A Garbage Detection and Classification Model for Orchards Based on Lightweight YOLOv7

Xinyuan Tian, Liping Bai () and Deyun Mo
Additional contact information
Xinyuan Tian: Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa 999078, China
Liping Bai: Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa 999078, China
Deyun Mo: Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa 999078, China

Sustainability, 2025, vol. 17, issue 9, 1-19

Abstract: The disposal of orchard garbage (including pruning branches, fallen leaves, and non-biodegradable materials such as pesticide containers and plastic film) poses major difficulties for horticultural production and soil sustainability. Unlike general agricultural garbage, orchard garbage often contains both biodegradable organic matter and hazardous pollutants, which complicates efficient recycling. Traditional manual sorting methods are labour-intensive and inefficient in large-scale operations. To this end, we propose a lightweight YOLOv7-based detection model tailored for the orchard environment. By replacing the CSPDarknet53 backbone with MobileNetV3 and GhostNet, an average accuracy (mAP) of 84.4% is achieved, while the computational load of the original model is only 16%. Meanwhile, a supervised comparative learning strategy further strengthens feature discrimination between horticulturally relevant categories and can distinguish compost pruning residues from toxic materials. Experiments on a dataset containing 16 orchard-specific garbage types (e.g., pineapple shells, plastic mulch, and fertiliser bags) show that the model has high classification accuracy, especially for materials commonly found in tropical orchards. The lightweight nature of the algorithm allows for real-time deployment on edge devices such as drones or robotic platforms, and future integration with robotic arms for automated collection and sorting. By converting garbage into a compostable resource and separating contaminants, the technology is aligned with the country’s garbage segregation initiatives and global sustainability goals, providing a scalable pathway to reconcile ecological preservation and horticultural efficiency.

Keywords: deep learning; orchards; garbage classification; YOLOv7; lightweight network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/3922/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/3922/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:3922-:d:1643663

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().