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Real-time litchi detection in complex orchard environments: A portable, low-energy edge computing approach for enhanced automated harvesting
- Source :
- Artificial Intelligence in Agriculture, Vol 11, Iss , Pp 13-22 (2024)
- Publication Year :
- 2024
- Publisher :
- KeAi Communications Co., Ltd., 2024.
-
Abstract
- Litchi, a succulent and perishable fruit, presents a narrow annual harvest window of under two weeks. The advent of smart agriculture has driven the adoption of visually-guided, automated litchi harvesting techniques. However, conventional approaches typically rely on laboratory-based, high-performance computing equipment, which presents challenges in terms of size, energy consumption, and practical application within litchi orchards. To address these limitations, we propose a real-time litchi detection methodology for complex environments, utilizing portable, low-energy edge computing devices. Initially, the litchi orchard imagery is collected to enhance data generalization. Subsequently, a convolutional neural network (CNN)-based single-stage detector, YOLOx, is constructed to accurately pinpoint litchi fruit locations within the images. To facilitate deployment on portable, low-energy edge devices, we employed channel pruning and layer pruning algorithms to compress the trained model, reducing its size and parameters. Additionally, the knowledge distillation technique is harnessed to fine-tune the network. Experimental findings demonstrated that our proposed method achieved a 97.1% compression rate, yielding a compact litchi detection model of a mere 6.9 MB, while maintaining 94.9% average precision and 97.2% average recall. Processing 99 frames per second (FPS), the method exhibited a 1.8-fold increase in speed compared to the unprocessed model. Consequently, our approach can be readily integrated into portable, low-computational automatic harvesting equipment, ensuring real-time, precise litchi detection within orchard settings.
Details
- Language :
- English
- ISSN :
- 25897217
- Volume :
- 11
- Issue :
- 13-22
- Database :
- Directory of Open Access Journals
- Journal :
- Artificial Intelligence in Agriculture
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fd923b6c531e4d17a4f10527fba67df9
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.aiia.2023.12.002