop fungal diseases. Significant progress has been achieved in many areas of epidemiology, resulting in a more comprehensive understanding of epidemic dynamics, the genetic basis of epidemics, and disease monitoring, forecasting, and control. Particular emphasis has been given to ecology-based management and sustainable management of crop fungal diseases, for which several new measures have been established. The integrated application of various management measures, such as plant quarantine, disease-resistant crop variety utilization, and agricultural, biological, physical, and chemical control, has improved disease control.

This Special Issue will present the latest advances and innovative perspectives on the epidemiology and control of crop fungal diseases. We welcome the submission of original research and review articles focusing on any aspect of the epidemiology and control of crop plant fungal diseases.

Dr. Haiguang Wang
Guest Editor

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Keywords

crop disease
fungal disease
epidemiology
disease control
disease epidemic
sustainable control
control measure
disease monitoring
disease forecast
disease management

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Published Papers (11 papers)

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Editorial

Jump to: Research

8 pages, 229 KiB

Open AccessEditorial

Epidemiology and Control of Fungal Diseases in Crop Plants

by Haiguang Wang

Agronomy 2023, 13(9), 2327; https://doi.org/10.3390/agronomy13092327 - 5 Sep 2023

Cited by 1 | Viewed by 3738

Abstract

Crop fungal diseases are a major threat to crop health and food security worldwide. The epidemiology is the basis for effective and sustainable control of crop fungal diseases. Safe, effective, sustainable, and eco-friendly disease control measures have important economic, ecological, and social significances. This Special Issue, “Epidemiology and Control of Fungal Diseases of Crop Plants”, collected one communication and nine original research articles focusing on the identification and detection of the causal agents of alfalfa Fusarium root rot, strawberry black spot, and barley leaf stripe; the semantic segmentation of wheat stripe rust images; the image-based identification of wheat stripe rust and wheat leaf rust; the image-based identification of the severity of wheat Fusarium head blight; the development process of vanilla Fusarium wilt; the regional migration of wheat leaf rust pathogen; the early prediction of potato early blight; the screening of alternative fungicides for the control of alfalfa Fusarium root rot; and the biocontrol potential of endophytic fungi to control of cumin root rot, presenting the progress of research on the epidemiology and control of crop fungal diseases. The studies contained in this Special Issue facilitated the development of epidemiology of the related crop fungal diseases and provided some basis for control of the diseases, which is conducive to the sustainable management of these diseases. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

Research

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9 pages, 5161 KiB

Open AccessCommunication

Characterization of Alternaria Species Associated with Black Spot of Strawberry in Dandong, China

by Xiaozhe Sun, Cuiyan Wang, Xu Gao, Xuehong Wu and Yu Fu

Agronomy 2023, 13(4), 1014; https://doi.org/10.3390/agronomy13041014 - 30 Mar 2023

Cited by 3 | Viewed by 3361

Abstract

Dandong has become the largest strawberry production and export base in China. Strawberry black spot disease is widespread and causes significant economic losses to strawberry growers in both the growing and harvest seasons. Until now, no study has reported the presence of the Alternaria species, the pathogen of strawberry black spot disease, in Dandong, Liaoning province, China. In 2020–2022, 108 isolates were obtained from strawberry leaves with typical symptoms of strawberry black spot disease from 56 major professional growing operations. Combined with morphological and molecular characteristics, the majority of isolates were identified as A. tenuissima (78 isolates, 72.2%), which had established total supremacy, followed by A. alternata (30 isolates, 27.8%). The pathogenicity results show that A. tenuissima and A. alternata are the two main pathogenic factors of strawberry black spot disease, the disease indexes of which were designated as 49.6–100.0% and 20.4–59.5%. To our knowledge, this paper is the first to identify A. tenuissima and A. alternata as causing black spot disease in strawberries in Dandong, China. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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13 pages, 1828 KiB

Open AccessEditor’s ChoiceArticle

Identification of Pathogens Causing Alfalfa Fusarium Root Rot in Inner Mongolia, China

by Le Wang, Na Wang, Jialiang Yu, Jie Wu, Huan Liu, Kejian Lin and Yuanyuan Zhang

