## 2 State-of-the-art
### Literature Review
For studying the effect of the base network, one of the experiments was tested on different networks, such as VGG16, MobileNet and Inception V2. The results indicated that the Inception V2 network obtained the best performance with an accuracy of 98.85%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Tomato Ripenes** & **Accuracy** & **Inference Time** & **Aethiner/Year** \\ \hline L*a*b’ colour space and K-means clustering & Ripe & N/A & 10.10s & [PERSON] et al. [16] 2009 \\ \hline L*a*b colour space and k-level partition fuzzy logic entropy & Ripe & N/A & N/A & [PERSON] et al. [17] 2012 \\ \hline L*a*b colour space and Threshold algorithm & Green, intermediate and ripe & 93\% & N/A & [PERSON] et al. [25] 2016 \\ \hline RGB, HSI and YQ colour spaces and morphological characteristics & Ripe & 96.3\% & N/A & [PERSON] et al. [18] 2011 \\ \hline RGB colour space images into an HSI colour model & Ripe & 83.9\% & 4s & [PERSON] et al. [32] 2015 \\ \hline RGB colour space into an HSI colour space, threshold method and Canny operator & Ripe & N/A & N/A & [PERSON] [19] 2015 \\ \hline R component of the RGB images and Sobel operator & Ripe & Clustered features: 87.5\% & N/A & [PERSON] et al. [20] 2020 \\ \hline HSV colour space and watershed segmentation method & Ripe & 81.6\% & N/A & [PERSON] et al. [21] 2018 \\ \hline Mathematical morphology and Fuzzy C-Means-based method & Ripe & N/A & [PERSON] et al. [22] 2012 \\ \hline Mathematical morphology, difference and iterative erosion course & \multirow{2}{*}{Ripe} & \multirow{2}{*}{70 cm-58.4\%} & \multirow{2}{*}{N/A} & \multirow{2}{*}{[PERSON] et al. [23] 2013} \\ \cline{1-1} \cline{5-5} Pixel-based segmentation, blob-based segmentation and X-means clustering & Green, intermediate and ripe & 88\% & N/A & [PERSON] et al. [24] 2014 \\ \hline Haar-like features of grey-scale image and Addhost classifier & Ripe & 96\% & N/A & [PERSON] et al. [9] 2016 \\ \hline Histograms of oriented gradients and SVM & Ripe & 94.4\% & N/A & [PERSON] et al. [21] 2019 \\ \hline Analysis and selection of multiple features, KPM and k-layer classification strategy & Ripe & 94.0\% & N/A & [PERSON] et al. [21] 2019 \\ \hline Ous segmentation algorithm & Ripe & 99.3\% & N/A & [PERSON] et al. [21] 2017 \\ Improved YOLO-3-tiny method & Ripe & \(\text{F}_{1}=\text{91.92\%}\) & N/A & [PERSON] et al. [28] 2020 \\ \hline YOLO-3 detection model to create the proposed YOLO-tomato model & Green, intermediate and Ripe & 94.8\% & N/A & [PERSON] et al. [25] 2020 \\ \hline Feature pyramid network & Green, intermediate and Ripe & 99.3\% & N/A & [PERSON] et al. [20] 2020 \\ \hline Faster R-CNN structure with the deep CNN ResNet-101 & Green & 87.8\% & N/A & [PERSON] et al. [31] 2020 \\ \hline \multirow{2}{*}{Comparison: R-CNN vs. SSD} & Green, intermediate and Ripe & R-CNN-19.48\% & N/A & [PERSON] et al. [32] 2020 \\ \cline{1-1} \cline{5-5} SSD-based algorithm used to train and develop network models such as VCG16, & Green, intermediate and Ripe & Best performance is Importing & N/A & [PERSON] et al. [33] 2020 \\ \cline{1-1} \cline{5-5} MobileNet, Inception V2 & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \cline{1-1} \cline{5-5} \end{tabular}
\end{table} 
Table 1: Algorithms, methods and techniques proposed by different authors regarding tomato detection at different ripeness levels (N/A—Not Available).