摘要
我国番茄的产量居世界前列,种植的主要方式为棚室种植,大多采用人工采摘,成本高、效率低,不利于种植产业的发展。为此,提出了一种基于A-ENet模型的图像处理技术,用于棚室番茄果实的识别。A-ENet模型在EfficientNet运行时引入注意力机制,用于优化深度网络运算结果。EfficientNet通过调整、完善网络的宽度、深度等网络参数提升网络的识别准确率,同时在网络提取输入信号特征时引入注意力机制用于捕捉输入信号特征的权重信息,主动忽略环境因素对目标信号的干扰。A-ENet模型能够解决由于识别目标之间的细微差异产生的识别错误的问题,且能够减少随机环境因素对识别过程的干扰,提高识别成功率,对棚室番茄果实识别问题起到积极的影响作用。实验结果表明:所提出的基于图像处理技术的A-ENet模型能够构建出效率更高、鲁棒性更强的目标识别系统。
The output of tomatoes in China is in the forefront of the world,and greenhouse planting is the main planting mode of tomatoes in China.At present,most of them are picked manually,but the cost of manual picking is high and the efficiency is low,which is not conducive to the development of the tomato planting industry in China.Based on the above basic situation of tomato planting industry in China,this paper proposes an image processing technology based on A-ENet model,which is used to identify tomato fruits in greenhouse.The A-ENet model introduces the attention mechanism when the Efficient Net network is running to optimize the network operation results.Efficient Net improves the classification accuracy of the network by adjusting and improving the network parameters such as the width and depth of the network.At the same time,attention mechanism is introduced to capture the weight information of the input signal features when the network extracts the input signal features,and actively ignores the interference of environmental factors on the target signal.A-ENet model can solve the problem of subtle differences between recognition targets and recognition errors.At the same time,the network can reduce the interference of random environmental factors in the recognition process,improve the recognition success rate,and play a positive role in the greenhouse tomato fruit recognition problem.Through experiments,the A-ENet model proposed in this paper based on image processing technology has a higher overall recognition rate.This method can build a more efficient and robust target recognition system.
作者
陈翠琴
孟清
Chen Cuiqin;Meng Qing(Chengdu University of Information Technology,Chengdu 610225,China;School of Industry and Information Technology,Hainan Vocational University,Haikou 570216,China)
出处
《农机化研究》
北大核心
2025年第1期189-193,共5页
Journal of Agricultural Mechanization Research
基金
海南省高等学校教育教学改革研究项目(Hnjg2022-126)
中华职业教育社黄炎培职业教育思想研究规划课题(ZJS2022Zd55)。