Agronomy 2023, 13(2), 456; https://doi.org/10.3390/agronomy13020456 - 3 Feb 2023

Cited by 12 | Viewed by 2622

Abstract

Alfalfa Fusarium Root Rot (AFRR) is a serious soil-borne disease with a complex pathogenicity. Diseased samples suspected of AFRR were collected from Hohhot, Ordos, Hulunbeier, Chifeng, and Bayannur in Inner Mongolia, China, leading to 317 isolates. The isolates were identified as Fusarium acuminatum, F. solani, F. equiseti, F. incarnatum, F. oxysporum, F. avenaceum, F. verticillioides, F. proliferatum, F. falciforme, F. tricinctum, F. virguliforme, and F. redolens, and the results of pathogenicity testing showed that 12 Fusarium species could cause alfalfa root rot. Among these, F. verticillioides, F. falciforme, and F. virguliforme have not previously been reported to cause AFRR in China. Although the population structure of the pathogens differed in different regions, the dominant pathogenic species was F. acuminatum. Fungicide toxicity tests showed that seven fungicides inhibited F. acuminatum, of which fludioxonil, kresoxim-methyl, and triadimefon were found to be strongly toxic towards F. acuminatum with EC₅₀ values of 0.09, 2.28, and 16.37 μg/mL, respectively, suggesting that these could be used as alternative fungicides for the control of AFRR. The results of this study can provide a theoretical basis for exploring the occurrence and epidemiology of alfalfa root rot and strategies for its control. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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39 pages, 3038 KiB

Open AccessEditor’s ChoiceArticle

Identification of Stripe Rust and Leaf Rust on Different Wheat Varieties Based on Image Processing Technology

by Hongli Wang, Qian Jiang, Zhenyu Sun, Shiqin Cao and Haiguang Wang

Agronomy 2023, 13(1), 260; https://doi.org/10.3390/agronomy13010260 - 14 Jan 2023

Cited by 8 | Viewed by 2985

Abstract

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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14 pages, 53094 KiB

Open AccessEditor’s ChoiceArticle

GSEYOLOX-s: An Improved Lightweight Network for Identifying the Severity of Wheat Fusarium Head Blight

by Rui Mao, Zhengchao Wang, Feilong Li, Jia Zhou, Yinbing Chen and Xiaoping Hu

Agronomy 2023, 13(1), 242; https://doi.org/10.3390/agronomy13010242 - 13 Jan 2023

Cited by 16 | Viewed by 2824

Abstract

F u s a r i u m

F u s a r i u m

head blight (FHB) is one of the most detrimental wheat diseases. The accurate identification of FHB severity is significant to the sustainable management of FHB and the guarantee of food production and security. A total of 2752 images with five infection levels were collected to establish an FHB severity grading dataset (FHBSGD), and a novel lightweight GSEYOLOX-s was proposed to automatically recognize the severity of FHB. The simple, parameter-free attention module (SimAM) was fused into the CSPDarknet feature extraction network to obtain more representative disease features while avoiding additional parameters. Meanwhile, the ghost convolution of the model head (G-head) was designed to achieve lightweight and speed improvements. Furthermore, the efficient intersection over union (EIoU) loss was employed to accelerate the convergence speed and improve positioning precision. The results indicate that the GSEYOLOX-s model with only 8.06 MB parameters achieved a mean average precision (mAP) of 99.23% and a detection speed of 47 frames per second (FPS), which is the best performance compared with other lightweight models, such as EfficientDet, Mobilenet-YOLOV4, YOLOV7, YOLOX series. The proposed GSEYOLOX-s was deployed on mobile terminals to assist farmers in the real-time identification of the severity of FHB and facilitate the precise management of crop diseases. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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16 pages, 63183 KiB

Open AccessArticle

Development of Loop-Mediated Isothermal Amplification Assay for the Rapid Detection of Pyrenophora graminea in Barley Seeds

by Zhangwei Hu, Liyifan Chen, Chunmei Du, Yaoxia Liu, Jiahui Yan, Qingyun Guo and Qiang Yao

