Weather‐data‐based model: an approach for forecasting leaf and stripe rust on winter wheat

Abstract

Classification and regression trees (CARTs) for data analysis, an hourly weather dataset, and a 3 year field incidence and severity dataset of winter wheat rust were integrated to forecast pathogens’ presence/absence. The field dataset of incidence and severity was collected for three production cycles. Measured records of 88 Automatic Meteorological Stations and the indirect weather dataset generated in the Weather Research and Forecasting environment interpolated to each Automatic Meteorological Station location were analysed in the Python ecosystem. The focal point of the analysis was the severity of the disease. The analysis of direct weather data revealed the association of leaf rust severity with a night temperature of <14.25°C and global radiation of <521.67 W·m–2, while the estimated dataset showed that its severity is better explained by the dew point temperature of <13.7°C and a mean temperature of <19.06°C. The direct dataset also indicated that stripe rust severity was associated with relative humidity of <88.73%, global radiation of <597.39 W·m–2 and dew point temperature of <16.09°C, whereas the estimated data revealed that pathogen severity is better explained by a model composed of a dew point temperature of <14.6°C, night temperature of <20.4°C and a maximum temperature of <27.9°C. The severity and intensity analysis indicated the pathogen's preference for non‐dry ambient conditions and the preference of stripe rust pathogen for humid and warmer temperatures than leaf rust. The weather thresholds of both pathogens, and CART analysis, unveiled that winter wheat rust can be forecasted. This constitutes the foundation of a more efficient extension programme based on the internet of things.

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

“…Moreover, hot summers and dry weather conditions are the least conducive to infections by P. striiformis. Above 25 • C, the sporulation is halted and for temperatures greater than 29 • C the pathogen dies [43,46]. Although the threshold-based weather model satisfactorily predicts infection events by P. striiformis under Luxembourgish conditions (probabilities of detection ≥0.90; false alarm ratios ≤0.38 on average, and critical success indices ranging from 0.63 to 1) [43], the missed infection events indicate that additional ground truthing is needed for some locations.…”

MISSR: A Mentoring Interactive System for Stripe Rust

“…Collectively, these findings highlighted the importance of integration of weather data within the newly developed DEWS. In 2020, Rodríguez-Moreno and others proposed a weather-data-based model for forecasting leaf and stripe rust on winter wheat [42]. This model used classification and regression trees (CARTs) for data analysis, an hourly weather dataset, and a three-year field disease severity survey of winter wheat rust to forecast pathogens' presence/absence [42].…”

“…In 2020, Rodríguez-Moreno and others proposed a weather-data-based model for forecasting leaf and stripe rust on winter wheat [42]. This model used classification and regression trees (CARTs) for data analysis, an hourly weather dataset, and a three-year field disease severity survey of winter wheat rust to forecast pathogens' presence/absence [42]. In the current study, we modeled the effects of meteorological parameters (particularly maximum temperature and relative humidity), virulence pattern of stripe-rust races, and three-year final stripe-rust severity of 17 Egyptian wheat to develop an easy-to-use, efficient, cost-effective DEWS based on the Internet of Things.…”

Towards intelligent and integrated pest management through an AIoT‐based monitoring system

“…[13][14][15][16][17][18] The incidence of plant diseases is also identified and predicted using AI. 6,[19][20][21] AI combined with IoT forms a more powerful concept called Artificial Intelligence of Things (AIoT). In AIoT, 'things' such as devices and machines work together while AI enables each device to perform smarter functions such as image recognition and object detection.…”

“…According to Høye et al , 12 deep learning and computer vision can revolutionize entomology; this was realized in past works as AI was used for automatically detecting and recognizing insect pests, forecasting insect pest populations, and more 13–18 . The incidence of plant diseases is also identified and predicted using AI 6,19–21 . AI combined with IoT forms a more powerful concept called Artificial Intelligence of Things (AIoT).…”