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Using Brainwave Patterns Recorded from Plant Pathology Experts to Increase the Reliability of AI-Based Plant Disease
Yonatan Meir1, Jayme Garcia Arnal Barbedo2, Omri Keren1
1InnerEye Ltd., Herzliya 4676670, Israel.
Sensors (Basel, Switzerland)
|May 13, 2023
Summary
Leveraging expert brain activity, specifically electroencephalograms (EEGs), significantly enhances artificial intelligence models for plant disease recognition. This method reduces labeling time and improves accuracy from 96% to 99%.
Area of Science:
- Agricultural Science
- Computer Vision
- Artificial Intelligence
- Neuroscience
Background:
- Developing accurate artificial intelligence (AI) and computer vision models for agriculture requires extensive labeled training data, which is time-consuming and costly to produce.
- Current image labeling processes demand significant cognitive and motor resources from human experts, detracting from core pattern recognition tasks.
- The need for efficient and reliable methods to create large, labeled datasets for agricultural AI applications is a critical bottleneck.
Purpose of the Study:
- To investigate the use of electroencephalograms (EEGs) from plant pathology experts to improve AI models for plant disease recognition.
- To explore how direct brain activity measurements can enhance the accuracy and robustness of image-based AI systems in agriculture.
- To reduce the time and cost associated with creating labeled datasets by incorporating expert knowledge more directly.
Main Methods:
- Recorded electroencephalograms (EEGs) from plant pathology experts while they identified plant diseases from images.
- Utilized brain-generated labels derived from expert EEG data.
- Implemented an active learning approach combined with EEG-based labeling to train AI models.
Main Results:
- AI model accuracy for plant disease recognition improved from 96% with a baseline model to 99% using EEG-generated labels and active learning.
- The approach demonstrated the viability of using brain activity to enhance AI model performance.
- Significant reduction in labeling time and improved incorporation of expert knowledge were observed.
Conclusions:
- Electroencephalogram (EEG) data from experts can substantially improve the accuracy and robustness of AI models for plant disease detection.
- This neuro-AI approach offers a promising solution to the data labeling challenge in agricultural computer vision.
- Future applications may include real-time expert knowledge integration into AI systems, accelerating agricultural innovation.