Agronomy 2023, 13(1), 62; https://doi.org/10.3390/agronomy13010062 - 24 Dec 2022

Cited by 3 | Viewed by 2051

Abstract

Barley leaf stripe, caused by Pyrenophora graminea, is an essential systemic seed-borne disease in barley worldwide. Barley is a major cereal crop in the Qinghai–Tibet Plateau, and barley production has been threatened by leaf stripe in this region, particularly in organic farming regions. Detecting the pathogen in infected barley seeds is crucial for managing barley leaf stripe. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed to detect the pathogen based on primers designed based on the sequence of the pig 14 gene (GenBank: AJ277800) of P. graminea. The optimal concentrations of MgSO₄, dNTPs, and enzymes in the LAMP reaction system were established as 10.0 mM, 1.0 mM, and 8 U in a 25 μL reaction volume, respectively. The established LAMP methods for detecting P. graminea were optimally performed at 63 °C for 70 min with high reliability. The minimum detection limit was 1 × 10⁻² ng·μL⁻¹ in the 25 μL reaction system. The specificity of LAMP for P. graminea was validated with eight fungal species. All DNA extracts from P. graminea-infected barley seeds with incubation, intact, and smashed treatments were applied in LAMP and confirmed to enable the detection of the pathogen. The LAMP assay in this study could facilitate the detection of P. graminea in barley seeds onsite, provide information for seed health certificates, and help decide on seed treatment in leaf stripe management. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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13 pages, 1360 KiB

Open AccessFeature PaperArticle

SSR Genotypes of the Puccinia triticina in 15 Provinces of China Indicate Regional Migration in One Season from East to West and South to North

by Zhe Xu, Hongfu Li, Xiaoshuang Xia, Bo Liu, Li Gao, Wanquan Chen and Taiguo Liu

Agronomy 2022, 12(12), 3068; https://doi.org/10.3390/agronomy12123068 - 4 Dec 2022

Cited by 2 | Viewed by 1594

Abstract

Leaf rust of wheat caused by Puccinia triticina (Pt) is one of the most common fungal diseases in the southwest and northwest of China, the middle and lower reaches of the Yangtze River, and the southern part of the Huang-Huai-Hai river basin. Using 13 simple sequence repeat (SSR) markers, we systematically revealed the genotypic diversities, population differentiation and reproduction of Pt isolates in 15 wheat-producing areas in China. A total of 622 isolates were divided into 3 predominant populations, including the eastern Pt populations, consisting of Pt samples from 8 eastern provinces of Beijing, Hebei, Shanxi, Shaanxi, Anhui, Shandong, Henan, and Heilongjiang; the 4 western Pt populations from Gansu, Qinghai, Sichuan, and Inner Mongolia provinces; and the bridge Pt populations including Jiangsu, Hubei, and Yunnan, which communicated the other 2 populations as a “bridge”. The pathogen transmission of eastern Pt populations was more frequent than western Pt populations. The linkage disequilibrium test indicated that the whole Pt population was in a state of linkage disequilibrium. However, populations of Beijing, Hebei, Shaanxi, Jiangsu, Henan, and Heilongjiang provinces showed obvious linkage equilibrium, while the five provinces of Qinghai, Hubei, Anhui, Shandong, and Inner Mongolia supported clonal modes of reproduction. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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11 pages, 2213 KiB

Open AccessArticle

Semantic Segmentation of Wheat Stripe Rust Images Using Deep Learning

by Yang Li, Tianle Qiao, Wenbo Leng, Wenrui Jiao, Jing Luo, Yang Lv, Yiran Tong, Xuanjing Mei, Hongsheng Li, Qiongqiong Hu and Qiang Yao

Agronomy 2022, 12(12), 2933; https://doi.org/10.3390/agronomy12122933 - 23 Nov 2022

Cited by 15 | Viewed by 2969

Abstract

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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15 pages, 2482 KiB

Open AccessArticle

In Vitro Evaluation of the Development of Fusarium in Vanilla Accessions

by Ana T. Mosquera-Espinosa, Andrea Bonilla-Monar, Nicola S. Flanagan, Álvaro Rivas, Francisco Sánchez, Paul Chavarriaga, Alejandra Bedoya and Donald Riascos-Ortiz

Agronomy 2022, 12(11), 2831; https://doi.org/10.3390/agronomy12112831 - 12 Nov 2022

