Keywords
artificial neural networks, leaf and surface wetness, multi-scale data, crop disease risk, decision support systems
Start Date
1-7-2008 12:00 AM
Abstract
The risk of fungal and bacterial crop disease can be predicted using risk models with specific environmental parameters such as temperature, relative humidity, solar radiation, wind speed, and leaf wetness duration (LWD). LWD has long been recognized as key in the management of crop disease. Air temperature and wetness influence the majority of fungal plant diseases. Wetness also impacts insect populations, as well as pollution deposits. Many parameters are well understood, readily defined, and easily measured. Unfortunately, LWD is a complex phenomenon, due to its spatial and temporal variability within a crop canopy. The inconvenience and uncertainty associated with monitoring LWD at the local leaf scale and the complexity of upscaling to the crop level prevent existing disease risk models from being used with reliability. In spite of their imprecision, LW projections are already included in a number of online weather products. One nonparametric statistical approach receiving scant attention for the modeling of LWD is that of artificial neural networks (ANNs). In this work, two previously untried ANNs estimate this key environmental variable at local crop scales, using local and regional weather station data and site-specific sensing data. The first ANN combines two statistical methods to accomplish this spatial mapping (a K-nearest means classifier and a Bayesian classifier), while the recurrent nature of the second ANN provides a means of leveraging the temporal property of the data. The ultimate goal is to embed the ANN into a highly-portable tool, designed to predict leaf wetness duration as an SOC (system on a chip) in conjunction with local weather stations, and as input to real-time decision support systems.
Using Artificial Neural Networks to Predict Local Disease Risk Indicators with Multi-Scale Weather, Land and Crop Data
The risk of fungal and bacterial crop disease can be predicted using risk models with specific environmental parameters such as temperature, relative humidity, solar radiation, wind speed, and leaf wetness duration (LWD). LWD has long been recognized as key in the management of crop disease. Air temperature and wetness influence the majority of fungal plant diseases. Wetness also impacts insect populations, as well as pollution deposits. Many parameters are well understood, readily defined, and easily measured. Unfortunately, LWD is a complex phenomenon, due to its spatial and temporal variability within a crop canopy. The inconvenience and uncertainty associated with monitoring LWD at the local leaf scale and the complexity of upscaling to the crop level prevent existing disease risk models from being used with reliability. In spite of their imprecision, LW projections are already included in a number of online weather products. One nonparametric statistical approach receiving scant attention for the modeling of LWD is that of artificial neural networks (ANNs). In this work, two previously untried ANNs estimate this key environmental variable at local crop scales, using local and regional weather station data and site-specific sensing data. The first ANN combines two statistical methods to accomplish this spatial mapping (a K-nearest means classifier and a Bayesian classifier), while the recurrent nature of the second ANN provides a means of leveraging the temporal property of the data. The ultimate goal is to embed the ANN into a highly-portable tool, designed to predict leaf wetness duration as an SOC (system on a chip) in conjunction with local weather stations, and as input to real-time decision support systems.
