# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 1 Introduction  
Kiwifruit is one of the most productive fruits in China and has great economic benefits. Because of the increasing cost of manual picking in recent years, many agricultural industries have to use picking robots [1]. A vision system is a vital part of vision-based picking robots, affecting performances such as efficiency, stability, and adaptation in complex environments [2, 3]. However, there are factors that make the vision system unstable: the varying light intensity due to changing weather conditions; the diversity of fruit clusters with different branch, leaf shading, and overlapping; the limited computational resources and complex algorithms which do not run efficiently for the picking robot. These factors make it difficult for picking robots to quickly and accurately detect kiwifruit. In recent years, researchers around the world have conducted lots of studies on the object recognition of fruit and vegetables in natural environments, including traditional image processing techniques and deep learning methods which are currently popular. Traditional recognition methods mainly include edge contour extraction methods, region growth segmentation methods, threshold segmentation methods, etc. The fruit objects in images are usually recognized by using single feature or the combination of multiple features extracted from the fruit images, such as shapes, textures, and color differences. [PERSON] et al. [4] used a circular Hough transform method for citrus object detection, but the detection accuracy was low for dense and overlapping fruits. [PERSON] et al. [5] employed RGBand YCbCr color space segmentation as well as texture segmentation based on adjacent pixel variability to segment mangoes from background pixels. [PERSON] et al. [6] proposed a string harvest tomato segmentation method based on the Canny edge detection algorithm, which solved the problem of fruit adhesion, but also wasted a large number of non-fruit adhesion points. [PERSON] et al. [7] used the Sobel edge algorithm to remove the target fruit background and identify kiwi using the template matching method, but did not use the fruit shape information. [PERSON] et al. [8] presented methods such as shape invariant moments to synthesize the color and shape features of fruits and used an SVM classifier to classify fruits, but the applicability of the algorithm is relatively poor for different environments. The methods mentioned above can identify a single type of fruit, but they are poorly adapted to situations such as similar color backgrounds, fruit shading, and light changes, resulting in poor generality. So, traditional machine vision technologies are limited by their classification algorithms and cannot meet the requirements for picking robots in complex environments [9]. Deep learning object detection algorithms can complete the fruit recognition tasks quickly and reach great performances, which are mainly divided into two categories: the first one is the regression-based one-stage object detection algorithm, including YOLO [10], Single Shot MultiBox Detector (SSD) [11], etc., and the other is the two-stage algorithm based on region suggestion, the representative algorithms include Faster RCNN [12], RCNN [13] and Mask R-CNN [14], etc. [PERSON] et al. [15] and [PERSON] et al. [16] used Faster R-CNN networks to identify bell pepper and kiwi, respectively and improved the network recognition accuracy. However, the two-stage object detection algorithm model is slower to train and has a longer detection time than that of one-stage algorithms. [PERSON] et al. [17] proposed a LeNet convolutional neural network-based multi-cluster kiwifruit recognition method, which used elevated angle of capture for image acquisition of trellis cultivated kiwifruit, with high accuracy for independent and adjacent fruit recognition. However, the accuracy is relatively low for obscured and overlapping fruits and the recognition speed is slow for individual fruits. [PERSON] et al.