外文摘要：High-throughput acquisition of phenotypic parameters based on machine vision is important for intelligent breeding, digital cultivation and automated harvesting of Oudemansiella raphanipes. However, due to the occlusion among Oudemansiella raphanipes in the growing bed, it is challenging to accurately and rapidly capture their full shape with conventional methods, resulting in a low measurement success rate. The overall goal of this study is to propose a deep learning-based method (Oudemansiella raphanipes Occlusion Region-based Convolutional Neural Networks, OR-ORCNN) to obtain the morphology of occluded Oudemansiella raphanipes precisely in the mushroom growing bed with high efficiency. To accomplish this, a state-of-the-art attention architecture (OR-SE), which consists of CCA (Criss-Cross Attention) and MSF (Multiscale Fusion) modules, was applied to the deocclusion assignment. Then, the predicted amodal mask combined pinhole camera model was used to compute the size of Oudemansiella raphanipes caps in millimeters (mm). The experimental results showed an improvement in the AP50 of 2.78 % and mAR of 5.60 % compared with the baseline model, demonstrating the effectiveness of the OR-SE module in improving the feature information capability of backbone networks. Meanwhile, the Oudemansiella raphanipes cap size estimation results reported an MAE of 0.93 mm and a MAPE of 4.96 %. Furthermore, to confirm the robustness and generalizability of the OR-ORCNN model, we also conducted experiments on Agrocybe cylindraceas and obtained satisfactory results. In summary, the proposed algorithm provides an effective way to recover the shape of the occluded region of Oudemansiella raphanipes with high efficiency and precision, which can be used to help researchers breed intelligently and growers optimize management.
外文关键词：occlusion;Phenotypic parameters;Amodal segmentation;Oudemansiella raphanipes
作者：Yin, Hua;Gao, Yang;Wei, Quan;Hu, Haijing;Wang, Yinglong
作者单位：Jiangxi Agr Univ
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：220
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台