## 1 Introduction  
The high labour demand for the execution of several agricultural tasks causes bottlenecks within farms' organisation with associated efficiency costs, especially in recurrent situations of unavailability of labour. Competition for labour between sectors and the ageing or scarcity of workers contribute to labour shortages [1]. In the agricultural industry, the problem is aggravated by the hazardous nature of most farming operations, which makes them unattractive and exclusive, often associated with social discrimination and illegal labour flows. Cost reduction is thus hindered by the vital needs for labour power [2]. This context demands the adoption of new technologies and the search for solutions that improve cost reduction or compensate for the lack of labour to guarantee the success of the various production systems. Introducing robotic technology in agriculture could alter productivity, ergonomics and labour hardship. Protected or greenhouse horiculture is one of the most intensive in production inputs and knowledge, focusing on the production of crops with high added value, where labour accounts for up to 50% of the usual costs [3]. The harvesting operation becomes an excellent candidate for automation due to being recurrent and crucial in the production of high-value crops [4]. Increasing efficiency and reducing labour dependency in this operation could ensure higher yields and competitiveness in high-tech food production, so the development of harvesting robots should be considered as a viable alternative [5]. However, despite all the advances, the penetration of robots is not yet comparable to the robots developed for open-field farming systems [6] and every year millions of tons of fruit and vegetables are harvested manually in greenhouses. The scarce use of robots can be attributed to their low performance, so it is essential to understand why this limited performance and challenges that can generate a positive trend [4; 5; 7]. Few crops are as important as tomatoes. Between 2003 and 2017, the world tomato production increased annually from 124 million tonnes to more than 177 million tonnes, and over the last 15 years, consumption has experienced sustained growth of around 2.5% [8]. This is one of the leaders when it comes to protected horticulture. In the south-east of Spain, Almeria, home to the world's largest concentration of greenhouses (over 30,000 hectares), tomatoes are the main crop, accounting for 37.7% of all production [9]. Manual tomato harvesting is associated with low labour productivity because it is sporadic, fatiguing, with high to moderate physical effort and high repeatability by the operator, requiring about 700-900 h/year/ha [10], generating a low labour force attractiveness. Along with the scarcity of labour, the precarious working conditions and increased labour costs constrain the greenhouse harvesting operation. The importance of this crop and the associated high production costs justify the fact that this is one of the most common crops in the development of robotic harvesting [6]. However, robotising tomato harvesting is not an easy task. The robot must detect and manipulate, in a heterogeneous and unpredictable environment, a fruit that also varies in position, size, shape, colour and even reflectance [4; 11].The colour is used as an indicator of ripeness, and the desired level of ripeness by the producer can vary. As a climacter fruit, tomatoes can be harvested at the physiological maturity stage (green colour), ripening detached from the plant, or at a more advanced stage, showing a reddish colour [12]. Therefore, in addition to detection, it is desirable to enable its classification based on colour, or even extract other relevant features, the so-called phenotyping process [13], in order to achieve a selective and differentiated harvest. Accessibility and visibility of the fruit are two major challenges in the harvesting task [4].