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Modelling Plant Diseases for Precision Crop Protection

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 33113

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Prof. Dr. Vittorio Rossi

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Guest Editor

Department of Sustainable Crop Production, Università Catolica del Sacro Cuore, Piacenza, Italy
Interests: plant pathology; botanical epidemiology; plant disease modelling; risk assessment; decision-making in crop protection; sustainable crop management; precision crop protection
Special Issues, Collections and Topics in MDPI journals

Dr. Elisa Gonzalez-Dominguez

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Guest Editor

Horta.srl, Via Egidio Gorra 55, 29122 Piacenza, Italy
Interests: plant pathology; botanical epidemiology; plant disease modelling; data analysis in botanical epidemiology; decision-support systems; precision agriculture

Dr. Giorgia Fedele

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Assistant Guest Editor

Department of Sustainable Crop Production, Università Catolica del Sacro Cuore, Piacenza, Italy
Interests: plant pathology; botanical epidemiology; plant disease modelling; risk assessment; decision-making in crop protection; sustainable crop management; biological control; precision crop protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is considered one of the most promising innovations for managing agricultural crops with less chemical input, thereby reducing negative impacts on human health and the environment while preserving or improving crop yield, quality, and safety. Precision crop protection (PCP) is a key component of precision agriculture. Special attention has been paid thus far to the spatial attributes of PCP, exploiting the within-plot variability in the distribution of plant diseases. PCP, however, has a second dimension, which is the time of intervention, which involves moving from calendar- to risk-based applications. Mathematical models are valuable tools for supporting decision-making on whether and when crop management actions are really needed. This Special Issue aims to focus on modelling plant diseases for crop protection purposes. All relevant modelling approaches will be considered, from data-based models (also exploiting big data techniques or artificial intelligence) to process-based models; from a general (e.g., strategic, tactical, or operational decision making, crop system models) to a specific perspective (e.g., models for specific crops and diseases, models for optimising monitoring activities, models for predicting disease risk or intervention thresholds, models for fungicide application). The Special Issue then covers a wide variety of models and modelling approaches or techniques, and has the ambition to provide a wide picture of the potentialities of plant disease models for precision crop protection.

Prof. Dr. Vittorio Rossi
Dr. Elisa Gonzalez-Dominguez
Dr. Giorgia Fedele
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

botanical epidemiology
plant disease modelling
decision-making in crop protection
precision crop protection

Published Papers (10 papers)

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Research

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20 pages, 10235 KiB

Open AccessArticle

Development of a Decision Support System for the Management of Mummy Berry Disease in Northwestern Washington

by Mladen Cucak, Dalphy O. C. Harteveld, Lisa Wasko DeVetter, Tobin L. Peever, Rafael de Andrade Moral and Chakradhar Mattupalli

Plants 2022, 11(15), 2043; https://doi.org/10.3390/plants11152043 - 04 Aug 2022

Cited by 1 | Viewed by 2060

Abstract

Mummy berry, caused by Monilinia vaccinii-corymbosi, is the most important disease of the northern highbush blueberry (Vaccinium corymbosum L.) in North America and can cause up to 70% yield losses in affected fields. A key event in the mummy berry disease cycle is the primary infection phase where ascospores are released by apothecia that infect emerging floral and vegetative tissues. Current management of mummy berry disease in northwestern Washington is predominantly reliant on the prevention of primary infections through prophylactic, calendar-based fungicide spray applications early in the growing season. To improve the understanding of risk during these periods and to help tailor management strategies, we developed a decision support system (DSS) based on field records spanning over five seasons and four locations in northwestern Washington. Environmental conditions across the region were highly uniform but different dynamics of apothecial development were observed under high- and low-management regimes. Based on our analysis, we suggest basing the initial iteration of the DSS on two sub-models. The first sub-model predicts the onset of apothecia based on chill-unit accumulation under high- and low-management regimes, and the second predicts primary infection risk, which provides opportunities to improve the timing of fungicide applications. The synoptic DSS proposed here is based on the current biological knowledge of the pathosystem and available data for the northwestern Washington region. We provide the analysis and the DSS implementation and evaluation as an open-source repository, providing opportunities for further improvements. Finally, we provide suggestions for future research and the operational efforts needed for improving the utility and accuracy of the mummy berry DSS. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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13 pages, 3693 KiB

