###### Abstract  
Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling. convolutional neural network; deep learning; fruit detection; precision agriculture; sustainability +
Footnote †: journal: _climate_  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-0000  
the area under agriculture, which means less deforestation and a reduction of natural resource consumption, and GHG emissions. The application of precision agriculture techniques can help to mitigate climate change. Deep neural networks emerge as powerful tools in precision agriculture as they can provide accurate product prediction, leading to improved resource management, such as irrigation water, nutrients, herbicides, and pesticides, and help reduce food loss and waste by improved agricultural activity scheduling. [PERSON] et al. [3] investigated the ability of an encoder-decoder long short-term memory model, to model the daily evapotranspiration for threes. [PERSON] et al. [4] used computer vision, based on convolutional neural networks, to classify peach fruit diseases. Yield estimation is a branch of precision agriculture. It enables planning for cropping, operations, inventory management, and other ancillary services (e.g., fruit pickup by a robot). There are several published works in the field of fruit yield estimation. For example, [PERSON] et al. [5] presented a methodology for apple production estimation. [PERSON] et al. [6] developed a system for citrus fruit detection and counting. [PERSON] and [PERSON] [7] presented a work on the detection of mangoes, almonds, and apples in orchards. A fruit yield estimation pipeline begins with detection, followed by tracking and counting. The fruit detection phase is critical to the performance of the yield estimation system. Most state-of-the-art fruit detection systems are based on a convolutional neural network (CNN) [8], such as object detection, segmentation, among others. These methods automatically extract features from the appearance of fruit images (i.e., colors and shapes). In this context, the development of fruit detection is specific (because the fruit colors, sizes, shapes, clusters, and distributions in the trees are particular). Figure 1 illustrates this problem. Considering the lack of work with peaches, this paper presents the results for peach detection using the object detection framework faster region-based convolutional neural network (Faster R-CNN). Another contribution is that the images are from a non-controlled environment (outdoors). That is, they come from a natural orchard. [PERSON] and [PERSON] [9] presented a methodology to obtain an estimate of red and green apples. For the detection phase, they used the traditional computer vision approach based on hue, saturation, and value (HSV) color space for segmentation.