## 1 Introduction  
Tomatoes, grown in different crop systems, are the world's second-most harvested vegetable and the leader among greenhouse vegetables [1]. In the last few decades, greenhouse tomato cultivation in several systems has increased worldwide because it has the advantage of enabling high productivity and stable supply throughout the year. While the value of greenhouse tomatoes is high on a per-unit basis, the costs are also high, mainly due to the labour costs. Greenhouse's manual operations account for up to 50% of the total greenhouse production costs, a large part of these costs being absorbed by the manual tomato harvesting, which requires 700 h yr\({}^{-1}\) ha\({}^{-1}\) to 1400 h yr\({}^{-1}\) ha\({}^{-1}\) according to the cropping system [2, 3]. Hence, manual harvesting of tomatoes is, actually, a challenge due to the global labour shortage and precarious working conditions [2, 4, 5]. Moreover, farmers need to secure additional workers during harvest seasons because the manpower requirements are higher than usual during this time [5]. Therefore, reducing the amount of labour in a greenhouse company is an important topic at the moment, and many companies are looking for automated solutions, such as harvesting robots [5]. The development of harvesting robots to work in greenhouses is always challenging because robots have to work in unstructured environments and perform uncertain tasks. In the case of harvesting tomato, the sensing mechanism has to detect fruit in the presence of various disturbances in an unpredicted heterogeneous environment included different arrangements of plant sizes and shapes, stems, branches, leaves, variable colour, under leaves, sun glare and variable light conditions [6; 7]. Even more, as a climacter fruit, according to the technological objectives, the tomato can be harvested at the physiological maturity phase (green colour), completing the ripening after harvest, or it can be harvested later at different stages of ripening in which it already has a reddish colour [8]. When to harvest will depend on how the tomato will be handled and used. Fresh market fruit for local consumers can be picked red, while fruit that will be transported long distances should be harvested at early maturation with green colour (Figure 1). Accurately identifying and detecting the mature fruit or fruit bunches comprise a key technique of harvesting robot research, which has recently received considerable scientific scrutiny. The performance of tomato harvesting robots has been greatly improved by the use of Artificial Intelligence (AI) tools mainly in tomato detection on images acquired in varying environmental and growth conditions such as fruit partially hidden by leaf or stem, state of ripeness (coloration) and light conditions. The current State-of-the-Art (SoA) explores and proposes different strategies to classify and detect tomatoes based on RGB images. However, in a general overview, these strategies are mostly framed in Machine Learning (ML) applications. These strategies are divided essentially based on the application of the most classical ML algorithms, using, for instance, Support Vector Machines (SVMs) [9] or Deep Learning (DL) strategies [10]. DL is based on the training of Artificial Neural Network (ANN) models to recognise features on annotated images (usually referred to as the training set). Then, the trained model is used to detect the trained objects on new images. The state-of-the-art identifies different ANN model structures, such as:  
1. Convolutional Neural Networks (CNNs);
2. Single-Shot MultiBox Detectors (SSDs). In comparison to other state-of-the-art ANN structures, the SSD [11] aims to be faster. Besides, the SSD does not compromise on the detection accuracy [11]. This work purposes the development of a vision system to recognise tomatoes in real scenarios by using a dataset of tomatoes grown in a greenhouse. The system explores DL models to be run in a TensorFlow Processing Unit (TPU) [12], to assure a high-speed tomato detection system.