Predictive Models for Plant Disease Assessment

Abstract

AbstractThe world’s increasing population demands more food commodities to fulfil the nutritional requirements. Increasing cultivation area and crop production and reducing yield losses will be required to meet food demand. Plants in gardens, forests, production fields, and landscapes are subjected to numerous health problems that cause yield reduction. Disease negativity in yield loss can be tackled by adopting appropriate management practices. Plant disease management requires diagnosis and severity assessment. Plant disease modeling is an important field for assessing the intensity or severity of the disease. It is a management system enabled to forecast the occurrence and any change in severity or intensity of plant diseases. Forecasting the occurrence of diseases under a specific area and time; however, appropriate preventive and control measures can be taken in advance to obtain potential yield. Applying management practices at the appropriate time reduces the wastage of crops and chemicals by forecasting disease and making it cost-effective. Basic components of plant disease are needed to investigate to make disease forecasting schemes. There are seven basic requirements for successful disease prediction. Intensity, incidence, and severity are primary disease measuring terms used to study disease forecasting. Modeling involves field observations, disease measurements, and weather conditions favoring disease spread, mathematical formulas, and computer use. The mathematical and analytical relationship gives information about interaction among the host, pathogen, and weather variables in mathematical equations presented as simple statements, tables, or graphs. Disease prediction can be made based on parameters involved, i.e., inoculum, weather variables, and comparative information. Computer simulation is beneficial for growers to understand the effect of components and subcomponents of the epidemic on yield loss. Many prediction schemes have been published.KeywordsPlant disease forecastingElements of plant disease predictionComputer simulationDisease predictive models