Real-Time Caterpillar Detection and Tracking in Orchard Using YOLO-NAS Plus SORT

Sumesh Nair, Guo-Fong Hong, Chia-Wei Hsu, Chun-Yu Lin and Shean-Jen Chen ()
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Sumesh Nair: College of Photonics, National Yang Ming Chiao Tung University, Tainan 711, Taiwan
Guo-Fong Hong: College of Photonics, National Yang Ming Chiao Tung University, Tainan 711, Taiwan
Chia-Wei Hsu: College of Photonics, National Yang Ming Chiao Tung University, Tainan 711, Taiwan
Chun-Yu Lin: College of Photonics, National Yang Ming Chiao Tung University, Tainan 711, Taiwan
Shean-Jen Chen: College of Photonics, National Yang Ming Chiao Tung University, Tainan 711, Taiwan

Agriculture, 2025, vol. 15, issue 7, 1-18

Abstract: Detecting and tracking caterpillars in orchard environments is crucial for advancing precision agriculture but remains challenging due to occlusions, variable lighting, wind interference, and the need for precise small-object detection. This study presents a real-time deep learning approach that integrates the YOLO-NAS object detection model with the SORT tracking algorithm to overcome these challenges. Evaluated in a jujube orchard, the proposed method significantly improved small caterpillar detection and tracking. Using an RGB-D camera operating at 30 frames per second, the system successfully detected caterpillars measuring 2–5 cm at distances of 20–35 cm, corresponding to resolutions of 21 × 6 to 55 × 10 pixels. The integration of YOLO-NAS with SORT enhanced detection performance, achieving a ~9% increase in true positive detections and an ~8% reduction in false positives compared to YOLO-NAS alone. Even for the smallest caterpillars (21 × 6 pixels), the method achieved over 60% true positive detection accuracy without false positives within 1 s inference. With an inference time of just 0.2 milliseconds, SORT enabled real-time tracking and accurately predicted caterpillar positions under wind interference, further improving reliability. Additionally, selective corner tracking was employed to identify the head and tail of caterpillars, paving the way for future laser-based precision-targeting interventions focused on the caterpillar head.

Keywords: caterpillar; YOLO-NAS; SORT; detection; tracking; laser targeting; pest control (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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