Open AccessArticle

The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia

by Petra Gašparovičová, Michal Ševčík and Stanislav David

Plants 2022, 11(11), 1484; https://doi.org/10.3390/plants11111484 - 31 May 2022

Cited by 4 | Viewed by 1873

Abstract

Invasive species are now considered the second biggest threat for biodiversity and have adverse environmental, economic and social impacts. Understanding its spatial distribution and dynamics is crucial for the development of tools for large-scale mapping, monitoring and management. The aim of this study was to predict the distribution of invasive Fallopia taxa in Slovakia and to identify the most important predictors of spreading of these species. We designed models of species distribution for invasive species of Fallopia—Fallopia japonica—Japanese knotweed, Fallopia sachalinensis—Sakhalin knotweed and their hybrid Fallopia × bohemica—Czech knotweed. We designed 12 models—generalized linear model (GLM), generalized additive model (GAM), classification and regression trees (CART), boosted regression trees (BRT), multivariate adaptive regression spline (MARS), random forests (RF), support vector machine (SVM), artificial neural networks (ANN), maximum entropy (Maxent), penalized maximum likelihood GLM (GLMNET), domain, and radial basis function network (RBF). The accuracy of the models was evaluated using occurrence data for the presence and absence of species. The final simplified logistic regression model showed the three most important prediction variables lead by distances from roads and rails, then type of soil and distances from water bodies. The probability of invasive Fallopia species occurrence was evaluated using Pearson’s chi-squared test (χ

{_{}^{2}}_{1}^{}

). It significantly decreases with increasing distance from transport lines (χ

{_{}^{2}}_{1}^{}

= 118.85, p < 0.001) and depends on soil type (χ

{_{}^{2}}_{1}^{}

= 49.56, p < 0.001) and the distance from the water, where increasing the distance decrease the probability (χ

{_{}^{2}}_{1}^{}

= 8.95, p = 0.003). Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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12 pages, 22951 KiB

Open AccessArticle

Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning

by Jia Yao, Yubo Wang, Ying Xiang, Jia Yang, Yuhang Zhu, Xin Li, Shuangshuang Li, Jie Zhang and Guoshu Gong

Plants 2022, 11(6), 768; https://doi.org/10.3390/plants11060768 - 13 Mar 2022

Cited by 9 | Viewed by 3767

Abstract

The prevention and management of crop diseases play an important role in agricultural production, but there are many types of crop diseases and complex causes, and their prevention and identification add difficulties to the process. The traditional methods of identifying diseases mostly rely on human visual and manual inspection, which requires a certain amount of expert knowledge and experience. There are shortcomings such as strong subjectivity and low accuracy. This paper takes the common diseases of kiwifruit as the research object. Based on deep learning and computer vision models, and given the influence of a complex background in actual scenes on the detection of diseases, as well as the shape and size characteristics of diseases, an innovative method of target detection and semantic segmentation was proposed to identify diseases accurately. The main contributions of this research are as follows: We produced the world’s first high-quality dataset on kiwifruit. We used the target detection algorithm YOLOX, we stripped the kiwi leaves from the natural background and removed the influencing factors existing in the complex background. Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. We used the training method of learning rate decay to further improve the training effect without increasing the training cost. After experimental verification, our two-stage disease detection algorithm had the advantages of high accuracy, strong robustness, and wide detection range, which provided a more efficient solution for solving the problem of precise monitoring of crop growth environment parameters. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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19 pages, 1905 KiB

Open AccessArticle

Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance

by Armando Sterling and Julio A. Di Rienzo

Plants 2022, 11(3), 329; https://doi.org/10.3390/plants11030329 - 26 Jan 2022

Cited by 3 | Viewed by 2892

Abstract

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO₂ assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R² ranged from 0.97 to 0.99) and testing (R² ranged from 0.96 to 0.99) phases. In comparison, lower performances (R² ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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23 pages, 1780 KiB

Open AccessArticle

Weather Patterns Associated with DON Levels in Norwegian Spring Oat Grain: A Functional Data Approach

by Anne-Grete Roer Hjelkrem, Heidi Udnes Aamot, Morten Lillemo, Espen Sannes Sørensen, Guro Brodal, Aina Lundon Russenes, Simon G. Edwards and Ingerd Skow Hofgaard

Plants 2022, 11(1), 73; https://doi.org/10.3390/plants11010073 - 27 Dec 2021

Cited by 2 | Viewed by 2595

Abstract

Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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12 pages, 1113 KiB

Open AccessArticle

Weather Variables Associated with Spore Dispersal of Lecanosticta acicola Causing Pine Needle Blight in Northern Spain

by Nebai Mesanza, David García-García, Elena R. Raposo, Rosa Raposo, Maialen Iturbide, Mª Teresa Pascual, Iskander Barrena, Amaia Urkola, Nagore Berano, Aitor Sáez de Zerain and Eugenia Iturritxa

Plants 2021, 10(12), 2788; https://doi.org/10.3390/plants10122788 - 16 Dec 2021

Cited by 5 | Viewed by 2200

Abstract

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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22 pages, 4715 KiB

