With the advancement of artificial intelligence, computer vision has become a widely adopted method to replace human visual observation. However, the complexity of the greenhouse tomato-growing environment poses significant challenges in using computer vision to quickly and accurately assess the ripeness of tomatoes. In order to solve these problems, we incorporate SPD-Conv and BoTNet to enhance the YOLOv8n network’s performance in feature extraction and target recognition capabilities in greenhouse tomato-growing environments. In simulations, we compare the performance of YOLOv8n with that of BS-YOLOv8n. Empirical findings demonstrate that the proposed BS-YOLOv8n performs better than YOLOv8n in both the accuracy and the response speed of tomato recognition in complex greenhouse environments.

Zhengdong Li, Yu Wang, Minghua Han, Zhou Zheng. BS-Yolov8n: An Improved Yolov8n Network for Tomato Detection at Different Ripeness Degrees in Complex Greenhouse Environments. Academic Journal of Agriculture & Life Sciences (2025), Vol. 6, Issue 1: 130-136. https://doi.org/10.25236/AJALS.2025.060118.

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