Wheat Spike Counting Correction via Image Recognition
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Solution Overview
Problem
Existing spike number prediction technologies, such as those described in Patent Literature 1, fail to accurately predict the number of spikes in grain cultivation fields due to unreadable or illegible information from images, such as spikes hidden behind leaves or densely distributed spikes.
Innovation Solution
A spike number prediction device that includes a spike number counting unit using image recognition techniques to count spikes from image data, a correction unit that adjusts the count values based on statistical correlation information, and a prediction unit that predicts spike numbers in a target area based on relative size relationships and corrected count values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image recognition technique is used to count spikes, then counting efficiency is improved, but prediction accuracy deteriorates due to unreadable or illegible information from images
Solution Approach 1:
The patent introduces a correction value as an intermediary element that bridges the gap between image-based counting and actual spike numbers. The correction value is derived from correlation information between counted spike numbers and actual spike numbers, and is applied to adjust the image-based counts to achieve more accurate predictions while maintaining the efficiency of image recognition.
Solution Approach 2:
The patent implements a feedback mechanism where the difference between image-based counted spike numbers and actual spike numbers is analyzed to generate correction values. These correction values are then fed back into the prediction system to adjust future predictions, continuously improving accuracy while maintaining the efficient image recognition process.
2Measurement precision
If manual counting is used to ascertain spike numbers, then prediction accuracy is improved, but labor effort increases significantly
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated image recognition system. The image recognition technique automatically detects and counts spikes from images of the grain field, eliminating the need for manual counting while maintaining high prediction accuracy through the correction mechanism.
Solution Approach 2:
The patent creates a digital copy of the grain field through image data, allowing automated analysis of spike numbers from the image representation rather than requiring physical manual counting. The correction values are then applied to this digital count to achieve accurate predictions.
3Productivity
If image data is used to predict spike numbers, then work efficiency is improved, but information completeness deteriorates due to hidden or densely distributed spikes
Solution Approach 1:
The patent changes the parameter used for prediction from raw image-based spike counts to corrected spike numbers that incorporate correction values. This parameter transformation adjusts the incomplete information from images by compensating for hidden or densely distributed spikes, thereby improving information completeness while maintaining work efficiency.
Data Source
AI summary
A spike number prediction device (10) includes: a spike number counting unit (14A) and the like configured to count, from image data obtained by imaging a two-dimensional unit region (frame F) provided in a wheat cultivation field (30), the number of wheat spikes from the frame F based on an image recognition technique; a correction unit (14B) configured to correct a spike number count value obtained by counting based on correlation information between a spike number count value obtained in advance by statistical processing and a spike number true value; and a spike number prediction unit (14C) configured to predict, based on a relative size relationship between a predetermined target range in the wheat cultivation field and the frame F, and a corrected spike number value after correction, the number of wheat spikes in the target range.