scholarly journals Determination of Specific Parameters for Early Detection of Botrytis cinerea in Lettuce

Horticulturae ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 23
Author(s):  
Viktorija Vaštakaitė-Kairienė ◽  
Neringa Rasiukevičiūtė ◽  
Lina Dėnė ◽  
Simona Chrapačienė ◽  
Alma Valiuškaitė
Keyword(s):  
Early Detection ◽  
Botrytis Cinerea ◽  
Spectral Reflectance ◽  
Crop Production ◽  
Plant Response ◽  
Plant Diseases ◽  
Economic Losses ◽  
Detection And Identification ◽  
Leaf Spectral Reflectance ◽  
Reflectance Indices

In horticulture, the demand for efficient farming processes and food industries increases rapidly. Plant diseases cause severe crop production and economic losses. Therefore, early detection and identification of the diseases in plants are critical. This study aimed to determine the specific parameters for early detection of Botrytis cinerea in lettuce. The lettuce “Little Gem” was inoculated with B. cinerea isolate spore suspension and disc to evaluate the plant response to inner and outer infection, respectively. The non-destructive measurements of leaf spectral reflectance indices and biochemical compounds (phenols, proteins, DPPH, FRAP, chlorophyll, and carotenoids) were used to evaluate the plant physiological response to inoculation with B. cinerea after 12, 18, 36, 60, and 84 h. Our data showed that lettuce responded differently to inner and outer inoculation with B. cinerea. Therefore, the findings of this study allow for the inoculation method to be chosen to determine the early plant response to infection with B. cinerea according to specific leaf spectral reflectance indexes and phytochemicals in further research.

Plant disease detection by hyperspectral imaging: from the lab to the field

2017 ◽  
Vol 8 (2) ◽  
pp. 238-243 ◽  
Author(s):  
A-K. Mahlein ◽  
M. T. Kuska ◽  
S. Thomas ◽  
D. Bohnenkamp ◽  
E. Alisaac ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Crop Production ◽  
Disease Incidence ◽  
Resistance Mechanism ◽  
Accurate Estimate ◽  
Plant Diseases ◽  
Negative Effects ◽  
Detection And Identification ◽  
Measuring Scale ◽  
Yield Quality

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

scholarly journals GISTECHNOLOGY FOR THE MONITORING OF SHARKA DISEASE IN THE ODESSA REGION

2016 ◽  
Vol 72 (2) ◽  
pp. 28-31
Author(s):  
S. Pavlova ◽  
O. Stakhurska ◽  
I. Budzanivska ◽  
V. Polischuk
Keyword(s):  
Crop Production ◽  
Plant Diseases ◽  
Economic Losses ◽  
Plum Pox Virus ◽  
Gis Technology ◽  
Considerable Effort ◽  
Virus Diseases ◽  
Fruit Cultivation ◽  
First Time ◽  
Sharka Disease

Plant virus causes many important plant diseases and are responsible for huge losses in crop production and quality in all parts of the world, and consequently, agronomists and plant pathologists have devoted considerable effort toward controlling virus diseases. One the most important virus on many Prunus species, causing great economic losses is Plum pox virus (PPV),casual agent of Sharka disease. Since its discovery, Sharka has been considered as a calamity in stone orchards. The virus has been detected in almost every country where any significant commercial stone fruit cultivation occurs [1]. The virus is entered into the list of regulated pests common in Ukraine. In Ukraine, the total area of PPV spread totals 4013,2764 ha. In Odessa region, 18.5 ha districts are in PPV quarantine. Six hotbeds of PPV infection totalling 28 hectares were found in Odessa region. For the first time in Odessa region, PPV was found on cherry trees. Peach and plum trees are hit equally. In this study, we use geographic information systems technology to identify potential locations in a Odessa region for controlling the spread of Plum pox virus. To our knowledge, this is the first attempt to employ GIS technology for controlling plant diseases in Ukraine. Provided it is properly maintained, the geospatial data, and the ability to generate detailed maps with it, is key to the success of PPV containment. Information management will be a key to improving for controlling the spread of Plum pox virus.

scholarly journals Effectors of Plant Necrotrophic Fungi

2021 ◽  
Vol 12 ◽  
Author(s):  
Dandan Shao ◽  
Damon L. Smith ◽  
Mehdi Kabbage ◽  
Mitchell G. Roth
Keyword(s):  
Fungal Pathogens ◽  
Crop Production ◽  
Plant Diseases ◽  
Economic Losses ◽  
Plant Colonization ◽  
Genetic Intervention ◽  
Necrotrophic Fungi ◽  
Plant Pathogen Interaction ◽  
Functional Context ◽  
Necrotrophic Effectors

