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This review focuses on the detection of diseased plants by analysis of volatile organic compound (VOC) emissions. It includes an overview of studies that report on the impact of infectious and noninfectious diseases on these emissions and discusses the specificity of disease-induced emissions. The review also provides an overview of processes that affect the gas balance of plant volatiles, including their loss processes. These processes are considered as important because they contribute to the time-dynamic concentration profiles of plant-emitted volatiles. In addition, we describe the most popular techniques currently in use to measure volatiles emitted from plants, with emphasis on agricultural application. Dynamic sampling coupled with gas chromatography and followed by an appropriate detector is considered as the most appropriate method for application in agriculture. It is recommended to evaluate the state-of-the-art in the fields concerned with this method and to explore the development of a new instrument based on the specific needs for application in agricultural practice. However, to apply such an instrument in agriculture remains a challenge, mainly due to high costs. [ABSTRACT FROM AUTHOR]

Plant disease cycles represent pathogen biology as a series of interconnected stages of development including dormancy, reproduction, dispersal, and pathogenesis. The progression through these stages is determined by a continuous sequence of interactions among host, pathogen, and environment. The stages of the disease cycle form the basis of many plant disease prediction models. The relationship of temperature and moisture to disease development and pathogen reproduction serve as the basis for most contemporary plant disease prediction systems. Pathogen dormancy and inoculum dispersal are considered less frequently. We found extensive research efforts evaluating the performance of prediction models as part of operation disease management systems. These efforts appear to be greater than just a few decades ago, and include novel applications of Bayesian decision theory. Advances in information technology have stimulated innovations in model application. This trend must accelerate to provide the disease management strategies needed to maintain global food supplies. [ABSTRACT FROM AUTHOR]

Many disease resistance (R) proteins of plants detect the presence of disease-causing bacteria, viruses, or fungi by recognizing specific pathogen effector molecules that are produced during the infection process. Effectors are often pathogen proteins that probably evolved to subvert various host processes for promotion of the pathogen life cycle. Five classes of effector-specific R proteins are known, and their sequences suggest roles in both effector recognition and signal transduction. Although some R proteins may act as primary receptors of pathogen effector proteins, most appear to play indirect roles in this process. The functions of various R proteins require phosphorylation, protein degradation, or specific localization within the host cell. Some signaling components are shared by many R gene pathways whereas others appear to be pathway specific. New technologies arising from the genomics and proteomics revolution will greatly expand our ability to investigate the role of R proteins in plant disease resistance. [ABSTRACT FROM AUTHOR]

The article presents information on genetics of disease resistance in plants. Most plant diseases arv infectious. They result horn the interaction of two organisms, the host plant which is the stiscept of the disease and the pathogen which is the causal agent of the disease. A wide array of pathogens cause disease. These include many fungi, bacteria, viruses, mycoplasma, nematodes, and parasitic phanerogams. Several ahiotic environmental Factors can also cause plant disease. Disease resistance may be partial or nearly complete. Comptete susceptibility of a plant to a pathogen is rare. In such hosts, nothing impedes pathogen development throughout the disease cycle.