Image Processing and Artificial Intelligence for Apple Detection and Localization: A Comprehensive Review
COMPUTER SCIENCE REVIEW（2024）
Minist Educ
Abstract
This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection and localization such as occlusions, varying illumination conditions, and clustered apples are highlighted, as well as the impact of environmental factors such as light changes on the performance of these algorithms. Potential future research perspectives are identified through a comprehensive literature analysis. These include combining cutting-edge deep learning and multi-vision and multi-modal sensors to potentially apply them in real-time for apple harvesting robots. Additionally, utilizing 3D vision for a thorough analysis of complex and dynamic orchard environments, and precise determination of fruit locations using point cloud data and depth information are presented. The outcome of this review paper will assist researchers and engineers in the development of advanced detection and localization mechanisms for infield apples. The anticipated result is the facilitation of progress toward commercial apple harvest robots.
MoreTranslated text
Key words
Apple,Detection,Localization,Image processing,Artificial intelligence,Robotics,Fruit harvesting
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined