Volatile Organic Compounds (VOCs), Phytoecdysteroids (PHYs) and Other Modern Solutions as a Promising Strategy in Modern Pest and Disease Management: Obvious Benefits and Risks

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 2900

Special Issue Editor

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Department of Biology and Plant Protection, Faculty of Agriculture and Biotechnology, UTP University of Science and Technology, 7 Prof. S. Kaliskiego Ave, 85-796 Bydgoszcz, Poland
Interests: insects; protection of plants against biotic and abiotic stress; plant priming; volatile organic compounds released as a defense reaction against pests and plant pathogens; attractants and repellants for insects; phytoecdysteroids; bioinsecticides; benefits and risks
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Special Issue Information

Dear Colleagues,

Protecting crops against insects/plant pathogens using environmental-friendly practices is a growing concern in Europe. A number of problems have arisen in the past due to the conventional pesticide treatment e.g. repeated and unfocussed use, selecting insect strains resistant to entire families of insecticide molecules, and contaminating and targeting beneficial insects. Genetically Modified Organisms (GMO) initially meant to replace insecticides, also failed short to receive acceptation from the end-user, at least in Europe. The question arises, if not pesticides and GMOs, what methods will be used to protect plants in agricultural production.

The co-evolution of plants and insects has resulted in a wide array of chemical plant defenses that effectively reduce damage caused by feeding herbivores or plant pathogens. Plants respond to insect-inflicted injury, pathogen infestation or elicitors by systemically releasing relatively large amounts of VOCs and PHYs, what makes them promising candidates for the development of an environmentally safe approach to crop protection. Most of the plant species have the genetic ability to produce VOCs and PHYs, but the biosynthetic pathway is not active and still not recognized.

On the other hand, the pleasant smell of plants caused by VOCs maybe, by coincidence, dangerous to human life. Excessive consumption of non-poisonous fruits or plants (which are associated with a pleasant smell) can, under certain circumstances, lead to disease.

Prof. Dr. Dariusz Piesik
Guest Editor

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  • volatile organic compounds (VOCs)
  • phytoecdysteroids (PHYs)
  • plant priming
  • eco-friendly pesticides
  • natural defense system of plants
  • benefits and risks

Published Papers (1 paper)

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14 pages, 1612 KiB  
Fused-Deep-Features Based Grape Leaf Disease Diagnosis
by Yun Peng, Shengyi Zhao and Jizhan Liu
Agronomy 2021, 11(11), 2234; https://doi.org/10.3390/agronomy11112234 - 4 Nov 2021
Cited by 12 | Viewed by 2240
Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a [...] Read more.
Rapid and accurate grape leaf disease diagnosis is of great significance to its yield and quality of grape. In this paper, aiming at the identification of grape leaf diseases, a fast and accurate detection method based on fused deep features, extracted from a convolutional neural network (CNN), plus a support vector machine (SVM) is proposed. In the research, based on an open dataset, three types of state-of-the-art CNN networks, three kinds of deep feature fusion methods, seven species of deep feature layers, and a multi-class SVM classifier were studied. Firstly, images were resized to meet the input requirements of the CNN network; then, the deep features of the input images were extracted via the specific deep feature layer of the CNN network. Two kinds of deep features from different networks were then fused using different fusion methods to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused deep features. The experimental results on the open dataset show that the fused deep features with any kind of fusion method can obtain a better classification performance than using a single type of deep feature. The direct concatenation of the Fc1000 deep feature extracted from ResNet50 and ResNet101 can achieve the best classification result compared with the other two fusion methods, and its F1 score is 99.81%. Furthermore, the SVM classifier trained using the proposed method can achieve a classification performance comparable to that of using the CNN model directly, but the training time is less than 1 s, which has an advantage over spending tens of minutes training a CNN model. The experimental results indicate that the method proposed in this paper can achieve fast and accurate identification of grape leaf diseases and meet the needs of actual agricultural production. Full article
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