###### Abstract
With the development of convolutional neural networks (CNNs), many authors use them for fruit detection based on segmentation and object detection approaches. [PERSON] et al. [10] proposed a watershed and trinocular stereo triangulation-based segmentation with a cascade of weak classifiers to perform a strawberry and apple detector model. Bargoti  
Figure 1: Particularity of fruit type: (**a**) peach. (**b**) apple. and [PERSON] [11] trained a CNN, where its output was the probability that each pixel belonged to a fruit. Then, the output result was used to generate a binary mask for segmentation. [PERSON] et al. [12] proposed the CNN segmentation model, named U-NET, for apple segmentation and circular Hough transform for fruit detection. The original purpose for this network is medical image segmentation. The author reports an F1 score of 0.858 for detection. CNNs have also made massive contributions in improving image classification and object detection. In this regard, the object detection framework R-CNN [13] and its variants fast R-CNN [14], faster R-CNN [15], and mask R-CNN [16] are widely used in the literature. [PERSON] et al. [17] proposed faster R-CNN to detect peppers, apples, avocados, mangoes, strawberries, and oranges. However, only the images of peppers are from the orchard. The rest of the fruit images are from the internet (Google Images). CNNs rely on large amounts of data for training to avoid overfitting, and they have good generalization. To achieve good performance with a relatively small set of training data, a transfer learning technique is often used. Recently, you only look once (YOLO), another branch of object detection based on CNN, became a framework for fruit detection. [PERSON] et al. [18] applied this model to detect mangoes in orchard images. They used two metrics for evaluation, F1 and average precision (AP), and reported results of 0.96 and 0.98, respectively. [PERSON] et al. [19] applied the mask R-CNN method to detect cucumber fruits. They chose ResNet-101 as the backbone and proposed a modification in the scales and aspect ratios of the anchor boxes. For evaluation, the authors used an F1 score and obtained a score of 0.894 for the test images. Other studies were developed to detect fruits. However, fruit detection procedures are specific due to the particular distribution of fruit colors, sizes, shapes, branches, and bundles. This study provides the first application (and, consequently, the annotated dataset) of peach detection that is constrained by (1) peaches with different colors, (2) peaches overlapped, and (3) peaches occluded by leafs. The faster R-CNN computer vision framework is used for fruit detection in peach orchards. Fruit detection can be used to count the number of fruits during the culture, and knowing that value, irrigation and all other cultural practices can be conveniently scheduled, and the correct amount of required inputs for fertilization and plant protection can be acquired. This approach improves the productivity and competitiveness of farmers while ensuring environmental concerns. Thus, it can be considered a precision agriculture application based on a deep neural network to help mitigate climate change, as continuous monitoring provides an up-to-date status of the field culture and allows monitoring and control of yields, which may be used as decision-making support systems that lead to increased production with less consumption of natural resources and GHG emissions.