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Recognition pest by image-based transfer learning.
Wang Dawei1, Deng Limiao1, Ni Jiangong1
1Department of electronic information, Science and Information College, Qingdao Agriculture University, Qingdao, China.
Journal of the Science of Food and Agriculture
|March 15, 2019
Summary
Accurate pest identification is crucial for crop health and yield. A new transfer learning system achieves 93.84% accuracy in recognizing ten pest types, comparable to human experts, aiding precision agriculture.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Plant pests, including insects and mites, cause significant crop losses globally.
- Accurate pest identification is vital for healthy crop growth, yield, and quality.
- Current pest identification methods face challenges in accuracy and reliability.
Purpose of the Study:
- To develop an automated system for pest detection and recognition.
- To improve the accuracy and efficiency of identifying various crop pests.
- To provide technical support for precision agriculture and effective pest management.
Main Methods:
- A diagnostic system utilizing transfer learning for pest detection and recognition was developed.
- The system was trained and tested on ten different types of pests.
- Performance was evaluated against human experts and traditional neural network models.
Main Results:
- The transfer learning system achieved 93.84% accuracy in identifying ten pest types.
- The model's performance was comparable to that of human experts and traditional neural networks.
- The system demonstrated general adaptability by achieving 98.92% accuracy in recognizing two weed species.
Conclusions:
- The developed method offers reliable technical support for precision agriculture.
- It aids in pest and weed control and the precise application of pesticides.
- The system contributes to enhancing crop yields and quality through accurate identification.
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