###### Abstract
[PERSON] [9] performed an examination of the morphology and developmental stage of cucumbers, which resulted in the creation of a repository of templates encompassing various dimensions and bending angles. Subsequently, the team utilized a template matching technique to accurately identify cucumbers. To enhance the amount of image data, researchers have introduced hyperspectral cameras, infrared cameras, and thermal imagers for image acquisition [10]. [PERSON] [11] utilized a hyperspectral camera with 60 spectral bands ranging from 369 nm to 1042 nm to capture images of green citrus. Through spectral analysis, they selected specific bands with distinctive features. They then applied a thresholding technique to remove bright and dark backgrounds in the images. Finally, by integrating all the bands, they established a linear discriminant to accurately segment green leaves and green citrus. [PERSON] [12] employed a handheld hyperspectral camera to capture images of mangoes in an orchard. They used two different methods, namely, PLS (partial least square) and simple CNN (convolutional neural network), for mango recognition and ripeness estimation, respectively. Despite the ability of the hyperspectral camera to capture extensive color data, its operation is intricate, the settings it operates in are challenging, and the process of acquiring images is highly demanding. The conditions are demanding, and the procedure of acquiring the photograph is time-consuming. For fruit recognition, three-dimensional picture features are employed as supplementary attributes. Both [PERSON] [13] and [PERSON] [14] have exploited depth photographs of fruits and vegetables to aid in fruit recognition. The fruit's location and center point were identified by rotating the center of the vortex's gradient vector of the depth image, which was mapped to a 2D space. They subsequently used shape fitting to successfully recognize targets, with a particular focus on round fruits like oranges and apples. [PERSON] [15] incorporated the detection of bell peppers to identify various 3D image features, including 3D surface normal features, 3D plane symmetry features, and elliptical surface highlight features. [PERSON] [16] employed a Kinect V2 camera to capture RGBD images of ripe broccoli in the field. They then utilized the VFH (viewpoint feature histogram) feature operator to describe the target and employed an SVM classifier to classify the feature vectors and locate the head of the ripe broccoli. [PERSON] [17] introduced the CTI (convex template instance) operator to represent the 3D shape of fruits for the purpose of identifying convex fruits like tomatoes and apples. The KNN (K-nearest neighbor) feature operator was employed to describe the fruits, and then, the SVM classifier was used to classify the feature vectors, enabling the detection and localization of ripe broccoli heads. Typically, fruit recognition research primarily concentrates on a single category of picture characteristics to identify the desired object. Nevertheless, many studies have employed more extensive techniques to apply specific visual characteristics to fruit identification. [PERSON] [18] introduced an algorithm for extracting texture features based entirely on color-related local binary patterns. Through the integration of color and texture characteristics and the utilization of the nearest neighbor classifier, the researchers successfully accomplished fruit and vegetable categorization, resulting in a minimum 5% improvement in recognition accuracy. Promising results have been achieved in the application of deep learning technology for fruit recognition in complicated situations [19; 20; 21; 22]. [PERSON] [23] utilized a Kinect camera to capture fruit images, employing RGB images, depth images, and reflected signal intensity maps. These images were merged into a 5-channel image and used as input for the Faster RCNN algorithm to identify Red Fuji apples.