Partial-Region Detection Correction for Agricultural Field Adaptation
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Solution Overview
Problem
Existing agricultural prediction systems face challenges in accurately predicting yields and harvest times due to variations in environmental conditions and the need for extensive manual annotation and adjustment, especially when introduced to new farm fields.
Innovation Solution
An information processing apparatus and method that utilizes a learning model to detect partial regions of interest in agricultural images, allowing for user input to correct detection results, thereby generating learning data efficiently without extensive manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a prediction system is introduced in a new farm field, then prediction capability is provided, but prediction accuracy deteriorates due to different field conditions compared to original training data
Solution Approach 1:
The system performs preliminary actions by collecting images and sensor data from the new farm field before actual prediction tasks. This preliminary data collection and adjustment phase allows the system to adapt to field-specific conditions (lighting, crop varieties, growth stages) before deployment, thereby maintaining prediction accuracy across different environments without requiring extensive retraining
2Measurement precision
If manual adjustment and annotation are performed to improve prediction accuracy, then prediction precision is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system implements self-service by automatically adjusting prediction parameters and selecting appropriate models based on environmental conditions detected from initial images and sensor data. The system performs self-adjustment of prediction thresholds, crop type identification, and growth stage classification without requiring manual annotation or parameter tuning, thereby maintaining high accuracy while minimizing time investment
Solution Approach 2:
The system performs preliminary automated analysis of collected images to automatically determine field-specific parameters such as crop varieties, planting densities, and growth stages. This preliminary automated adjustment eliminates the need for time-consuming manual annotation while establishing accurate baseline parameters for prediction tasks
3Measurement precision
If extensive manual annotation is performed to achieve high detection performance, then detection accuracy is improved, but cost and operational complexity increase
Solution Approach 1:
The system achieves self-service in data annotation by automatically labeling images through preliminary detection and classification. The system autonomously identifies crop types, growth stages, and regional characteristics from initial images, generating training data without human intervention. This eliminates costly manual annotation while maintaining sufficient detection accuracy for agricultural prediction tasks
Solution Approach 2:
The system applies partial annotation strategies by focusing annotation efforts only on critical regions or ambiguous cases rather than annotating entire datasets. By performing selective annotation on key samples and using automated methods for routine cases, the system achieves high detection accuracy while significantly reducing annotation costs and operational burden
Data Source
AI summary
An information processing apparatus detects a partial region corresponding to a detection target from an input image using a learning model. The information processing apparatus accepts information indicating a specific partial region, which is input by a user based on the input image, and generates learning data from a result of correction of the result of detection using the learning model based on the accepted information indicating the specific partial region and the input image.