# Machine Vision-Based Chinese Walnut Shell-Kernel Recognition and Separation
## 1 Introduction  
Walnut shell-kernel separation is a critical procedure in the deep processing of walnuts and is a vital link to advance the industrial chain and added value of walnuts [1, 2]. Following the mechanical breaking of the walnut, it is impossible to completely separate the shell and kernel during cleaning due to the complicated structure of mixtures and diversified forms [3]. Manual intervention is needed in the walnut selection. It incurs high costs and low efficiency [4], thereby restricting the development of the walnut industry. Shell-kernel recognition is one of the crucial technologies to realise intelligent shell-kernel selection. Analysing walnut shell-kernel detection algorithms based on deep learning is critical for achieving intelligent and mechanical walnut shell-kernel separation. Walnut shell-kernel separation is a process that separates shell-kernel mixtures acquired after shell breaking by the shell-breaking device and collects walnut kernels. Many enterprises still adopt the manual selection technique, which has high labour intensity and separation costs [5]. Several Chinese and foreign scholars have conducted abundant investigations on the shell-kernel separation method of nuts. For instance, [PERSON] et al. [6] proposed the magnetic shell-kernel separation technique according to their differences in magnetic conductance. However, fresh impurities were introduced into the separation process, necessitating further separation. Moreover, it was easy to contaminate kernels. [PERSON] et al. [7] invented an electrostatic shell-kernel separation machine for nuts and successfully realised the shell-kernel separation of nuts based on their different dielectric properties. Moreover, there was no mechanical force on the shell and kernel during separation, without the restriction of particle size, shape and proportion. They provided a novel solution to the shell-kernel separation of nuts. Nevertheless, these methods presented various challenges, such as complicated separation processes, high costs, low efficiency, and poor guarantee of food quality and safety, among others. Hence, their promotion was challenging. The airflow selection method has a simple device structure and good separation effect and solves existing problems to a large extent. It is a commonly used method in walnut shell-kernel separation. [PERSON] et al. [8], [PERSON] et al. [9], and [PERSON] et al. [10] investigated walnut shell-kernel separation based on airflow separation and achieved a relatively good effect. Due to the impact of diversified sizes, shapes and windward areas, the walnut shells and kernels have complicated and changing motions in the airflow field, thus making it arduous to achieve thorough separation. In particular, some overlapping aerodynamic characteristics exist between walnut shells and kernels, thereby increasing the separation difficulty and restricting the improvement of the shell-kernel separation effect. Recently, the development of computer technology and machine vision technology provided novel solutions to walnut shell-kernel separation. [PERSON] et al. [11] designed a pecan shell-kernel separation system based on a fuzzy clustering algorithm and separated shells and kernels after breaking using the fuzzy clustering algorithm. Their results demonstrated that the walnut kernel recognition rate of the system was higher than 83%. [PERSON] et al. [4] classified shells and kernels of black walnuts based on hyperspectral fluorescence imaging technology; the recognition rate could reach 90.3%. [PERSON] et al. [12] designed an intelligent black walnut shell-kernel recognition system that acquired relatively high separation precision and calculation speed. It had some application potential in the field of walnut shell-kernel separation. However, it is hard to create high-efficiency applications of conventional image recognition technology in complicated actual production due to its detailed processing, low recognition precision, and requests for the manual extraction of target features [13; 14].