scholarly journals Predicting symptoms of downy mildew, powdery mildew, and gray mold diseases of grapevine through machine learning

2021 ◽  
pp. 57-69
Author(s):  
Iride Volpi ◽  
Diego Guidotti ◽  
Michele Mammini ◽  
Susanna Marchi
Keyword(s):  
Machine Learning ◽  
Powdery Mildew ◽  
Downy Mildew ◽  
Negative Impact ◽  
Crop Protection ◽  
Gray Mold ◽  
Test Set ◽  
Spatial Indices ◽  
Crop Disease ◽  
Complex Relationships

Downy mildew, powdery mildew, and gray mold are major diseases of grapevine with a strong negative impact on fruit yield and fruit quality. These diseases are controlled by the application of chemicals, which may cause undesirable effects on the environment and on human health. Thus, monitoring and forecasting crop disease is essential to support integrated pest management (IPM) measures. In this study, two tree-based machine learning (ML) algorithms, random forest and C5.0, were compared to test their capability to predict the appearance of symptoms of grapevine diseases, considering meteorological conditions, spatial indices, the number of crop protection treatments and the frequency of monitoring days in which symptoms were recorded in the previous year. Data collected in Tuscany region (Italy), on the presence of symptoms on grapevine, from 2006 to 2017 were divided with an 80/20 proportion in training and test set, data collected in 2018 and 2019 were tested as independent years for downy mildew and powdery mildew. The frequency of symptoms in the previous year and the cumulative precipitation from April to seven days before the monitoring day were the most important variables among those considered in the analysis for predicting the occurrence of disease symptoms. The best performance in predicting the presence of symptoms of the three diseases was obtained with the algorithm C5.0 by applying (i) a technique to deal with imbalanced dataset (i.e., symptoms were detected in the minority of observations) and (ii) an optimized cut-off for predictions. The balanced accuracy achieved in the test set was 0.8 for downy mildew, 0.7 for powdery mildew and 0.9 for gray mold. The application of the models for downy mildew and powdery mildew in the two independent years (2018 and 2019) achieved a lower balanced accuracy, around 0.7 for both the diseases. Machine learning models were able to select the best predictors and to unravel the complex relationships among geographic indices, bioclimatic indices, protection treatments and the frequency of symptoms in the previous year. 

scholarly journals NGS comparison of Downy and Powdery mildew resistance genomic regions between 'Regent' and 'Red Globe'

2021 ◽  
Author(s):  
Carel Jacobus van Heerden ◽  
Phylli Burger ◽  
Johan Theodorus Burger ◽  
Renée Prins
Keyword(s):  
Next Generation Sequencing ◽  
Powdery Mildew ◽  
Downy Mildew ◽  
Genetic Factors ◽  
Powdery Mildew Resistance ◽  
Negative Impact ◽  
Chromosome 15 ◽  
Genomic Regions ◽  
Per Gene ◽  
Generation Sequencing

Powdery and downy mildew have a large negative impact on grape production worldwide. Quantitative trait loci (QTL) mapping projects have identified several loci for the genetic factors responsible for resistance to these pathogens. Several of these studies have focused on the cultivar Regent, which carries the resistance loci to downy mildew on chromosome 18 (Rpv3), as well powdery mildew on chromosome 15 (Ren3, Ren9). Several other minor resistance loci have also been identified on other chromosomes. Here we report on the re-sequencing of the Regent and Red Globe (susceptible) genomes using next generation sequencing. While the genome of Regent has more SNP variants than Red Globe, the distribution of these variants across the two genomes is not the same, nor is it uniform. The variation per gene shows that some genes have higher SNP density than others and that the number of SNPs for a given gene is not always the same for the two cultivars. In this study, we investigate the effectiveness of studying the variation of non-synonymous to synonymous SNP ratio's between resistant and susceptible cultivars in the target QTL regions as a strategy to narrow down the number of likely candidate genes for Rpv3, Ren3 and Ren9.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Content of trans-resveratrol in leaves and berries of interspecific grapevine (Vitis sp.) varieties

