About: Plant disease is a research topic. Over the lifetime, 9781 publications have been published within this topic receiving 222484 citations.
Papers published on a yearly basis
16 Jan 1990
TL;DR: This paper presents the results of a two-year study into the design and modelling of Spatial Pattern Simulation Models of Plant Disease Designing Experiments and Sampling Crop Loss Assessment and Modelling Forecasting Plant Diseases.
Abstract: Epidemics and Plant Disease Epidemiology Monitoring Epidemics: Host Monitoring Epidemics: Environment Monitoring Epidemics: Pathogen Monitoring Epidemics: Disease Modeling and Data Analysis Temporal Analysis of Epidemics I Temporal Analysis of Epidemics Spatial Aspects of Plant Disease Epidemics I: Dispersal Gradients of Long Range Transport Spatial Aspects of Plant Disease Epidemics II: Analysis of Spatial Pattern Simulation Models of Plant Disease Designing Experiments and Sampling Crop Loss Assessment and Modelling Forecasting Plant Diseases.
TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.
Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
TL;DR: Rhizobacteria-mediated induced systemic resistance (ISR) is effective under field conditions and offers a natural mechanism for biological control of plant disease.
Abstract: Nonpathogenic rhizobacteria can induce a systemic resistance in plants that is phenotypically similar to pathogen-induced systemic acquired resistance (SAR). Rhizobacteria-mediated induced systemic resistance (ISR) has been demonstrated against fungi, bacteria, and viruses in Arabidopsis, bean, carnation, cucumber, radish, tobacco, and tomato under conditions in which the inducing bacteria and the challenging pathogen remained spatially separated. Bacterial strains differ in their ability to induce resistance in different plant species, and plants show variation in the expression of ISR upon induction by specific bacterial strains. Bacterial determinants of ISR include lipopolysaccharides, siderophores, and salicylic acid (SA). Whereas some of the rhizobacteria induce resistance through the SA-dependent SAR pathway, others do not and require jasmonic acid and ethylene perception by the plant for ISR to develop. No consistent host plant alterations are associated with the induced state, but upon challenge inoculation, resistance responses are accelerated and enhanced. ISR is effective under field conditions and offers a natural mechanism for biological control of plant disease.
TL;DR: The different structural traits and physico-chemical properties of these effective surface- and membrane-active amphiphilic biomolecules explain their involvement in most of the mechanisms developed by bacteria for the biocontrol of different plant pathogens.
TL;DR: In the case of metal anions, the metal cations can be adsorbed by chelation on amine groups of chitosan in near neutral solutions as discussed by the authors.
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