A Multi-Strategy Framework for Enhancing Shatian Pomelo Detection in Real-World Orchards

As a specialty agricultural product with a large market scale, Shatian pomelo necessitates the adoption of automated detection to ensure accurate quantity and meet commercial demands for lean production. Existing research often involves specialized networks tailored for specific theoretical or dataset scenarios, but these methods tend to degrade performance in real-world. Through analysis of factors in this issue, this study identifies four key challenges that affect the accuracy of Shatian pomelo detection: imaging devices, lighting conditions, object scale variation, and occlusion. To mitigate these challenges, a multi-strategy framework is proposed in this paper. Firstly, to effectively solve tone variation introduced by diverse imaging devices and complex orchard environments, we utilize a multi-scenario dataset, STP-AgriData, which is constructed by integrating real orchard images with internet-sourced data. Secondly, to simulate the inconsistent illumination conditions, specific data augmentations such as adjusting contrast and changing brightness, are applied to the above dataset. Thirdly, to address the issues of object scale variation and occlusion in fruit detection, an REAS-Det network is designed in this paper. For scale variation, RFAConv and C3RFEM modules are designed to expand and enhance the receptive fields. For occlusion variation, a multi-scale, multi-head feature selection structure (MultiSEAM) and soft-NMS are introduced to enhance the handling of occlusion issues to improve detection accuracy. The results of these experiments achieved a precision(P) of 87.6%, a recall (R) of 74.9%, a mAP@.50 of 82.8%, and a mAP@.50:.95 of 53.3%. Our proposed network demonstrates superior performance compared to other state-of-the-art detection methods.