Cultivation Condition Modeling for Plant Trouble Prevention
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Solution Overview
Problem
Current agricultural technologies lack an effective method to predict and prevent cultivation troubles in plant growth, such as blossom-end rot, by accurately analyzing environmental and plant conditions, leading to potential chemical usage after issues arise.
Innovation Solution
A cultivation assistance system that acquires and processes data on environmental and plant conditions, generates a Bayesian network model to predict trouble occurrences, and estimates optimal cultivation conditions to prevent issues without chemical intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a Bayesian network model is used to predict cultivation troubles, then the ability to prevent physiological troubles is improved, but the device complexity increases
Solution Approach 1:
The system segments the cultivation prediction problem into distinct modules: data acquisition unit, preprocessing unit, Bayesian network model generation unit, and estimation unit. Each module handles specific tasks independently, making the complex system manageable and maintainable while achieving reliable trouble prediction
Solution Approach 2:
The patent introduces a Bayesian network model as an intermediary between raw cultivation data and trouble prediction results. This probabilistic model acts as a mediator that processes multiple cultivation parameters and outputs reliable predictions about physiological troubles, bridging the gap between complex data and actionable insights
2Measurement precision
If data preprocessing and feature extraction are performed, then the measurement precision of cultivation conditions is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary data preprocessing and feature extraction before model generation. By preparing and organizing cultivation data in advance, the system improves measurement precision of cultivation conditions while reducing the time required during actual prediction operations
Solution Approach 2:
The patent replaces manual data analysis with automated preprocessing algorithms and feature extraction methods. This substitution of mechanical/manual processes with computational methods improves precision while efficiently managing processing time through algorithmic optimization
3Object-generated harmful factors
If the system estimates cultivation conditions to prevent troubles, then chemical usage is reduced, but the difficulty of detecting and measuring optimal conditions increases
Solution Approach 1:
The system implements feedback by continuously monitoring cultivation conditions and comparing them against the Bayesian network model's predictions. This feedback loop enables the system to detect and measure optimal conditions dynamically, adjusting recommendations to prevent physiological troubles while minimizing chemical usage
Solution Approach 2:
The patent utilizes parameter changes in the Bayesian network model to represent different cultivation scenarios. By adjusting and analyzing multiple cultivation parameters simultaneously, the system identifies optimal conditions that reduce chemical usage while maintaining plant health, making the detection and measurement of these conditions more manageable
Data Source
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AI summary
Provided is a cultivation assistance system including a cultivation condition acquisition unit configured to acquire a cultivation condition under which a plant is cultivated, a trouble acquisition unit configured to acquire a trouble occurrence situation in cultivation of the plant, a model generation unit configured to generate, by using the cultivation condition and the trouble occurrence situation, a model for predicting one of a cultivation condition or a trouble from the other, and an estimation unit configured to estimate, by using the model, a cultivation condition for suppressing occurrence of a trouble in cultivation of the plant. The cultivation assistance system includes a preprocessing unit to perform preprocessing on data of at least one of the cultivation condition or the trouble occurrence situation. The model generation unit is to generate, by using the preprocessed data, a model for predicting one of the cultivation condition or the trouble from the other.