2009 ◽  
Vol 26 (Special Issue) ◽  
pp. S13-S17 ◽  
Author(s):  
P. Bábíková ◽  
N. Vrchotová ◽  
J. Tříska ◽  
M. Kyseláková
Keyword(s):  
Powdery Mildew ◽  
Downy Mildew ◽  
Plasmopara Viticola ◽  
Fungal Diseases ◽  
Uncinula Necator ◽  
Botryotinia Fuckeliana

The aim of this project was to study changes in the content of <i>trans</i>-resveratrol in berries and leaves of grapevine (<i>Vitis</i> sp.) infested by fungal diseases, especially by <i>Botryotinia fuckeliana</i> Whetzel, called as grey mildew, <i>Plasmopara viticola</i> (Berk. & M.A. Curtis) Berl & De Toni, called downy mildew and <i>Uncinula necator</i> (Schw.) Burr, called powdery mildew. In our experiments two white and two blue varieties were used. Contents of <i>trans</i>-resveratrol were determined in healthy and infested leaves and in healthy berries. Infested leaves of white varieties contained more <i>trans</i>-resveratrol than those of blue varieties. The content of <i>trans</i>-resveratrol in berries was lower than that in leaves.

scholarly journals Genome-wide identification and expression analysis of the Brassica oleracea L. chitin-binding genes and response to pathogens infections

Planta ◽  
2021 ◽  
Vol 253 (4) ◽  
Author(s):  
Mingzhao Zhu ◽  
Shujin Lu ◽  
Mu Zhuang ◽  
Yangyong Zhang ◽  
Honghao Lv ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Gene Family ◽  
Brassica Oleracea ◽  
Downy Mildew ◽  
Fusarium Wilt ◽  
Black Spot ◽  
Housekeeping Genes ◽  
Chitinase Gene ◽  
Genome Wide ◽  
A Genome

Abstract Main conclusion Chitinase family genes were involved in the response of Brassica oleracea to Fusarium wilt, powdery mildew, black spot and downy mildew. Abstract Abstract Chitinase, a category of pathogenesis-related proteins, is believed to play an important role in defending against external stress in plants. However, a comprehensive analysis of the chitin-binding gene family has not been reported to date in cabbage (Brassica oleracea L.), especially regarding the roles that chitinases play in response to various diseases. In this study, a total of 20 chitinase genes were identified using a genome-wide search method. Phylogenetic analysis was employed to classify these genes into two groups. The genes were distributed unevenly across six chromosomes in cabbage, and all of them contained few introns (≤ 2). The results of collinear analysis showed that the cabbage genome contained 1–5 copies of each chitinase gene (excluding Bol035470) identified in Arabidopsis. The heatmap of the chitinase gene family showed that these genes were expressed in various tissues and organs. Two genes (Bol023322 and Bol041024) were relatively highly expressed in all of the investigated tissues under normal conditions, exhibiting the expression characteristics of housekeeping genes. In addition, under four different stresses, namely, Fusarium wilt, powdery mildew, black spot and downy mildew, we detected 9, 5, 8 and 8 genes with different expression levels in different treatments, respectively. Our results may help to elucidate the roles played by chitinases in the responses of host plants to various diseases.

Spectral characterization of fungal diseases downy mildew, powdery mildew, black-foot and Petri disease on Vitis vinifera leaves

2021 ◽  
Vol 42 (15) ◽  
pp. 5680-5697
Author(s):  
Pâmela A. Pithan ◽  
Jorge R. Ducati ◽  
Lucas R. Garrido ◽  
Diniz C. Arruda ◽  
Adriane B. Thum ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Vitis Vinifera ◽  
Downy Mildew ◽  
Fungal Diseases ◽  
Spectral Characterization ◽  
Petri Disease ◽  
Black Foot

scholarly journals Secondary Metabolites from Artemisia Genus as Biopesticides and Innovative Nano-Based Application Strategies