Cited by 5 | Viewed by 3038

Abstract

Vanilla is an economically important crop for low-lying humid tropical regions. World demand for natural vanilla is increasing, but cultivated plants face serious phytosanitary problems. The disease known as Fusarium wilt is mainly related to the fungus Fusarium oxysporum f. sp. vanillae, and for its management, the pathogen–host relationship must be understood. Four in vitro multiplied vanilla accessions were evaluated: two Vanilla planifolia from Colombia and Mexico, one from V. odorata, and one (1) F1 hybrid (V. rivasii × V. trigonocarpa). In addition, three isolates of Fusarium from different symptomatic plants present in small-scale agroforestry systems: (1Fov) F. oxysporum f. sp. vanillae from leaf, (2Fov) F. oxysporum f. sp. vanillae from root and (3Fs) F. solani also from root. Plants with two months of growth were inoculated in vitro by immersion of roots, and the development of Fusarium wilt was recorded for 15 days, using a severity scale to describe symptoms and to calculate the area under the disease progress curve (AUDPC). No statistical differences were found when analyzing the interaction between Fusarium isolates and vanilla accessions. However, when independently analyzing the design factor Fusarium isolates, there were significant differences; the 1Fov isolate of F. oxysporum f. sp. vanillae induced the highest symptoms as well as death in some plants of all accessions, while F. solani was considered a secondary pathogen. There were no statistical differences for the vanilla accessions factor, but the values of AUDPC and symptoms observed suggest a slight resistance in all the accessions. Therefore, it is suggested to explore the vanilla gene pool to generate multiplication material with resistance genes and to contribute with genetic improvement to successfully integrate the management of Fusarium wilt in commercial systems. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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21 pages, 4360 KiB

Open AccessArticle

Biocontrol Potential of Endophytic Fungi for the Eco-Friendly Management of Root Rot of Cuminum cyminum Caused by Fusarium solani

by Kamal A. M. Abo-Elyousr, Omer H. M. Ibrahim, Adel D. Al-Qurashi, Magdi A. A. Mousa and Maged M. Saad

Agronomy 2022, 12(11), 2612; https://doi.org/10.3390/agronomy12112612 - 24 Oct 2022

Cited by 6 | Viewed by 2257

Abstract

Root rot disease of Cuminum cyminum caused by Fusarium solani is one of the most destructive diseases threatening cumin production. The present study investigates the biocontrol potential of some endophytes against F. solani and their effect on the induction of defense-related enzymes in a greenhouse. The results herein presented illustrate the strong biocontrol potential of three (out of twelve) endophytes. During the in vitro assay, three isolates demonstrated strong mycelial growth inhibition of F. solani: isolates 3, 4, and 9, with 87%, 65%, and 80% reductions, respectively, with respect to the control (100%). These isolates were identified as Trichoderma harzianum, T. longibrachiatum, and Chaetomium globosum, which produce siderophore and indole-3-acetic acid (IAA). Cumin seed priming with the culture filtrates of T. harzianum, C. globosum, and T. longibrachiatum positively affected the seed germination, as a higher germination (%) of culture filtrate-treated seeds was observed followed by infected and healthy control/untreated seeds. In the greenhouse, the application of T. harzianum, T. longibrachiatum, and C. globosum caused a reduction in disease severity (67.7%, 58.1%, and 59.3%, respectively) on cumin plants, with a lower disease severity (20%, 26%, and 25%, respectively) recorded in treated plants compared to the infected control (62%). Furthermore, a significant increase in defense-related enzymes in culture filtrate-treated cumin plants was recorded. Higher peroxidase (PO), polyphenoloxidase (PPO), and phenylalanine ammonia-lyase (PAL) activity, and a higher content of phenolic compounds, were found in culture filtrate-treated plants. These results indicate that the culture filtrates of these bioagents not only increased seed germination, but also protected the plants from F. solani infection by acting as important elements of the cellular antioxidant system in plants upon infection, conferring the biocontrol potential of C. globosum and Trichoderma species toward mitigating the root rot disease of cumin plants in a greenhouse. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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13 pages, 1610 KiB

Open AccessEditor’s ChoiceArticle

Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain

by Laura Meno, Isaac Kwesi Abuley, Olga Escuredo and M. Carmen Seijo

Agronomy 2022, 12(7), 1611; https://doi.org/10.3390/agronomy12071611 - 4 Jul 2022

Cited by 11 | Viewed by 3363

Abstract

In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later. Full article

(This article belongs to the Special Issue Epidemiology and Control of Fungal Diseases of Crop Plants)

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