Plant Disease Diagnosis via Image Recognition and Neural Network Filtering
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Solution Overview
Problem
Current methods for diagnosing plant diseases and insect pests often rely on professional personnel and can lead to delayed detection, causing adverse effects on plant growth, as they are not always effective in timely identification.
Innovation Solution
A method and system utilizing image recognition models, specifically neural network models like convolutional neural networks and residual networks, to diagnose plant diseases and insect pests by processing plant images, determining candidate species and corresponding disease/insect pest information, and filtering results based on preset confidence and accuracy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional management personnel manually diagnose plant diseases and insect pests, then diagnostic accuracy can be maintained, but detection timeliness deteriorates and productivity decreases
Solution Approach 1:
The patent replaces the mechanical manual diagnosis system with an automated image recognition system based on deep learning models. The system captures plant images and uses pre-trained models to automatically identify diseases and insect pests, eliminating the need for manual inspection while maintaining diagnostic accuracy and significantly improving detection speed.
Solution Approach 2:
The system enables self-diagnosis for farmers and plant owners without requiring professional knowledge. By providing an automated diagnostic tool that processes images and returns results independently, the system allows users to diagnose plant health issues themselves, reducing dependency on professional personnel and accelerating detection.
2Reliability
If professional personnel are used for diagnosis, then diagnostic reliability is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system transforms professional diagnostic capabilities into an accessible self-service tool. Farmers can simply capture plant images and receive diagnostic results without needing to understand complex diagnostic procedures or possess specialized knowledge, making the process as simple as taking a photograph while maintaining professional-level reliability.
3Device complexity
If manual diagnosis methods are used, then system complexity is reduced, but productivity and detection efficiency deteriorate
Solution Approach 1:
The patent replaces manual diagnostic processes with an automated image recognition system that processes multiple images simultaneously. The system can analyze numerous plant images in parallel, dramatically increasing detection throughput and productivity while maintaining manageable complexity through the use of standardized deep learning models and automated workflows.
Data Source
AI summary
A method and a system for diagnosing a plant disease and an insect pest are provided. The method includes: obtaining an plant image; determining a candidate specie and candidate disease and insect pest information corresponding to at least part of the candidate specie according to the plant image when a current diagnosis mode is a passive diagnosis mode; screening out the candidate disease and insect pest information of the candidate specie according to a first preset condition for the candidate specie with the corresponding candidate disease and insect pest information; and outputting at least part of remaining disease and insect pest information after screening out when there is the remaining disease and insect pest information.