## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Method** & **No. Ripeness Classes** & **Results** & **Authors** \\ \hline Detection & L*a*b* colour space and & 1 class & Inference time 10.14 s & [PERSON] et al. (2011) \\  & K-means clustering & & & \\ Detection & L*a*b colour space and & 1 class & N/A & [PERSON] et al. (2011) \\  & Bi-level partition fuzzy & & & \\  & logic entropy & & & \\ Detection & RGB, HSI, and YIQ colour & 1 class & Accuracy 96.36\% & [PERSON] et al. (2018) \\  & spaces and Morphological & & & \\  & characteristics & & & \\ Detection & RGB colour space images & 1 class & Inference time 4 s & [PERSON] et al. (2018) \\  & into a HIS colour model & & Accuracy 83.90\% & \\ Detection & RGB colour space into an & 1 class & N/A & [PERSON] (2018) \\  & HIS colour space, threshhold & & & \\  & method and Canny operator & & & \\ Detection & R component of the RGB & 1 class & Accuracy & \\  & images and Sobel operator & & 87.50\% (Clustered) & [PERSON] et al. (2018) \\  & HSV colour space and Watershed & 1 class & Accuracy 81.60\% & [PERSON] et al. (2015) \\ Detection & L*a*b colour space and & 3 classes & Accuracy 93\% & [PERSON] et al. (2018) \\  & Threshold algorithm & & & \\ Classification & Aggregated percent surface area & 6 classes & Accuracy 77\% & [PERSON] et al. (2018) \\ Classification & HSV colour histogram matching & 5 classes & Accuracy 97.20\% & [PERSON] et al. (2018) \\ Classification & K-Nearest Neighbour based on & 5 classes & Accuracy 100\% & [PERSON] et al. (2019) \\ Classification & Fuzzy Rule-Based classification & 6 classes & Accuracy 94.29\% & [PERSON] and [PERSON] (2018) \\  & based on RGB colour space & & & \\ Classification & YCbCr colour histogram & 6 classes & Accuracy 98\% & [PERSON] et al. (2018) \\ Classification & Multiplication of V and Cb colour & 6 classes & Mean Square & [PERSON] et al. (2018) \\  & channel using Otsu thresholding & & Error 3.14 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of different papers regarding tomato detection and classification through colour-based models. (N/A = Not Available). DL architecture used for object detection and classification are the Convolutional Neural Networks (CNN) [47] that makes use of convolution operations in at least one of its layers. CNN are faster at learning and interpreting complex, large-scale problems due to the sharing of weights and the use of more sophisticated models that allow massive parallelisation [48]. One of the most popular approaches are the one-stage detection frameworks, such as the Single Shot Multibox Detector (SSD) [25] or You Only Look Once (YOLO) [26]. These frameworks are composed of a backbone, which is a CNN that consider a dense sampling of possible locations of the objects to detect, and additional convolution layers, often referred to as the head, which can detect and classify the objects in a single step [59]. This process is less time consuming and can therefore be used in real-time applications. The most elementary feature extraction methods, which rely on the \"manual\" selection of certain features, can see their effectiveness dissipate when faced with the agricultural environment's problems (i.e., variations in illumination, occlusions, overlaps, etc.). The great advantage of DL models is that they do not require \"manual\" feature extraction. However, these can be used as a processing input, automatically selecting and classifying relevant features [60]. There are already some studies that compare and evaluate different SSD and YOLO architectures, with different CNNs, especially when it comes to tomato detection [28; 49; 50; 55; 57]. However, the only and most relevant paper found on tomato classification based on one-stage detectors was the one by [PERSON] et al. [58], who compared a CNN with a YOLO model to classify tomatoes into 3 classes (unriped, riped and damaged). It is worth noting that some papers report the modification and improvement of several YOLO models. [PERSON] et al. [51] and [PERSON] et al.