Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive...

Area of Science:

• Computer Vision
• Machine Learning
• Agricultural Technology

Background:

• Convolutional Neural Networks (CNNs) are advanced deep learning algorithms known for local perception and parameter sharing.
• CNN applications are widespread in computer vision and natural language processing.
• Fresh fruit production is a vital socioeconomic sector where CNN technology offers significant potential.

Purpose of the Study:

• To provide the first comprehensive review of CNN-based deep learning detection technology across the entire fresh fruit production process.
• To detail CNN network architecture, principles, and training methodologies.
• To investigate and compare CNN applications in key fruit production stages.

Main Methods:

• Systematic literature review of CNN-based deep learning detection technologies in fresh fruit production.
• Analysis of CNN network architectures and training processes.
• Comparison of various CNN detection methods applied to fruit flower detection, fruit detection, harvesting, and grading.

Main Results:

• CNN-based deep learning has demonstrated breakthroughs in critical fresh fruit production stages.
• Improved CNN models, tailored to specific production links, maximize detection capabilities.
• CNNs show promise in overcoming challenges like environmental variability and multi-task execution.

Conclusions:

• CNN-based deep learning is a powerful tool for optimizing fresh fruit production.
• Tailored CNN approaches enhance efficiency and accuracy in fruit detection, harvesting, and grading.
• Future research should focus on CNNs' adaptability to complex environmental factors and diverse production tasks.