YOLO11n-DRE: A METHOD FOR MATURITY DETECTION AND ACCURATE COUNTING OF SINGLE TRUSS TOMATO FRUITS IN COMPLEX UNSTRUCTURED ENVIRONMENTS
Year: 2026
Type: article
Abstract: Accurate perception of the maturity of individual truss tomato fruits and in-situ counting in unstructured protected horticulture scenarios are critical prerequisites for driving selective automated harvesting, yield prediction and improving the level of refined management. Affected by factors such as the small fruit size and the complexity of the natural growth environment, vision-based maturity detection still faces considerable challenges. This paper proposes an improved method for maturity detection and counting of individual truss tomato fruits based on YOLO11n. Under the principle of maintaining a lightweight design, a fine-grained feature stream based on the P2 layer is developed, and the DySample operator is integrated to optimize the quality of feature fusion. Combined with the Selective Feature Refinement Module (EMA) and a four-head detector with full-scale coverage, the proposed method aims to maximize the model's representation capability in capturing small long-distance targets and in dense occluded environments. A dataset of individual truss tomato fruits with three maturity labels (immature, turning, ripe) is constructed, and systematic comparative experiments are conducted on the original YOLO11n and the improved model. The experimental results show that the improved YOLO11n-DRE outperforms the original YOLO11n model in maturity detection accuracy, with P, R and [email protected] increased by 0.65%, 0.63% and 1.07% respectively, and the model parameters reduced by 8.5%. This method demonstrates excellent detection performance, and provides a reference model and technical prerequisite for the maturity detection and yield estimation of individual truss tomato fruits.
Source: INMATEH Agricultural Engineering
Authors: Baofan CHEN, Yuhao HAO, Bingjun CHEN, Shuaishuai CUI, Yaqi YAN, Guozhu SONG
Institution: Shanxi Agricultural University
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W7155008158