Computational Model for Plant Disease Probability Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural plant disease management is challenging due to the complex interplay of host plant vulnerabilities, disease-causing pathogens, and environmental conditions, leading to inefficiencies in determining the appropriate time and quantity of plant protection measures, especially with rising population demands and climate changes.
Innovation Solution
A computer-implemented method using a computational model that predicts disease probability based on plant observation and weather data, determining optimal plant protection treatment parameters, such as timing and quantity of plant protection agents, through a distributed computer system that can adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If plant protection measures are applied frequently to ensure yield protection, then disease control effectiveness is improved, but resource consumption and environmental impact increase
Solution Approach 1:
The system performs preliminary disease risk assessment by analyzing weather data, plant observation data, and historical disease patterns before disease occurrence. This allows proactive scheduling of plant protection measures at optimal times rather than reactive application, reducing overall agent consumption while maintaining yield protection
Solution Approach 2:
The system continuously monitors plant health status, weather conditions, and disease development through sensors and imaging systems. This real-time feedback enables dynamic adjustment of plant protection timing and dosage, applying agents only when and where needed based on actual disease pressure rather than fixed schedules
2Measurement precision
If computational models are made complex to accurately predict disease probability, then prediction accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The computational model is divided into modular components: weather data processing module, plant observation data analysis module, historical disease pattern recognition module, and disease probability calculation module. Each module handles specific data types and computations independently, improving accuracy through specialized processing while reducing overall system complexity through modular architecture
Solution Approach 2:
The computational model is designed to handle multiple disease types, plant species, and environmental conditions using a unified framework. This multi-functional approach achieves high prediction accuracy across diverse agricultural scenarios without requiring separate complex models for each case, thereby controlling device complexity
Data Source
AI summary
The present application provides a method for determining a plant protection treatment plan of an agricultural plant, the method carried out by a data processing unit (111), and the method comprising the steps of: obtaining (S110), by the data processing unit, plant observation data indicative for a current state of health of the agricultural plant or of a reference plant, obtaining (S120), by the data processing unit, weather data associated with a location at which the agricultural plant is cultivated, predicting (S130), by a computational model (113) executed by the data processing unit, based on the obtained observation data and the obtained weather data, a time-related disease probability of the agricultural plant, and determining (S140), by the computational model (113), based on at least the predicted disease probability, at least one plant protection treatment parameter to be included in the plant protection treatment plan.