Development of Gray Mold of Poinsettia and Powdery Mildew of Begonia and Rose Under Split Night Temperatures

Plant Disease ◽  
1982 ◽  
Vol 66 (1) ◽  
pp. 776 ◽  
Author(s):  
B. Sammons
Keyword(s):  
Powdery Mildew ◽  
Gray Mold

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.

scholarly journals Bacillus sp. BS061 Suppresses Gray Mold and Powdery Mildew through the Secretion of Different Bioactive Substances

Mycobiology ◽  
2013 ◽  
Vol 41 (3) ◽  
pp. 164-166 ◽  
Author(s):  
Young-Sook Kim ◽  
Ja-Gyeong Song ◽  
In-Kyoung Lee ◽  
Woon-Hyung Yeo ◽  
Bong-Sik Yun
Keyword(s):  
Powdery Mildew ◽  
Gray Mold ◽  
Bacillus Sp ◽  
Bioactive Substances

scholarly journals Bicarbonates and Botrytis: VI. Control of Gray Mold Enhances Geranium Growth

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 767A-767
Author(s):  
C.L. Palmer ◽  
R.W. Langhans ◽  
R.K. Horst ◽  
H.W. Israel
Keyword(s):  
Powdery Mildew ◽  
Plant Height ◽  
Disease Incidence ◽  
Dry Weight ◽  
Colony Growth ◽  
Gray Mold ◽  
Disease Development ◽  
Development Data ◽  
Low Levels

Botrytis cinerea Pers. causes gray mold on greenhouse-grown geraniums (Pelargonium ×hortorum L. H. Bailey), among many other crops. Bicarbonates effectively control rose powdery mildew (Plant Dis. 76:247–480) and inhibit B. cinerea in vitro colony growth and conidial germination (Phytopathology 84:546, 1065). To examine bicarbonate effects on gray mold incidence and geranium growth, we sprayed seedling geranium cultivars Red Elite and Scarlet Elite weekly with 0, 25, and 50 mM NH4HCO3 or KHCO3. Seedlings were transplanted in Metromix 360 and misted every 24 m for 5 s to enhance disease development. Data were collected biweekly on disease incidence, floral number, plant height, and dry weight. Both cultivars performed similarly. Disease incidence decreased with application of bicarbonates. KHCO3 at 25 mM slightly increased dry weight and height over 0 mM, whereas 25 and 50 mM NH4HCO3 greatly increased both features. Fifty mM KHCO3 decreased height slightly, but had no effect on dry weight. Floral number decreased slightly with all bicarbonate treatments. It is indicated that KHCO3 at low levels and NH4HCO3 enhance seedling geranium growth by controlling gray mold incidence and by providing additional nutrients. (Supported by H&I Agritech Inc., Ithaca, NY 14850.)

scholarly journals Bacillussp. BS061 Suppresses Powdery Mildew and Gray Mold

Mycobiology ◽  
2013 ◽  
Vol 41 (2) ◽  
pp. 108-111 ◽  
Author(s):  
Young-Sook Kim ◽  
Ja-Gyeong Song ◽  
In-Kyoung Lee ◽  
Woon-Hyung Yeo ◽  
Bong-Sik Yun
Keyword(s):  
Powdery Mildew ◽  
Gray Mold

scholarly journals Effect of Microbial Agent on Control of Tomato Gray mold and Powdery mildew

2012 ◽  
Vol 16 (4) ◽  
pp. 364-368 ◽  
Author(s):  
Tack-Soo Kim ◽  
Min-Jung Ko ◽  
Se-Weon Lee ◽  
Ji Hee Han ◽  
Kyungseok Park ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Gray Mold ◽  
Microbial Agent

scholarly journals Impact of UV-C Radiation Applied during Plant Growth on Pre- and Postharvest Disease Sensitivity and Fruit Quality of Strawberry

Plant Disease ◽  
2020 ◽  
Vol 104 (12) ◽  
pp. 3239-3247 ◽  
Author(s):  
M. Forges ◽  
M. Bardin ◽  
L. Urban ◽  
J. Aarrouf ◽  
F. Charles
Keyword(s):  
Powdery Mildew ◽  
Plant Growth ◽  
Fruit Quality ◽  
Natural Infection ◽  
Soft Rot ◽  
Gray Mold ◽  
Postharvest Disease ◽  
Flowering Initiation ◽  
Ultraviolet C ◽  
Uv C

Ultraviolet-C (UV-C) radiation is efficient in reducing the development of diseases in many species, including strawberry (Fragaria × ananassa). Several studies suggest that UV-C radiation is effective not only because of its disinfecting effect but also because it may stimulate plant defenses. In this study, the effect of preharvest UV-C radiation applied during strawberry cultivation on plant growth, fruit quality, and susceptibility to major fungal diseases such as gray mold, powdery mildew, and soft rot was evaluated. UV-C treatments had an impact on flowering initiation and fruit development. Flowering occurred earlier for UV-C-treated plants than for nontreated plants. At harvest, a larger amount of fruit was produced by treated plants despite their slight decrease in leaf area. UV-C treatment did not improve strawberry shelf life but did not alter the physical integrity of strawberry fruit. Natural infection of leaves to powdery mildew and of fruit to Rhizopus spp. strongly decreased in response to UV-C treatment.

scholarly journals Induction of Systemic Resistance in Plants by Biochar, a Soil-Applied Carbon Sequestering Agent

2010 ◽  
Vol 100 (9) ◽  
pp. 913-921 ◽  
Author(s):  
Yigal Elad ◽  
Dalia Rav David ◽  
Yael Meller Harel ◽  
Menahem Borenshtein ◽  
Hananel Ben Kalifa ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Fungal Pathogens ◽  
Nutrient Retention ◽  
Crop Productivity ◽  
Gray Mold ◽  
Systemic Resistance ◽  
Polyphagotarsonemus Latus ◽  
Potting Medium ◽  
Broad Mite

Biochar is the solid coproduct of biomass pyrolysis, a technique used for carbon-negative production of second-generation biofuels. The biochar can be applied as a soil amendment, where it permanently sequesters carbon from the atmosphere as well as improves soil tilth, nutrient retention, and crop productivity. In addition to its other benefits in soil, we found that soil-applied biochar induces systemic resistance to the foliar fungal pathogens Botrytis cinerea (gray mold) and Leveillula taurica (powdery mildew) on pepper and tomato and to the broad mite pest (Polyphagotarsonemus latus Banks) on pepper. Levels of 1 to 5% biochar in a soil and a coconut fiber-tuff potting medium were found to be significantly effective at suppressing both diseases in leaves of different ages. In long-term tests (105 days), pepper powdery mildew was significantly less severe in the biochar-treated plants than in the plants from the unamended controls although, during the final 25 days, the rate of disease development in the treatments and controls was similar. Possible biochar-related elicitors of systemic induced resistance are discussed.

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. 

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