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 3061
Author(s):  
Bianca Ivănescu ◽  
Ana Flavia Burlec ◽  
Florina Crivoi ◽  
Crăița Roșu ◽  
Andreia Corciovă
Keyword(s):  
Secondary Metabolites ◽  
Negative Impact ◽  
Crop Protection ◽  
Duration Of Action ◽  
Worldwide Distribution ◽  
Artemisia Species ◽  
Extended Duration ◽  
Reduced Toxicity ◽  
Available Information ◽  
Potential Use

The Artemisia genus includes a large number of species with worldwide distribution and diverse chemical composition. The secondary metabolites of Artemisia species have numerous applications in the health, cosmetics, and food sectors. Moreover, many compounds of this genus are known for their antimicrobial, insecticidal, parasiticidal, and phytotoxic properties, which recommend them as possible biological control agents against plant pests. This paper aims to evaluate the latest available information related to the pesticidal properties of Artemisia compounds and extracts and their potential use in crop protection. Another aspect discussed in this review is the use of nanotechnology as a valuable trend for obtaining pesticides. Nanoparticles, nanoemulsions, and nanocapsules represent a more efficient method of biopesticide delivery with increased stability and potency, reduced toxicity, and extended duration of action. Given the negative impact of synthetic pesticides on human health and on the environment, Artemisia-derived biopesticides and their nanoformulations emerge as promising ecofriendly alternatives to pest management.

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

scholarly journals Exploring sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: a machine learning approach

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Tokodi ◽  
A Behon ◽  
E.D Merkel ◽  
A Kovacs ◽  
Z Toser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sex Differences ◽  
Cardiac Resynchronization Therapy ◽  
Cardiac Resynchronization ◽  
Funding Source ◽  
Resynchronization Therapy ◽  
Test Set ◽  
All Cause Mortality ◽  
Mortality Predictors

Abstract Background The relative importance of variables explaining sex differences in outcomes is scarcely explored in patients undergoing cardiac resynchronization therapy (CRT). Purpose We sought to implement and evaluate machine learning (ML) algorithms for the prediction of 1- and 3-year all-cause mortality in patients undergoing CRT implantation. We also aimed to assess the sex-specific differences and similarities in the predictors of mortality using ML approaches. Methods A retrospective registry of 2191 CRT patients (75% males) was used in the current analysis. ML models were implemented in 6 partially overlapping patient subsets (all patients, females or males with 1- or 3-year follow-up data available). Each cohort was randomly split into a training (80%) and a test set (20%). After hyperparameter tuning with 10-fold cross-validation in the training set, the best performing algorithm was also evaluated in the test set. Model discrimination was quantified using the area under the receiver-operating characteristic curves (AUC) and the associated 95% confidence intervals. The most important predictors were identified using the permutation feature importances method. Results Conditional inference random forest exhibited the best performance with AUCs of 0.728 [0.645–0.802] and 0.732 [0.681–0.784] for the prediction of 1- and 3-year mortality, respectively. Etiology of heart failure, NYHA class, left ventricular ejection fraction and QRS morphology had higher predictive power in females, whereas hemoglobin was less important than in males. The importance of atrial fibrillation and age increased, whereas the relevance of serum creatinine decreased from 1- to 3-year follow-up in both sexes. Conclusions Using advanced ML techniques in combination with easily obtainable clinical features, our models effectively predicted 1- and 3-year all-cause mortality in patients undergoing CRT implantation. The in-depth analysis of features has revealed marked sex differences in mortality predictors. These results support the use of ML-based approaches for the risk stratification of patients undergoing CRT implantation. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Research, Development and Innovation Office of Hungary

scholarly journals Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Jia-Rong Xiao ◽  
Pei-Che Chung ◽  
Hung-Yi Wu ◽  
Quoc-Hung Phan ◽  
Jer-Liang Andrew Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Powdery Mildew ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Cost Effective ◽  
Gray Mold ◽  
Leaf Blight ◽  
Crown Rot ◽  
Accuracy Rate ◽  
Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

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