Crop Disease Detection via Real-Time Image Processing
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Solution Overview
Problem
Current methods for crop field evaluation are time-consuming and labor-intensive, making it difficult for growers to detect crop diseases and estimate crop yields efficiently.
Innovation Solution
Agricultural intelligence computer systems that utilize mobile devices with image processing and machine learning models to recognize diseases and estimate yields in real-time, integrating data from various sources such as soil, weather, and crop health data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual field evaluation methods are used, then comprehensive crop assessment can be performed, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical field evaluation with an automated image processing system. Mobile devices capture images of crops, and computer algorithms automatically analyze these images to detect diseases, count crop components, and estimate yields. This substitution eliminates the need for manual inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The system enables self-service evaluation where the crop field itself provides the information needed for assessment. By capturing images directly from the field and using automated analysis, the system allows the crop to 'evaluate itself' without requiring external manual intervention, thereby reducing labor and time requirements.
2Loss of information
If manual field evaluation methods are used, then detailed crop health assessment can be performed, but labor intensity increases
Solution Approach 1:
The patent replaces manual field evaluation with an automated image processing system. Mobile devices capture images of crops, and computer algorithms automatically analyze these images to detect diseases, count crop components, and estimate yields. This substitution eliminates the need for manual inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The system creates digital copies of the crop field through images captured by mobile devices. These image copies contain all the visual information needed for assessment, allowing detailed analysis without physically handling or disturbing the crops. The digital copy serves as a complete representation of the field state.
3Measurement precision
If traditional yield estimation methods are used, then comprehensive data collection can be performed, but processing time increases
Solution Approach 1:
The patent replaces manual field evaluation with an automated image processing system. Mobile devices capture images of crops, and computer algorithms automatically analyze these images to detect diseases, count crop components, and estimate yields. This substitution eliminates the need for manual inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The system enables continuous evaluation by processing images as they are captured. The automated analysis pipeline allows for real-time or near-real-time processing of crop images, maintaining continuous monitoring capability without the interruptions and delays inherent in manual evaluation methods.
Data Source
AI summary
In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.