Open AccessFeature PaperArticle

Modeling the Effects of the Environment and the Host Plant on the Ripe Rot of Grapes, Caused by the Colletotrichum Species

by Tao Ji, Irene Salotti, Chaoyang Dong, Ming Li and Vittorio Rossi

Plants 2021, 10(11), 2288; https://doi.org/10.3390/plants10112288 - 25 Oct 2021

Cited by 15 | Viewed by 5486

Abstract

Ripe rot caused by Colletotrichum spp. is a serious threat in many vineyards, and its control relies mainly on the repeated use of fungicides. A mechanistic, dynamic model for the prediction of grape ripe rot epidemics was developed by using information and data from a systematic literature review. The model accounts for (i) the production and maturation of the primary inoculum; (ii) the infection caused by the primary inoculum; (iii) the production of a secondary inoculum; and (iv) the infection caused by the secondary inoculum. The model was validated in 19 epidemics (vineyard × year combinations) between 1980 and 2014 in China, Japan, and the USA. The observed disease incidence was correlated with the number of infection events predicted by the model and their severity (ρ = 0.878 and 0.533, respectively, n = 37, p ≤ 0.001). The model also accurately predicted the disease severity progress during the season, with a concordance correlation coefficient of 0.975 between the observed and predicted data. Overall, the model provided an accurate description of the grape ripe rot system, as well as reliable predictions of infection events and of disease progress during the season. The model increases our understanding of ripe rot epidemics in vineyards and will help guide disease control. By using the model, growers can schedule fungicides based on the risk of infection rather than on a seasonal spray calendar. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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25 pages, 3996 KiB

Open AccessArticle

Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

by Parimal Sinha and Xianming Chen

Plants 2021, 10(5), 957; https://doi.org/10.3390/plants10050957 - 11 May 2021

Cited by 7 | Viewed by 3792

Abstract

Barberry (Berberis spp.) is an alternate host for both the stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), and the stem rust pathogen, P. graminis f. sp. tritici (Pgt), infecting wheat. Infection risk was assessed to determine whether barberry could be infected by either of the pathogens in Asia and Southeastern Europe, known for recurring epidemics on wheat and the presence of barberry habitats. For assessing infection risk, mechanistic infection models were used to calculate infection indices for both pathogens on barberry following a modeling framework. In East Asia, Bhutan, China, and Nepal were found to have low risks of barberry infection by Pst but high risks by Pgt. In Central Asia, Azerbaijan, Iran, Kazakhstan, southern Russia, and Uzbekistan were identified to have low to high risks of barberry infection for both Pst and Pgt. In Northwest Asia, risk levels of both pathogens in Turkey and the Republic of Georgia were determined to be high to very high. In Southwest Asia, no or low risk was found. In Southeastern Europe, similar high or very high risks for both pathogens were noted for all countries. The potential risks of barberry infection by Pst and/or Pgt should provide guidelines for monitoring barberry infections and could be valuable for developing rust management programs in these regions. The framework used in this study may be useful to predict rust infection risk in other regions. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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22 pages, 2720 KiB

Open AccessArticle

A Mechanistic Weather-Driven Model for Ascochyta rabiei Infection and Disease Development in Chickpea

by Irene Salotti and Vittorio Rossi

Plants 2021, 10(3), 464; https://doi.org/10.3390/plants10030464 - 01 Mar 2021

Cited by 6 | Viewed by 2582

Abstract

Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model’s ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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Review

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13 pages, 14599 KiB

Open AccessReview

A Systematic Map of the Research on Disease Modelling for Agricultural Crops Worldwide

by Giorgia Fedele, Chiara Brischetto, Vittorio Rossi and Elisa Gonzalez-Dominguez

Plants 2022, 11(6), 724; https://doi.org/10.3390/plants11060724 - 09 Mar 2022

Cited by 7 | Viewed by 3197

Abstract

In this work, we developed a systematic map to identify and catalogue the literature pertaining to disease modelling for agricultural crops worldwide. Searches were performed in 2021 in the Web of Science and Scopus for papers reporting any type of disease model for 103 crops. In total, 768 papers were retrieved, and their descriptive metadata were extracted. The number of papers found increased from the mid-1900s to 2020, and most of the studies were from North America and Europe. More disease models were retrieved for wheat, potatoes, grapes, and apples than for other crops; the number of papers was more affected by the crop’s economic value than by its cultivated area. The systematic map revealed an underrepresentation of disease models for maize and rice, which is not justified by either the crop economic value or by disease impact. Most of the models were developed to understand the pathosystem, and fewer were developed for tactical disease management, strategic planning, or scenario analysis. The systematic map highlights a variety of knowledge gaps and suggests questions that warrant further research. Full article

(This article belongs to the Special Issue Modelling Plant Diseases for Precision Crop Protection)

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