Plant diseases caused by necrotrophic fungal pathogens result in large economic losses in field crop production worldwide. Effectors are important players of plant-pathogen interaction and deployed by pathogens to facilitate plant colonization and nutrient acquisition. Compared to biotrophic and hemibiotrophic fungal pathogens, effector biology is poorly understood for necrotrophic fungal pathogens. Recent bioinformatics advances have accelerated the prediction and discovery of effectors from necrotrophic fungi, and their functional context is currently being clarified. In this review we examine effectors utilized by necrotrophic fungi and hemibiotrophic fungi in the latter stages of disease development, including plant cell death manipulation. We define “effectors” as secreted proteins and other molecules that affect plant physiology in ways that contribute to disease establishment and progression. Studying and understanding the mechanisms of necrotrophic effectors is critical for identifying avenues of genetic intervention that could lead to improved resistance to these pathogens in plants.

scholarly journals Endophytic Bacteria Bacillus subtilis, Isolated from Zea mays, as Potential Biocontrol Agent against Botrytis cinerea

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 492
Author(s):  
Hernando José Bolivar-Anillo ◽  
Victoria E. González-Rodríguez ◽  
Jesús M. Cantoral ◽  
Darío García-Sánchez ◽  
Isidro G. Collado ◽  
...  
Keyword(s):  
Zea Mays ◽  
Bacillus Subtilis ◽  
Botrytis Cinerea ◽  
Endophytic Bacteria ◽  
Biochemical Properties ◽  
Plant Diseases ◽  
Phytopathogenic Fungus ◽  
Economic Losses

Plant diseases are one of the main factors responsible for food loss in the world, and 20–40% of such loss is caused by pathogenic infections. Botrytis cinerea is the most widely studied necrotrophic phytopathogenic fungus. It is responsible for incalculable economic losses due to the large number of host plants affected. Today, B. cinerea is controlled mainly by synthetic fungicides whose frequent application increases risk of resistance, thus making them unsustainable in terms of the environment and human health. In the search for new alternatives for the biocontrol of this pathogen, the use of endophytic microorganisms and their metabolites has gained momentum in recent years. In this work, we isolated endophytic bacteria from Zea mays cultivated in Colombia. Several strains of Bacillus subtilis, isolated and characterized in this work, exhibited growth inhibition against B. cinerea of more than 40% in in vitro cultures. These strains were characterized by studying several of their biochemical properties, such as production of lipopeptides, potassium solubilization, proteolytic and amylolytic capacity, production of siderophores, biofilm assays, and so on. We also analyzed: (i) its capacity to promote maize growth (Zea mays) in vivo, and (ii) its capacity to biocontrol B. cinerea during in vivo infection in plants (Phaseolus vulgaris).

scholarly journals Microbial Biocontrol as an Alternative to Synthetic Fungicides: Boundaries between Pre- and Postharvest Applications on Vegetables and Fruits

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 60
Author(s):  
Vincenzo Michele Sellitto ◽  
Severino Zara ◽  
Fabio Fracchetti ◽  
Vittorio Capozzi ◽  
Tiziana Nardi
Keyword(s):  
Crop Production ◽  
Fruits And Vegetables ◽  
Control Plant ◽  
Agricultural Practices ◽  
Plant Diseases ◽  
Postharvest Quality ◽  
Crucial Point ◽  
Production Chain ◽  
Vegetables And Fruits ◽  
And Storage

From a ‘farm to fork’ perspective, there are several phases in the production chain of fruits and vegetables in which undesired microbial contaminations can attack foodstuff. In managing these diseases, harvest is a crucial point for shifting the intervention criteria. While in preharvest, pest management consists of tailored agricultural practices, in postharvest, the contaminations are treated using specific (bio)technological approaches (physical, chemical, biological). Some issues connect the ‘pre’ and ‘post’, aligning some problems and possible solution. The colonisation of undesired microorganisms in preharvest can affect the postharvest quality, influencing crop production, yield and storage. Postharvest practices can ‘amplify’ the contamination, favouring microbial spread and provoking injures of the product, which can sustain microbial growth. In this context, microbial biocontrol is a biological strategy receiving increasing interest as sustainable innovation. Microbial-based biotools can find application both to control plant diseases and to reduce contaminations on the product, and therefore, can be considered biocontrol solutions in preharvest or in postharvest. Numerous microbial antagonists (fungi, yeasts and bacteria) can be used in the field and during storage, as reported by laboratory and industrial-scale studies. This review aims to examine the main microbial-based tools potentially representing sustainable bioprotective biotechnologies, focusing on the biotools that overtake the boundaries between pre- and postharvest applications protecting quality against microbial decay.

scholarly journals AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3830
Author(s):  
Ahmad Almadhor ◽  
Hafiz Tayyab Rauf ◽  
Muhammad Ikram Ullah Lali ◽  
Robertas Damaševičius ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Color Difference ◽  
Plant Diseases ◽  
Economic Losses ◽  
Production Loss ◽  
Postharvest Diseases ◽  
Tropical Regions ◽  
Guava Fruit ◽  
Tree Classifier

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

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