Computer‐aided design of improved warning systems – a case study for Botrytis control in flower bulbs*

Abstract

Development and improvement of warning systems are often done empirically, relying on extensive field testing. As this approach is both costly and time-consuming, there is a need for a more rational and efficient alternative. In a study to explore the options for improvement of a Botrytis warning system in flower bulbs, we applied a computer-based approach to systems design. The approach consisted of the construction and evaluation of modified versions of the warning system using epidemiological knowledge, data sets of recorded and forecast weather and a simulation model of epidemic development and fungicide spray impact. Performance of modified versions was evaluated with regard to fungicide input, efficacy of disease control and sensitivity to the prediction error in weather forecasts. This approach can be more efficient than a purely empirical one, as it enables the designer to limit the number of alternative versions to be field-tested on the basis of explicit performance criteria. It also has the advantage that it provides insight into the potentials for improvement of the warning system. IntroductionDisease warning or forecasting systems are used to improve the efficiency and efficacy of disease control, by timing fungicide application according to identified need. Often, an empirical approach is taken to the development and improvement of warning systems. Development involves the definition of predictive equations and decision rules, and choosing values for parameters and decision thresholds. These may be based on epidemiological relationships established in controlled experiments or correlations derived from field observations, but they usually also include arbitrary elements, e.g. the choice of an action threshold. Subsequently, the warning system is evaluated in field experiments, commonly over several seasons, and several cycles of modification and field testing may follow before acceptable or improved performance is achieved. In consequence, this empirical approach requires a large research investment and is very time-consuming. Design of warning systems could become more efficient if alternative versions of the warning system could be screened with regard to their performance before field evaluation. Field testing could then be restricted to the most promising alternatives. In a study to explore the options for the improvement of a Botrytis warning system in flower bulbs, we applied such a screening technique in the form of computer-based evaluation. In this paper, we will describe the current weather-based Botrytis warning system and its *Paper presented at the EPPO Conference on Warning Services for Plant Protection, Piacenza (IT), 1999-05-12/14, background, explain the methodology we used, present first results and, finally, discuss various aspects of our approach.

Staat van Plantgezondheid 2023 : In de Nederlandse Land- en tuinbouw

“…In relatie tot Botrytis preventie zijn er voor de teelt van tulp, lelie en gladiool waarschuwingssystemen ontwikkeld (de Kraker, van den Ende, Rossing, Bastiaansen, & van der Werf, 2000;Van Den Ende, Pennock-Vos, Bastiaansen, Koster, & Van Der Meer, 2000). Aan de hand van de weersvoorspelling wordt de infectiekans door B. tulipae, B. elliptica en B. gladiolorum in respectievelijk tulp, lelie en gladiool voor de komende dagen voorspelt.…”

Establishment of warning system of greenhouse rose diseases based on visual C++

“…At present,the system has already formed a mature system(an expert system or prediction system)which can predict the occurrence of plant diseases at home and abroad.It can reduce the loss caused by the diseases,so as to achieve the purpose of prevention [1][2][3][4][5] .But there have been no systematic reports on prediction system of greenhouse rose diseases at home and abroad.Through researching methods and findings of applications to plant diseases,we can get the conclusion:In predicting plant diseases,we usually use statistical methods or establish the experts' disease system which is related expert experience and knowledge of the disease expert system to establish the prediction [6] .According to the experimental data of greenhouse rose diseases and meteorological data from automatic weather stations,we build up greenhouse rose diseases prediction model which is based on genetic algorithm [7][8][9] and neural network [10][11] ,then get the accurate index of disease inflection rate of rose diseases.Users can have a convenient and quick operation, and take effective measures to prevent the occurrence of diseases.…”

Development of Botrytis cinerea under reduction of pesticides treatments in Macedonian viticulture production

“…The species of the genus Botrytis are word widespread and are developing in the different climatic zone, hence the approach to making warning systems was different according to the microclimatic facts on the field. Newly developed warning systems should be evaluated in field experiments, by comparing their recommendations with the conventional management practice [13]. Such knowledge was accumulated over the years and the first attempts for developing warning systems for management of Botrytis-incited diseases were made in the 1960s-1970s [12]; [8].…”