Zhang Chi, Huang Wenqian, Liang Xiaoting, He Xin, Tian Xi, Chen Liping, Wang Qingyan
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
College of Information Technology, Shanghai Ocean University, Shanghai, China.
Front Plant Sci. 2022 Sep 15;13:956636. doi: 10.3389/fpls.2022.956636. eCollection 2022.
Slight crack of cottonseed is a critical factor influencing the germination rate of cotton due to foamed acid or water entering cottonseed through testa. However, it is very difficult to detect cottonseed with slight crack using common non-destructive detection methods, such as machine vision, optical spectroscopy, and thermal imaging, because slight crack has little effect on morphology, chemical substances or temperature. By contrast, the acoustic method shows a sensitivity to fine structure defects and demonstrates potential application in seed detection. This paper presents a novel method to detect slightly cracked cottonseed using air-coupled ultrasound with a light-weight vision transformer (ViT) and a sound-to-image encoding method. The echo signal of air-coupled ultrasound from cottonseed is obtained by non-contact and non-destructive methods. The intrinsic mode functions (IMFs) of ultrasound signal are obtained as the sound features using variational mode decomposition (VMD) approach. Then the sound features are converted into colorful images by a color encoding method. This method uses different colored lines to represent the changes of different values of IMFs according to the specified encoding period. A light-weight MobileViT method is utilized to identify the slightly cracked cottonseeds using encoding colorful images corresponding to cottonseeds. The experimental results show an average overall recognition accuracy of 90.7% for slightly cracked cottonseed from normal cottonseed, which indicates that the proposed method is reliable to applications in detection task of cottonseed with slight crack.
棉籽的轻微裂缝是影响棉花发芽率的关键因素,因为发泡酸或水会通过种皮进入棉籽。然而,使用常见的无损检测方法,如机器视觉、光谱学和热成像,很难检测出有轻微裂缝的棉籽,因为轻微裂缝对形态、化学物质或温度影响很小。相比之下,声学方法对细微结构缺陷表现出敏感性,并在种子检测中显示出潜在的应用价值。本文提出了一种使用空气耦合超声、轻量级视觉Transformer(ViT)和声音到图像编码方法来检测轻微裂缝棉籽的新方法。通过非接触和无损方法获取棉籽的空气耦合超声回波信号。使用变分模态分解(VMD)方法获得超声信号的本征模态函数(IMF)作为声音特征。然后通过颜色编码方法将声音特征转换为彩色图像。该方法根据指定的编码周期,用不同颜色的线条表示IMF不同值的变化。利用轻量级移动视觉Transformer方法,通过与棉籽对应的编码彩色图像来识别轻微裂缝的棉籽。实验结果表明,对于正常棉籽中的轻微裂缝棉籽,平均总体识别准确率为90.7%,这表明所提出的方法在轻微裂缝棉籽检测任务中的应用是可靠的。